WO2018069877A1

WO2018069877A1 - Metabolic imaging of produce

Info

Publication number: WO2018069877A1
Application number: PCT/IB2017/056348
Authority: WO
Inventors: Anjan Kr. Dasgupta; Sufi Oasim Raja; Mohammad AZHARUDDIN
Original assignee: University Of Calcutta
Priority date: 2016-10-14
Filing date: 2017-10-13
Publication date: 2018-04-19

Abstract

A system includes memory configured to store a database comprising produce temperature information corresponding to a produce quality, for example for the same or similar produce, and which may relate to produce quality over time. The system also includes a thermal imaging device configured to capture an image of produce and a processor operatively coupled to the memory and the thermal imaging device. The processor is configured to receive the image of the produce, determine a temperature of the produce based on the received image, and determine a quality of the produce based on a comparison of the temperature of the produce to the produce temperature information stored in the database.

Description

METABOLIC IMAGING OF PRODUCE

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to Indian Provisional Application No. 201631035221, filed on October 14, 2016, which is incorporated herein by reference in its entirety for any and all purposes.

TECHNICAL FIELD

[0001] The present disclosure relates, in general, to metabolic imaging of produce. More particularly, the present disclosure relates to thermal imaging of produce for determining quality of the produce.

BACKGROUND

[0002] The following description is provided to assist the understanding of the reader. None of the information provided or references cited is admitted to be prior art. Determining whether a piece of fruit or other produce is ripe, unripe, rotten, or infected is often a guessing game for consumers and merchants. An improved system for determining the quality of fruit may help merchants (e.g., store or fruit stand owners) and consumers.

SUMMARY

[0003] An illustrative system includes memory configured to store a database comprising produce temperature information corresponding to a quality of a reference produce over time. The system also includes a thermal imaging device configured to capture an image of produce and a processor operatively coupled to the memory and the thermal imaging device. The processor is configured to receive the image of the produce, determine a temperature of the produce based on the received image, and determine a quality of the produce based on a comparison of the temperature of the produce to the produce temperature information stored in the database.

[0004] An illustrative method includes receiving an image of produce from a thermal imaging device and determining a temperature of the produce based on the received image. The method also includes comparing the temperature of the produce to produce temperature information stored in a database. The produce temperature information corresponds to a quality of a reference produce, for example over time. The method further includes determining the quality of the produce based on the comparison of the temperature of the produce to the produce temperature information.

[0005] An illustrative non-transitory computer-readable medium includes computer-readable instructions that, upon execution by a processor, cause a device to perform operations. The device receives a database comprising produce temperature information corresponding to a quality of reference produce over time and receiving an image of the produce from a thermal imaging device. The device also determines a temperature of the produce based on the received image. The device further determines the quality of the produce based on the temperature of the produce and the database.

[0006] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the following drawings and the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Fig. 1 is a block diagram of a metabolic imaging system in accordance with an illustrative embodiment.

[0008] Fig. 2 is a flow chart of a method for monitoring produce in

accordance with an illustrative embodiment.

[0009] Fig. 3 is a flow chart of a method for determining the quality of produce in accordance with an illustrative embodiment.

[0010] Fig. 4 is a flow chart of a method for monitoring produce in

accordance with an illustrative embodiment.

[0011] Fig. 5 is a histogram of temperatures observed in the thermal images of a banana in accordance with an illustrative embodiment.

[0012] Fig. 6 is a histogram of temperatures observed in the thermal images of another banana in accordance with an illustrative embodiment. [0013] Fig. 7 is a histogram of temperatures observed in a thermal image of two bananas in accordance with an illustrative embodiment.

[0014] Fig. 8 is a histogram of temperatures observed in a thermal image of two apples in accordance with an illustrative embodiment.

[0015] Fig. 9 is a histogram of temperatures observed in a thermal image of two grapes in accordance with an illustrative embodiment.

[0016] Figs. 10A and 10B are graphs of temperatures observed of produce over time in accordance with illustrative embodiments.

[0017] Fig. 11 is a block diagram of a computing device in accordance with an illustrative embodiment.

[0018] The foregoing and other features of the present disclosure will become apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. Understanding that these drawings depict only several embodiments in accordance with the disclosure and are, therefore, not to be considered limiting of its scope, the disclosure will be described with additional specificity and detail through use of the accompanying drawings.

DETAILED DESCRIPTION

[0019] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, and designed in a wide variety of different configurations, all of which are explicitly contemplated and make part of this disclosure. [0020] The marketplace for goods and services is advancing worldwide to become more advanced, and many consumers are becoming more discerning. Many manufacturers and suppliers are advancing to provide customers and consumers with more consistent and higher quality goods. For non-perishable goods, many industries and manufacturers use various techniques in manufacturing to produce goods with consistent quality. For perishable goods, however, in many instances, the quality of the goods is determined, in part, by natural processes that are difficult to control. For example, a bushel of apples from the same producer (and, in some cases, from the same tree) may have some apples that are unripe, some that are ripe, some that are over-ripe, and some that are rotten or infected. However, from a visual inspection, it may be difficult for a customer to determine which apples are in which category.

[0021] In many instances, the primary cause of deterioration of fruits and vegetables is physiochemical deterioration, microbial infection, or fungal infection. Examples of physiochemical processes include oxidation, bleaching, scalding, artificial ripening, excessive softening, and water or moisture loss (e.g., caused by cold storage). Examples of pathogenic infection includes fungal infections such as Alternaria, Botrytis, Diplodia, Monilinia, Phomopsis, Penicillium, Rhizopus,

Ceratocystis, and Fusarium. Examples of bacterial infection include Erwinia, and Pseudomonas. A combination of less-than-ideal conditions can compound the problem. For example, physiochemically damaged fruit is more susceptible to infection. In many instances, physiochemical damage occurs first, and damage caused by infection follows. The deterioration of produce is often accompanied by changes in temperature of the produce. In various embodiments described herein, the temperature of the produce is monitored to determine if the produce has deteriorated.

[0022] In general, when a piece of produce is harvested, physiochemical processes will begin to degrade the fruit, causing ripeness. Thus, the temperature of the produce will increase with respect to ambient temperatures while the

physiochemical processes continue. Once ripe, the physiochemical processes stop (or slow) and less heat is generated within the produce. Thus, the average temperature of the produce drops to be closer to the ambient temperature. If there is an infection within the produce, the infectious organisms begin to degrade the fruit and create heat. Accordingly, the average temperature of the produce rises with respect to ambient temperatures. Thus, by monitoring the temperature of the produce over time, the stage of ripeness and/or infection can be determined based on the temperature of the produce. [0023] In some embodiments, such methods can be used for any suitable plant material. For example, the temperature of leaves of a plant can be monitored over time to determine if the leaves are infected, rotten, etc. In an illustrative embodiment, root nodule bacteria can be determined to be actively infecting a plant by monitoring the temperature of the plant. Any suitable plant material that has the metabolism of quickly growing microbes in a supportive niche can be monitored for an increase of temperature which is indicative of the metabolism. Thus, although various embodiments described herein relate to produce, such embodiments can be used for any suitable plant material.

[0024] In an illustrative embodiment, grading techniques are used to identify damage to produce. The damage can be caused by physiochemical processes or infection. In an illustrative embodiment, the quality of produce can be monitored from harvest of the produce to the time the produce is sold to a consumer (or by the consumer after purchase). In such an embodiment, a handler of the produce (e.g., farmer, transporter, shipper, merchant, etc.) can monitor the temperature of the produce and save the temperature information. The temperature information can be used to guarantee the safety and quality of the produce. In some embodiments, exporters/importers can use the information to help ensure that produce entering a country is not contaminated or infected. Similarly, consumers can use the information to determine the quality of individual pieces of produce to select the most appropriate pieces. [0025] In an illustrative embodiment, metabolic imaging of produce can use thermal imaging techniques to track the metabolism of produce. In general, by monitoring the temperature of produce, at least two features can be monitored. An increase of metabolism can be indicated by an increase in temperature and a decrease of metabolism can be indicated by a decrease in temperature. The relative

temperature of produce can be monitored while taking into consideration the ambient temperature that the produce is stored at. [0026] In an illustrative embodiment, metabolic imaging of produce can be used to detect physical damage of the produce. For example, if a piece of produce is dropped, crushed, punctured, etc., the produce may be physically damaged. The physical damage may include cell walls of cells being ruptured, thereby providing easier access to the nutrients stored within cell walls to bacteria and fungus. The bacteria and/or fungus may more rapidly multiply or spread because of the relatively easy access to the nutrients released by the damaged cells. Thus, an area of the fruit that is damaged may appear warmer than other areas of the fruit (e.g., caused by increased bacteria or fungus metabolic activity). In such a manner, physical damage to produce can be detected. In some instances, damaged fruit becomes infected faster than undamaged fruit. Thus, physical damage to the produce may be detected based on an early infection of the piece of produce as a whole.

[0027] Fig. 1 is a block diagram of a metabolic imaging system in accordance with an illustrative embodiment. An illustrative system 100 includes a temperature sensor 110, a processor 120, and a database 130. In alternative embodiments, additional, fewer, and/or different elements may be used.

[0028] The temperature sensor 110 can be a device or sensor that detects a temperature of one or more pieces of produce. In an illustrative embodiment, the temperature sensor 110 is a thermal imaging camera. In such an embodiment, the temperature sensor 110 can be configured to capture a thermal image of a piece of produce. The thermal image can include several thermal samples of the piece of produce. For example, the thermal image can be a thermal map of the piece of produce.

[0029] Although the use of a thermal imaging camera is discussed for determining the temperature of produce, any other suitable temperature measuring device can be used. An illustrative thermal imaging camera detects infrared wavelengths and can detect the average internal temperature of the produce. That is, an illustrative thermal imaging camera does not detect only the skin surface temperature but detects the sub-surface temperature. For example, a model SC305 thermal camera manufactured by FLIR can be used. In another example, a thermal camera for a smartphone can be used, such as the Therm- App ® device produced by Opgal Optronic Industries, Inc. In alternative embodiments, other types of thermal imaging devices can be used, such as near-infrared imaging devices. Other types of temperature detection can include a liquid crystal thermometer strip that can be attached to fruit, a temperature probe that can be inserted into fruit, etc.

[0030] In the embodiment illustrated in Fig. 1, the temperature sensor 110 is in communication with the processor 120. The communication between the temperature sensor 110 and the processor 120 can include wired or wireless communication. In an illustrative embodiment, the temperature sensor 110 and the processor 120 are included in a single device. In an alternative embodiment, the temperature sensor 110 and the processor 120 are separate devices. In such an embodiment, the temperature sensor 110 and the processor 120 may be coupled together. For example, the processor 120 may be included in a handheld device such as a smartphone, and the temperature sensor 120 can be a thermal camera that mounts to the handheld device. In alternative embodiments, the processor 120 and the temperature sensor 110 can be remote from each other. For example, the processor 120 and the temperature sensor 110 may communicate with one another via one or more communication networks.

[0031] In some embodiments, the processor 120 receives a thermal image of a piece of produce from the temperature sensor 110. In such an embodiment, the processor 120 can analyze the thermal image to determine a temperature of the piece of produce. For example, the processor 120 can determine an outline of the piece of produce in the image. In such an example, edge detection can be used to determine the outline of the piece of produce. The processor 120 can determine an average temperature of the piece of produce by analyzing the thermal samples within the outline of the piece of produce in the image. For example, the processor 120 can average the temperature samples within the outline. In some embodiments, other mathematical functions can be used to determine a temperature of the fruit, such as a median or mode of the temperature samples. In alternative embodiments, the processor 120 can average a subset of the thermal samples within the outline of the piece of produce. In alternative embodiments, the temperature sensor 110 can determine the temperature of the piece of produce, and the temperature sensor 110 transmits the determined temperature to the processor 120.

[0032] In an illustrative embodiment, the processor 120 is configured to track and/or monitor the temperature of the piece of produce over time. For example, the processor 120 can store the temperature of the piece of produce in a memory associated with the processor 120. The temperature can be stored with an indication of the time or date that the temperature was taken and/or with an indication of which piece of produce the temperature is associated with. For example, the system 100 can be used with multiple pieces of produce, and the processor 120 can track or monitor the temperature of each of the pieces of produce over overlapping times.

[0033] As illustrated in Fig. 1, the processor 120 is in communication with the database 130. In an illustrative embodiment, the database 130 includes one or more temperature trends that are each associated with one or more fruit qualities. The physiochemical processes that ripen or degrade produce over time affect the temperature of the produce. Thus, the temperature trend of a piece of produce can be matched with a known temperature trend for the type of produce with a known quality. For example, the system 100 can be used to determine the quality of an apple. The processor 120 can receive multiple indications from the temperature sensor 110 of the temperature of the apple over time. The database 130 can store multiple temperature trends for apples (e.g., of the same type of apples as the apple being tested). Each of the temperature trends can be associated in the database 130 with a quality of produce. For instance, one or more temperature trends may be associated with unripe apples, one or more temperature trends may be associated with ripe apples, one or more temperature trends may be associated with rotten apples, one or more temperature trends may be associated with infected apples, etc.

[0034] In an illustrative embodiment, the processor 120 determines the quality of the produce. For example, the processor 120 receives from the temperature sensor 110 indications of the temperature of the produce over time. The processor 120 can determine a temperature trend of the produce based on the indications received from the temperature sensor 110. The processor can compare the temperature trend of the produce with temperature trends stored in the database 130. For example, the processor 120 can determine which trend stored in the database 130 is a closest match to the temperature trend of the produce. The processor 120 can determine that the produce has a quality associated with the closest match of the temperature trend stored in the database 130. For example, the processor 120 can determine that the temperature trend of the produce matches closest with a particular trend stored in the database 130 that is associated with rotten produce. That is, the particular trend stored in the database 130 can have been previously determined to be indicative of rotten produce, for example produce with at least one rotten portion, or a rotten piece of produce. Based on the match to the trend that is indicative of rotten produce, the processor 120 can determine that the produce monitored by the processor 120 is rotten. In other examples, the produce may be determined to be ripe, unripe, overripe, or other quality. [0035] The database 130 can include any suitable information. An illustrative database 130 includes an average temperature of produce over time for unripe, ripe, over-ripe, infected, rotten, etc. produce. For example, the average temperature of hundreds of produce over time can be used as a reference. [0036] Fig. 2 is a flow chart of a method 200 for monitoring produce in accordance with an illustrative embodiment. In alternative embodiments, additional, fewer, and/or different operations may be performed. Also, the use of a flow chart and arrows is illustrative only and not meant to be limiting with respect to the order or flow of operations.

[0037] In an operation 205, the type of produce to be monitored is determined. The type of produce can indicate whether the produce is a fruit, a vegetable, etc. In an illustrative embodiment, the type of produce includes a genus and/or species of the produce. In some embodiments, the type of produce includes a description of the type of produce (e.g., apple, grape, pumpkin, etc.). In an illustrative embodiment, the operation 205 includes receiving a type of produce. For example, the type of produce can be input by a user. In alternative embodiments, any suitable method for automatically determining the type of produce to be monitored is used. For example, a thermal imaging device can capture an image of the produce and determine, based on the size, shape, and/or thermal signature of the produce, which type of produce is being monitored. [0038] In an operation 210, the temperature of the produce is monitored. Any suitable method for monitoring the temperature of the produce can be used. In an illustrative embodiment, the temperature of produce is monitored from the time that the produce is harvested. In alternative embodiments, the temperature of the produce is monitored at any time after harvest. In an illustrative embodiment, the temperature of the produce is monitored using any suitable method. In some embodiments, the temperature of produce is monitored for a period of time before the method 200 proceeds to operation 215. For example, the temperature of the produce can be monitored for a suitable amount of time for the produce to acclimate to the ambient temperature. In another example, the temperature of the produce can be monitored for a suitable amount of time to identify a trend in temperature change. For instance, the temperature of the produce can be monitored for two days.

[0039] In an illustrative embodiment, the environment in which the produce is stored can be controlled. For example, the temperature of the room that the produce is stored in can be controlled such that the temperature does not rise too high. If the environmental temperature is high enough, it can be difficult to differentiate the rise in temperature caused by metabolism of the produce over the environmental temperature. In an illustrative embodiment, the produce is stored in a room in which the temperature does not rise above 37°C. In alternative embodiments, any other suitable temperature threshold can be used. In some embodiments, the produce is stored in a room temperature of between 20°C and 30°C.

[0040] In an illustrative embodiment, one or more thermal imaging devices are used to monitor the temperature of produce. For example, the produce can be stored or transported in bushels, sacks, cartons, racks, etc., and a thermal imaging device captures an image of a group of produce (e.g., a bushel of apples). In another example, a thermal imaging device is placed among the produce (e.g., in the middle of a bushel of apples) and captures an image of the produce from within the group of produce. In some embodiments, the average temperature of multiple pieces of produce are monitored. In alternative embodiments, the average temperature of one or more individual pieces of produce are monitored. In an illustrative embodiment, one or more pieces of produce are monitored. For example, the one or more pieces of produce can be chosen by random, can be chosen pseudo-randomly, etc.

[0041] The temperature of the produce can be sampled at any suitable rate. For example, the temperature of the produce is monitored continuously (e.g., with a video thermal imaging device). In other examples, the temperature of the produce is monitored once per minute, once per hour, once per day, etc. In an illustrative embodiment, video can be used to monitor the temperature of the produce.

[0042] In an illustrative embodiment, a thermal imaging device, such as the temperature sensor 110, is communicatively coupled to a computing device, such as the processor 120. For example, the thermal imaging device is connected to a server. Any suitable communication method can be used, such as wired or wireless communication. For example, the thermal imaging device can communicate with a computing device via Wi-Fi, a cellular network, etc. In illustrative embodiments, the thermal imaging device transmits thermal images (or average temperatures) to the computing device. The computing device can store the received thermal images (or average temperatures).

[0043] In an operation 215, the temperature of the produce is compared to a database, such as the database 130. The temperature of the produce can be an average temperature of the produce. In an illustrative embodiment, the type of produce and the temperature of the produce are used to look up in the database the quality of the fruit. For example, the database can contain one or more graphs (e.g., graphs similar to the graphs of Figs. 10A and 10B, which is discussed in greater detail below) for each type of produce. The difference between the average temperature of the produce and the ambient temperature can be compared to the graphs stored in the database. In an illustrative embodiment, a trend of the temperature of the produce is compared to the graphs stored in the database. For example, the temperature of the produce over time can be graphed, and the graph can be compared to the graphs stored in the database.

[0044] Based on the comparison in operation 215, in an operation 220, the quality of the produce can be determined. For example, in the operation 215 it can be determined that a trend of the produce being monitored matches closest to a graph stored in the database. The graph can be associated in the database with a quality of produce. In such an example, the operation 220 includes determining that the produce has the quality associated with the graph that most closely matches the trend of the temperature of the produce. The monitoring of temperature can be continued in operation 210.

[0045] In some instances, an end user can use the temperature of produce to determine the quality of the produce. For example, a grocery store shopper can determine the average temperature of a piece of produce and determine the quality of the produce based on the average temperature. Fig. 3 is a flow chart of a method 300 for determining the quality of produce in accordance with an illustrative embodiment. In alternative embodiments, additional, fewer, and/or different operations may be performed. Also, the use of a flow chart and arrows is illustrative only and not meant to be limiting with respect to the order or flow of operations.

[0046] In an operation 305, an image of produce is received. In an illustrative embodiment, the image of the produce is a thermal image of a single piece of produce (e.g., one apple, one grape, etc.). In an illustrative embodiment, the image of the produce is received by a mobile device such as a smartphone. The smartphone can receive the image from an attached (or integrated) camera, such as a thermal imaging device. For example, a piece of fruit is placed on a stand and a thermal imaging camera of a smartphone is used to capture an image of the produce. In such an example, the primary source of heat in the captured thermal image can be from the piece of fruit (e.g., not a human hand, arm, other produce, etc.).

[0047] In an illustrative embodiment, an image of the produce is captured while the produce is not in a lighted environment. For example, the produce can be placed in a box or behind a curtain. Some produce, such as apples, can have relatively reflective skin or outer surfaces. Ambient light (e.g., sunlight, artificial overhead lighting, spot lights, etc.) can reflect off of the surface of the produce and into the lens of a thermal imaging device, thereby causing noise or interference with the image of the produce. For example, a thermal image of a piece of produce in sunlight may indicate a higher temperature of the piece of produce than a thermal image of the piece of produce in a dark place.

[0048] In an operation 310, an average temperature of the produce is determined. In an illustrative embodiment, the average temperature is determined based on the received thermal image. In alternative embodiments, any suitable method of determining the temperature of the produce is used. In an illustrative embodiment, the thermal image includes multiple points or pixels indicating a sensed temperature. The values of the pixels corresponding to the produce (e.g., not pixels corresponding to a background) can be averaged together to determine an average temperature of the produce.

[0049] In an operation 315, the type of produce is determined. In an illustrative embodiment, the type of produce is determined based on the received image. In such embodiments, the size, shape, and/or temperature of the produce can be used to determine the type of produce. For example, a thermal image of a banana can be used to determine that the type of produce is a banana based on the shape of the banana. In alternative embodiments, the type of produce is received from a user interface. For example, a user can select from a menu a type of produce that is being analyzed.

[0050] In an operation 320, the average temperature of the produce is compared to a database. For example, the average temperature and the type of produce can be compared to models or examples of produce with known qualities (e.g., ripe, unripe, rotten, etc.). In an illustrative embodiment, the difference between the average temperature of the fruit and an ambient temperature is compared to a database. In some embodiments, a look-up table is used. In such an embodiment, the ambient temperature can be determined using any suitable method. For example, the area surrounding the produce in the received thermal image can be used to determine the ambient temperature. In another example, a thermometer of a device (e.g., a smartphone) is used to determine the ambient temperature. In an illustrative embodiment, the ambient temperature can be estimated based on a location of the device (e.g., smartphone). For example, location services of a smartphone can be used to determine that the smartphone (and the produce) are within a grocery store. It can be estimated that the grocery store ambient temperature is room temperature (e.g., about 21°C). In another example, location services of a smartphone can be used to determine that the smartphone is outside (e.g., at a fruit stand). The outside temperature can be determined using any suitable method, such as accessing a weather database or service.

[0051] In an operation 325, the quality of the produce is determined. For example, the average temperature of the produce is compared to the database to determine which model and/or example within the database the produce matches most closely. For instance, the average temperature of the produce can be compared to a database that includes trends of fruit with a known quality. Based on the temperature of the fruit, it can be determined which state the fruit is in (e.g., ripe, over-ripe, infected, etc.).

[0052] In an illustrative embodiment, additional information is received regarding the fruit. For example, the number of days since harvest, the number of days in cold storage, the historical average temperature of the produce, etc. can be received. In an illustrative embodiment, a device related to the produce can store the additional information and transmit the information to a user device (e.g., a smartphone). For example, a storage device can be located next to (or among) fruit at a grocery store. The storage device can wirelessly transmit to as user's smartphone the additional information (e.g., by responding to a request sent by the user's smartphone). The additional information can be transmitted in any suitable manner, such as via Wi-Fi, Near Field Communication (NFC), Radio Frequency Identification (RFID), etc. [0053] In an illustrative embodiment, a user device captures a thermal image of a piece of produce and transmits the thermal image (or the average temperature of the produce) to a remote server. The remote server can compare the average temperature of the produce (e.g., as determined based on the thermal image) to the database and transmit to the user device the determined quality of the produce. In alternative embodiments, the various operations of the method 300 (or method 200) can be performed by any suitable device.

[0054] In an illustrative embodiment, once the quality of the produce is determined, the quality of the produce is displayed. For example, the quality of the produce can be sent to a touch screen of a smartphone. In an illustrative embodiment, the user is informed whether the produce is "good" or "bad." In an illustrative embodiment, the user is informed which stage the produce is in (e.g., unripe, ripe, over-ripe, rotting, infected, etc.). In yet another embodiment, the user is given relevant information to determine the quality of the fruit. For example, the average temperature of the fruit (or the average difference between the fruit and the ambient temperature) is displayed to the user as well as one or more graphs or charts. For example, the average temperature of the fruit can be displayed to the user along with graphs of other fruit over time with known qualities. In such an embodiment, the user can use the information provided by the user's smartphone and the user's senses (e.g., touch, smell, vision, etc.) to determine the quality of the fruit. For example, the user can determine based on a relatively low average temperature of a banana and the green appearance of the banana that the banana is un-ripe (e.g., as opposed to rotten). [0055] In an alternative embodiment, the trend of the temperature of the produce is displayed to the user along with trends of the temperature of other produce with known qualities. For example, the trend of the temperature of the produce is displayed along with a trend for the same type of produce that has become rotten. The user can compare the graphs and, based on a similarity of the graphs, determine that the piece of produce is rotten.

[0056] By tracking metabolism of produce over time, chemical degradation (e.g., ripening) and biological degradation can be discerned from one another. For example, chemical degradation can be determined based on a cooling of the produce and biological degradation (e.g., infection) can be determined based on a warming of the produce after the produce is ripe. [0057] In an illustrative embodiment, one or more methods can be used to determine the quality of fruit using a non-contact diagnostic technique. Fig. 4 is a flow chart of a method 400 for monitoring produce in accordance with an illustrative embodiment. In alternative embodiments, additional, fewer, and/or different operations may be performed. Also, the use of a flow chart and arrows is illustrative only and not meant to be limiting with respect to the order or flow of operations.

[0058] In an operation 405, the temperature of produce is monitored. In an illustrative embodiment, the temperature of produce is monitored from the time that the produce is harvested. In alternative embodiments, the temperature of the produce is monitored at any time after harvest. In an illustrative embodiment, the temperature of the produce is monitored using any suitable method. In some embodiments, the temperature of produce is monitored for a period of time before the method 700 proceeds to operation 405. For example, the temperature of the produce can be monitored for a suitable amount of time for the produce to acclimate to the ambient temperature. In another example, the temperature of the produce can be monitored for a suitable amount of time to identify a trend in temperature change. For instance, the temperature of the produce can be monitored for two days. In an illustrative embodiment, the operation 405 is the same as the operation 210. [0059] In an illustrative embodiment, one or more thermal imaging devices are used to monitor the temperature of produce. For example, the produce can be stored or transported in bushels, sacks, cartons, racks, etc., and a thermal imaging device captures an image of a group of produce (e.g., a bushel of apples). In another example, a thermal imaging device is placed among the produce (e.g., in the middle of a bushel of apples) and captures an image of the produce from within the group of produce. In some embodiments, the average temperature of multiple pieces of produce are monitored. In alternative embodiments, the average temperature of one or more individual pieces of produce are monitored. In an illustrative embodiment, one or more pieces of produce are monitored. For example, the one or more pieces of produce can be chosen by random, can be chosen pseudo-randomly, etc.

[0060] The temperature of the produce can be sampled at any suitable rate. For example, the temperature of the produce is monitored continuously (e.g., with a video thermal imaging device). In other examples, the temperature of the produce is monitored once per minute, once per hour, once per day, etc. In an illustrative embodiment, video can be used to monitor the temperature of the produce. [0061] In an operation 410, whether there is a slowing in the increase of temperature is determined. For example, the increase in temperature can be monitored during the physiochemical processes. The ripening of produce is signified by an increase in temperature and a slowing in the increase in temperature signifies over-ripening of the produce. In an illustrative embodiment, if it is determined that there has been a slowing in the increase of temperature, it is determined that the produce is over-ripe. To determine that there is a slowing in the increase of the temperature, the rate of change of the temperature can be compared to a threshold rate of change. If the rate of change of the temperature is less than the threshold rate of change, then it can be determined that there is a slowing in the increase of the temperature.

[0062] If it is determined that there has not been a slowing of the increase of temperature, the temperature is continued to be monitored in operation 405. If it is determined that there has been a slowing in the increase in temperature, in an operation 415, it is determined whether there has been a subsequent increase of temperature. After the physiochemical process slows indicating that the produce is ripe, a subsequent hastening of the increase in temperature can indicate that the produce is infected. In an illustrative embodiment, the operation 415 includes determining that the rate of change of the temperature of the produce is above a threshold rate of change.

[0063] If it is determined that there has been a subsequent increase of temperature of the produce, then it can be determined in an operation 420 that the produce (may) be infected. In an illustrative embodiment, the operation 420 includes a notification to a user that the produce may be infected. Any suitable notification method can be used, such as a visual alarm (e.g., a blinking light), an audible alarm (e.g., a beep, a chirp, a siren), a textual alarm (e.g., an email, a report), etc. In response to the notification, the produce can be inspected to determine whether the produce is of acceptable quality.

[0064] If it is determined that there has not been a subsequent increase of temperature of the produce, then in an operation 425 it is determined whether there is a relatively quick decrease in temperature. As the physiochemical process slows and there is no infection, then a relatively quick decrease in temperature can indicate that the produce is rotten. In an illustrative embodiment, the rate of change of the temperature of the produce is compared to a threshold rate of change. If the rate of change of the temperature of the produce is above the threshold rate of change (e.g., the produce is cooling faster than the threshold rate of change), then it can be determined that there is a quick decrease of temperature in the operation 425.

[0065] If it is determined that there has been a quick decrease of temperature of the produce, then it can be determined in an operation 430 that the produce (may) be rotten. In an illustrative embodiment, the operation 430 includes a notification to a user that the produce may be rotten. Any suitable notification method can be used, such as a visual alarm (e.g., a blinking light), an audible alarm (e.g., a beep, a chirp, a siren), a textual alarm (e.g., an email, a report), etc. In response to the notification, the produce can be inspected to determine whether the produce is acceptable. If it is determined that there has not been a quick decrease in temperature of the produce, then the temperature can continue to be monitored in operation 405. In an illustrative embodiment, if it is determined that there has not been a large decrease in

temperature, it can be determined that the produce may be over-ripe. In alternative embodiments, fuzzy logic can be used to determine the quality of produce.

[0066] Fig. 5 is a histogram of temperatures observed in the thermal images of a banana in accordance with an illustrative embodiment. Fig. 5 illustrates the change in temperature over time of a banana that become infected. Thermal images contain multiple temperature samples that are arranged in a two-dimensional format. That is, the pixels of the thermal images are indicative of a sensed temperature. The histogram of Fig. 5 is a plot of the pixels corresponding to the temperature of a banana over four days. The x-axis is the temperature value of the pixel in the thermal images in degrees Celsius, and the y-axis is the number of pixels corresponding to the temperature in the x-axis. Thermal images of a banana were taken on consecutive days: "Day 1," "Day 2," "Day 3," and "Day 4." The camera used to capture the thermal images has a spectral range of between 7.5 micrometers (μιη) to 13 μιη. The histogram of Fig. 5 shows the temperature samples from Day 1 510, Day 2 520, Day 3 530, and Day 4 540.

[0067] Fig. 5 shows the rise in temperature over time of a banana. The average temperature of the banana was about 26 degrees Celsius (°C) on Day 1, about 30.3°C on Day 2, about 30.6°C on Day 3, and about 33.3°C on Day 4. The banana was stored at a constant ambient temperature of 25°C over the course of the four days. Over the course of the four days, the temperature of the banana increased.

Metabolism within the banana caused the rise in temperature between Day 3 and Day 4. For example, infectious organisms inherent within the fruit became active and began to degrade the banana. The metabolism of the infectious organisms causes heat, which increases the temperature of the banana.

[0068] On Day 1, the banana was of average quality. That is, the banana was suitable for consumption. As shown in Fig. 5, there is a relatively large increase in temperature between Day 1 and Day 2. It can be inferred that because the fruit is relatively fresh, the increase in temperature was caused by physiochemical processes (e.g., ripening). Thus, on Day 2, the banana was ripe. Between Day 2 and Day 3, there is a relatively small increase in temperature. Thus, it can be inferred that the physiochemical processes were slowing down because the fruit was nearing peak ripeness (e.g., over-ripeness). Thus, on Day 3, the banana was over-ripe for the average consumer. After the slowing of the physiochemical processes, there was a relatively large increase in temperature of the banana between Day 3 and Day 4.

Because the physiochemical processes have ceased (or significantly slowed), it can be inferred that the increase in temperature was caused by an infection within the banana. Thus, on Day 4, the banana was infected.

[0069] Fig. 6 is a histogram of temperatures observed in the thermal images of another banana in accordance with an illustrative embodiment. Fig. 6 illustrates the change in temperature over time of a banana that become infected. The histogram of Fig. 6 is a plot of the pixels corresponding to the temperature of the banana over four days. The x-axis is the temperature value of the pixel in the thermal images in degrees Celsius, and the y-axis is the number of pixels corresponding to the temperature in the x-axis. Thermal images of the banana were taken on consecutive days: "Day 1," "Day 2," "Day 3," and "Day 4." The camera used to capture the thermal images has a spectral range of between 7.5 micrometers (μιη) to 13 μιη. The histogram of Fig. 6 shows the temperature samples from Day 1 610, Day 2 620, Day 3 630, and Day 4 640.

[0070] Fig. 6 shows the rise in temperature over time. The average

temperature of the banana was about 26°C on Day 1, about 29.6°C on Day 2, about 30.5°C on Day 3, and about 32.3°C on Day 4. The banana was stored at a constant ambient temperature of 25°C over the course of the four days. Over the course of the four days, the temperature of the banana increased. Metabolism within the banana caused the rise in temperature. For example, infectious organisms inherent within the fruit became active and began to degrade the banana. The metabolism of the infections organisms causes heat, which increases the temperature of the banana. [0071] On Day 1, the banana was ripe. On Day 2, the banana was over-ripe.

On Day 3, the banana was infected. On Day 4, the infection of the banana spread.

[0072] Figs. 5 and 6 show the rise in temperature over time of produce with an infection. However, not all produce is infected with bacteria or fungi that will degrade the produce after ripening. Over-ripening of the produce will cause the produce to rot. Because infection does not occur in such instances and the ripening process slows, there is a temperature decrease in the fruit. Regardless of whether a piece of produce is infected, if left long enough, the produce becomes inedible (or less than ideal). Fig. 7 is a histogram of temperatures observed in a thermal image of two bananas in accordance with an illustrative embodiment. The camera used to capture the thermal image has a spectral range of between 7.5 μιη to 13 μιη. [0073] A thermal image was captured of a banana 710 and a banana 720. The banana 710 was relatively fresh, and the banana 720 was relatively old at the time the thermal image was captured. The histogram of Fig. 7 is a plot of the pixels corresponding to the temperature of the two bananas. The x-axis is the temperature value of the pixel in the thermal image in degrees Celsius, and the y-axis is the number of pixels corresponding to the temperature in the x-axis. As shown in Fig. 7, the average temperature of the fresh banana 710 is greater than the average temperature of the old banana 720. Thus, it can be determined that the old banana 720 is rotten. It can be inferred that the old banana 720 has a lower temperature than the fresh banana 710 because the fresh banana 710 is undergoing a ripening process and generating internal heat while the old banana 720 finished the ripening process and is not generating heat. Because the old banana 720 is not generating heat and the temperature of the old banana 720 is moving toward room temperature, it can be inferred that there is no infection (e.g., no source of internal heat. Thus, the banana 720 continued to ripen until the banana 720 rotted. That is, the banana 720 ripened and the physiochemical process slowed. The temperature of the banana 720 dropped, indicating that the physiochemical process ran out of water (e.g., is rotten).

[0074] Although the examples above are with regard to bananas, the present disclosure can be used with other types of produce such as fruits (such as berries), vegetables (such as tubers), and the like. Examples of the described methods can be used to determine the quality of apples, bananas, grapes, pears, pumpkins, strawberries, and the like. In some embodiments, examples of the present disclosure can be used with any other suitable solid or liquid food product. For example, if the food product is stored at ambient temperature, an increase in the food product may indicate metabolism of the food product caused by an infection.

[0075] Fig. 8 is a histogram of temperatures observed in a thermal image of two apples in accordance with an illustrative embodiment. Fig. 8 shows the difference in temperature between a freshly harvested apple 810 and an apple 820 that was harvested and stored. A thermal image was taken of the apple 810 and the apple 820. The histogram of Fig. 8 is a plot of the pixels corresponding to the temperature of the two apples. The x-axis is the temperature value of the pixel in the thermal image in degrees Celsius, and the y-axis is the number of pixels corresponding to the temperature in the x-axis.

[0076] As shown in Fig. 8, the relatively fresh apple 810 had an average temperature of about 30.7°C, and the apple 820 that had been in storage had an average temperature of about 30°C. Because the fresh apple 810 still had a relatively active physiochemical process, the temperature of the fresh apple 810 was greater than the stored apple 820. Thus, it can be determined that the stored apple 820 was not infected (e.g., because the temperature did not rise after the physiochemical process slowed) and had become rotten (e.g., because the physiochemical process degraded the apple over time).

[0077] Fig. 9 is a histogram of temperatures observed in a thermal image of two grapes in accordance with an illustrative embodiment. Fig. 9 shows the difference in temperature between a ripe grape 910 and a rotten grape 920. A thermal image was taken of a ripe grape 910 and a rotten grape 920. The histogram of Fig. 9 is a plot of the pixels corresponding to the temperature of the two grapes. The x-axis is the temperature value of the pixel in the thermal image in degrees Celsius, and the y-axis is the number of pixels corresponding to the temperature in the x-axis. [0078] The data points on the right of the histogram of Fig. 9 correspond to a freshly harvested grape 910, and the data points on the left of the histogram correspond to a rotten grape 920. Similar to the example of the fresh apple 810 and the rotten apple 820 of Fig. 8, the rotten grape 920 had an average temperature of about 29.65°C which was lower than that of the fresh grape 910, which had an average temperature of about 29.8°C. The average temperature difference between the fresh grape 910 and the rotten grape 920 was less than the average temperature difference between the fresh apple 810 and the rotten apple 820 because of the mass of the different fruits. For example, because apples have greater mass than individual grapes, the internal temperature of apples is less affected by the ambient temperature.

[0079] Figs. 10A and 10B are graphs of temperatures observed of produce over time in accordance with illustrative embodiments. The graphs of Figs. 10A and 10B are meant to be illustrative and explanatory only and are not meant to be limiting with respect to proportions, slopes, amounts, etc. The graph of Fig. 10A illustrates the temperature of an infected piece of produce over time in accordance with an illustrative embodiment. The graph of Fig. 10B illustrates the temperature of a piece of produce that rotted over time in accordance with an illustrative embodiment.

[0080] Figs. 10A and 10B are plots of temperature (y-axis) over time (x-axis). Line 1005 corresponds to the ambient temperature. Line 1010 corresponds the temperature of the piece of produce. The line segment 615 corresponds to the piece of produce being unripe. The line segment 1020 corresponds to piece of produce being ripe. The line segment 1025 corresponds to the piece of produce being overripe. The line segment 1030 corresponds to the piece of produce having gone bad. In Fig. 10A, the shape and/or slope of the line segment 1030 is indicative of the piece of produce being infected. In Fig. 10B, the shape and/or slope of the line segment 1030 is indicative of the piece of produce having rotted.

[0081] In both Figs. 10A and 10B, the increase of temperature as indicated in line segment 1015 indicates that physiochemical processes were occurring within the piece of produce to ripen the piece of produce. After enough ripening, the piece of produce was considered to be ripe, corresponding to line segment 1020. The piece of produce continued to ripen, but the ripening slowed, corresponding to a slowing of the increase in temperature, as shown by line segment 1025. In some instances, the time delay between the line segment 1020 and line segment 1030 of Fig. 10A is long enough that there is a drop in temperature instead of the leveling off of temperature indicated in Fig. 10A.

[0082] As illustrated in Fig. 10A, after the physiochemical processes slowed down, as shown by line segment 1025, an increase in temperature, as shown by line segment 1030, indicates that infection had begun. As the infection spread, the temperature of the piece of produce rose. In some instances, the temperature of the piece of produce levels off indicating a constant rate of infection.

[0083] As illustrated in Fig. 10B, after the physiochemical processes slowed down, as shown by line segment 1025, a continued decrease in temperature, as shown by line segment 1030, indicates that the piece of produce rotted. That is, the physiochemical processes continue to degrade the piece of produce, but at a slower rate. As the physiochemical processes continued to slow down, less heat was generated within the piece of produce, and the average temperature of the piece of produce decreased and approached the ambient temperature.

[0084] Information contained in the graphs of Figs. 10A and 10B can be used to determine the quality of a piece of produce. For example, if there is an initial rise in temperature of a piece of produce similar to line segment 1015, it can be determined that the produce is unripe. In some embodiments, properly stored produce can be considered to be fresh produce because when properly stored, the produce undergoes minor physiochemical degradation. If there is a rise in temperature above ambient similar to the line segment 1020, it can be determined that the produce is ripe. If there is a leveling off of the temperature or a decrease in the temperature similar to the line segment 1025, it can be determined that the produce is over-ripe. If there is another increase in temperature (similar to the line segment 1030 of Fig. 10A) or a continued decrease in temperature (similar to the line segment 1030 of Fig. 10B), it can be determined that the produce is spoiled.

[0085] The graphs of Figs. 10A and 10B are used for illustrative purposes. In alternative embodiments, any suitable data can be used. The demarcation lines between unripe and ripe, ripe and over-ripe, etc. can be different based on the produce, the end consumer, the type of produce, etc.

[0086] Fig. 11 is a block diagram of a computing device in accordance with an illustrative embodiment. An illustrative computing device 1100 includes a memory 1105, a processor 1110, a transceiver 1115, a user interface 1120, a power source 1125, and an image capture device 1130. In alternative embodiments, additional, fewer, and/or different elements may be used. The computing device 1100 can be any suitable device described herein. For example, the computing device 1100 can be a desktop computer, a laptop computer, a smartphone, a specialized computing device, etc. The computing device 1100 can be used to implement one or more of the methods described herein.

[0087] In an illustrative embodiment, the memory 1105 is an electronic holding place or storage for information so that the information can be accessed by the processor 1110. The memory 1105 can include, but is not limited to, any type of random access memory (RAM), any type of read only memory (ROM), any type of flash memory, etc. such as magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips, etc.), optical disks (e.g., compact disk (CD), digital versatile disk (DVD), etc.), smart cards, flash memory devices, etc. The computing device 1100 may have one or more computer-readable media that use the same or a different memory media technology. The computing device 1100 may have one or more drives that support the loading of a memory medium such as a CD, a DVD, a flash memory card, etc. [0088] In an illustrative embodiment, the processor 1110 executes

instructions. The instructions may be carried out by a special purpose computer, logic circuits, or hardware circuits. The processor 1110 may be implemented in hardware, firmware, software, or any combination thereof. The term "execution" is, for example, the process of running an application or the carrying out of the operation called for by an instruction. The instructions may be written using one or more programming language, scripting language, assembly language, etc. The processor 1110 executes an instruction, meaning that it performs the operations called for by that instruction. The processor 1110 operably couples with the user interface 1120, the transceiver 1115, the memory 1105, etc. to receive, to send, and to process information and to control the operations of the computing device 1100. The processor 1110 may retrieve a set of instructions from a permanent memory device such as a ROM device and copy the instructions in an executable form to a temporary memory device that is generally some form of RAM. An illustrative computing device 1100 may include a plurality of processors that use the same or a different processing technology. In an illustrative embodiment, the instructions may be stored in memory 1105. [0089] In an illustrative embodiment, the transceiver 1115 is configured to receive and/or transmit information. In some embodiments, the transceiver 1115 communicates information via a wired connection, such as an Ethernet connection, one or more twisted pair wires, coaxial cables, fiber optic cables, etc. In some embodiments, the transceiver 1115 communicates information via a wireless connection using microwaves, infrared waves, radio waves, spread spectrum technologies, satellites, etc. The transceiver 1115 can be configured to communicate with another device using cellular networks, local area networks, wide area networks, the Internet, etc. In some embodiments, one or more of the elements of the computing device 1100 communicate via wired or wireless communications. In some embodiments, the transceiver 1115 provides an interface for presenting information from the computing device 1100 to external systems, users, or memory. For example, the transceiver 1115 may include an interface to a display, a printer, a speaker, etc. In an illustrative embodiment, the transceiver 1115 may also include alarm/indicator lights, a network interface, a disk drive, a computer memory device, etc. In an illustrative embodiment, the transceiver 1115 can receive information from external systems, users, memory, etc.

[0090] In an illustrative embodiment, the user interface 1120 is configured to receive and/or provide information from/to a user. The user interface 1030 can be any suitable user interface. The user interface 1030 can be an interface for receiving user input and/or machine instructions for entry into the computing device 1100. The user interface 1030 may use various input technologies including, but not limited to, a keyboard, a stylus and/or touch screen, a mouse, a track ball, a keypad, a microphone, voice recognition, motion recognition, disk drives, remote controllers, input ports, one or more buttons, dials, joysticks, etc. to allow an external source, such as a user, to enter information into the computing device 1100. The user interface 1030 can be used to navigate menus, adjust options, adjust settings, adjust display, etc. [0091] The user interface 1030 can be configured to provide an interface for presenting information from the computing device 1100 to external systems, users, memory, etc. For example, the user interface 1030 can include an interface for a display, a printer, a speaker, alarm/indicator lights, a network interface, a disk drive, a computer memory device, etc. The user interface 1030 can include a color display, a cathode-ray tube (CRT), a liquid crystal display (LCD), a plasma display, an organic light-emitting diode (OLED) display, etc.

[0092] In an illustrative embodiment, the power source 1125 is configured to provide electrical power to one or more elements of the computing device 1100. In some embodiments, the power source 1125 includes an alternating power source, such as available line voltage (e.g., 120 Volts alternating current at 60 Hertz in the United States). The power source 1125 can include one or more transformers, rectifiers, etc. to convert electrical power into power useable by the one or more elements of the computing device 1100, such as 1.5 Volts, 8 Volts, 12 Volts, 24 Volts, etc. The power source 1125 can include one or more batteries.

[0093] In an illustrative embodiment, the computing device 1100 includes an image capture device 1130. In other embodiments, image capture device 1130 is an independent device and is not integrated into the computing device 1100. The image capture device 1130 can be configured to capture images. In some embodiments, the image capture device 1130 can capture two-dimensional images. In other

embodiments, the image capture device 1130 can capture three-dimensional images. The image capture device 1130 can be a still-image camera, a video camera, etc. In an illustrative embodiment, the image capture device 1130 captures infrared images. For example, the image capture device 1130 can be a model SC305 thermal camera manufactured by FLIR. In another example, the image capture device 1130 is a device attachable to a smartphone, tablet, etc. In yet another example, the image capture device 1130 is a device integrated into a smartphone, tablet, etc.

[0094] In an illustrative embodiment, any of the operations described herein can be implemented at least in part as computer-readable instructions stored on a computer-readable memory. Upon execution of the computer-readable instructions by a processor, the computer-readable instructions can cause a node to perform the operations.

[0095] Examples of the present disclosure may be used to determine the quality of produce, such as fruit, vegetables, herbs, and fungi (such as mushrooms). In some examples, the quality of other items may be determined in an analogous manner, for example for other plant products such as wood and wood-based items, construction materials, animal feed, and the like. [0096] In some examples, produce temperature information may be previously obtained for reference produce. The reference produce may be produce of the same or similar type, and of determined quality. The quality of reference produce used to obtain the produce temperature information may be obtained using one or more of visual appearance, touch, taste, optical analysis, or other sensory or analytical method. In other examples, more generally, item temperature information may be obtained for items of the same or similar type, and of determined characteristic. Characteristics of items may then be obtained using, for example, thermal imaging or other temperature measurements performed on the items, based on a comparison with item temperature information stored in a database. In some examples, chemical processes such as annealing, curing, other chemical reactions, and the like may be analyzed using similar approaches.

[0097] The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same

functionality is effectively "associated" such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as "associated with" each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being "operably connected," or "operably coupled," to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being "operably couplable," to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.

[0098] With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.

[0099] It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., "a" and/or "an" should typically be interpreted to mean "at least one" or "one or more"); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of "two recitations," without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to "at least one of A, B, or C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B." Further, unless otherwise noted, the use of the words "approximate," "about," "around," "substantially," etc., mean plus or minus ten percent.

[0100] The foregoing description of illustrative embodiments has been presented for purposes of illustration and of description. It is not intended to be exhaustive or limiting with respect to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from practice of the disclosed embodiments. It is intended that the scope of the invention be defined by the claims appended hereto and their equivalents.

Claims

CLAIMS:

1. A system comprising:

a memory configured to store a database comprising produce temperature information;

a thermal imaging device configured to capture an image of produce; and a processor operatively coupled to the memory and the thermal imaging device and configured to:

receive the image of the produce;

determine a temperature of the produce based on the received image; and

determine a quality of the produce based on a comparison of the temperature of the produce to the produce temperature information stored in the database.

2. The system of claim 1, wherein, to determine the quality of the produce, the processor is configured to determine whether the produce is unripe, ripe, over-ripe, infected, or rotten.

3. The system of claim 2, wherein the produce temperature information stored in the database comprises a first temperature range corresponding to unripe produce, a second temperature range corresponding to ripe produce, a third temperature range corresponding to infected produce, and a fourth temperature range corresponding to rotten produce.

4. The system of claim 3, wherein:

the first temperature range comprises temperatures lower than temperatures of the second temperature range,

the second temperature range comprises temperatures lower than temperatures in the third temperature range, and

the fourth temperature range comprises temperatures lower than temperatures in the second temperature range.

5. The system of claim 2, wherein the produce temperature information stored in the database comprises a first temperature curve corresponding to unripe produce, a second temperature curve corresponding to ripe produce, a third temperature curve corresponding to infected produce, and a fourth temperature curve corresponding to rotten produce.

6. The system of claim 5, wherein the processor is further configured to: receive a plurality of images of the produce; and

determine a temperature trend of the produce based on the received plurality of images, and

wherein, to determine the quality of the produce, the processor is configured to compare the temperature trend to one or more of the first temperature curve, the second temperature curve, the third temperature curve, and the fourth temperature curve.

7. The system of claim 2, wherein:

to determine that the produce is unripe, the processor is configured to determine that the temperature of the produce is below a first threshold temperature, to determine that the produce is ripe, the processor is configured to determine that the temperature of the produce is above the first threshold temperature and below a second threshold temperature,

to determine that the produce is over-ripe, the processor is configured to determine that the temperature of the produce is above the second threshold temperature and below a third threshold temperature,

to determine that the produce is infected, the processor is configured to determine that the temperature of the produce is above the third threshold

temperature, and

to determine that the produce is rotten, the processor is configured to determine that the temperature of the produce is below the second threshold temperature.

8. The system of claim 7, wherein, to determine that the produce is unripe, the processor is further configured to determine that the temperature of the produce was not previously above the first threshold temperature, and

wherein, to determine that the produce is rotten, the processor is further configured to determine that the temperature of the produce was previously above the first threshold temperature.

9. The system of claim 1, wherein, to determine the quality of the produce, the processor is configured to determine if the produce is spoiled.

10. The system of claim 9, wherein the produce temperature information stored in the database comprises a first temperature range corresponding to not- spoiled produce and a second temperature range corresponding to spoiled produce.

11. The system of claim 10, wherein the first temperature range comprises temperatures lower than temperatures of the second temperature range.

12. The system of claim 9, wherein the produce temperature information stored in the database comprises a first temperature curve corresponding to not spoiled produce, a second temperature curve corresponding to spoiled produce, and a third temperature curve corresponding to spoiled produce, and wherein the third temperature curve comprises temperatures lower than temperatures of the second temperature curve.

13. The system of claim 12, wherein the processor is further configured to: receive a plurality of images of the produce; and

wherein, to determine the quality of the produce, the processor is configured to compare the temperature trend to the first temperature curve, the second temperature curve, and the third temperature curve.

14. The system of claim 9, wherein, to determine that the produce is not spoiled, the processor is configured to determine that the temperature of the produce was not previously above a threshold temperature, and

wherein, to determine that the produce is spoiled, the processor is configured to determine (a) that the temperature of the produce is above the threshold

temperature or (b) that the temperature of the produce is below the threshold temperature and was previously above the threshold temperature.

15. The system of claim 1, wherein, to determine the quality of the produce, the processor is configured to determine if the produce is ripe.

16. The system of claim 15, wherein the produce temperature information stored in the database comprises a first temperature range corresponding to ripe produce.

17. The system of claim 16, wherein, to determine that the produce is ripe, the processor is further configured to determine that the temperature of the produce is within the first temperature range and that the temperature of the produce was not previously above the first temperature range.

18. The system of claim 1, further comprising a user interface, wherein the processor is further configured to cause the user interface to display an indication of the quality of the produce.

19. The system of claim 1, wherein the produce is a single piece of produce.

20. The system of claim 1, wherein the produce comprises multiple pieces of produce.

21. The system of claim 1, wherein the produce temperature information is determined for reference produce of a known quality.

22. The system of claim 1, wherein, to determine the quality of the produce, the processor is further configured to compare the temperature of the produce to an ambient temperature of the produce.

23. A method comprising:

receiving an image of produce from a thermal imaging device;

determining a temperature of the produce based on the received image;

comparing the temperature of the produce to produce temperature information stored in a database, wherein the produce temperature information corresponds to a quality of the produce over time; and

determining the quality of the produce based on the comparison of the temperature of the produce to the produce temperature information.

24. The method of claim 23, wherein said determining the quality of the produce includes determining whether the produce is unripe, ripe, over-ripe, infected, or rotten.

25. The method of claim 24, wherein the produce temperature information stored in the database comprises a first temperature range corresponding to unripe produce, a second temperature range corresponding to ripe produce, a third temperature range corresponding to infected produce, and a fourth temperature range corresponding to rotten produce.

26. The method of claim 25, wherein:

27. The method of claim 24, wherein the produce temperature information stored in the database comprises a first temperature curve corresponding to unripe produce, a second temperature curve corresponding to ripe produce, a third temperature curve corresponding to infected produce, and a fourth temperature curve corresponding to rotten produce.

28. The method of claim 27, further comprising:

receiving a plurality of images of the produce; and

determining a temperature trend of the produce based on the received plurality of images, and

wherein said comparing the temperature of the produce to produce temperature information comprises comparing the temperature trend to the first temperature curve, the second temperature curve, the third temperature curve, and the fourth temperature curve.

29. The method of claim 24, wherein:

determining that the produce is unripe comprises determining that the temperature of the produce is below a first threshold temperature,

determining that the produce is ripe comprises determining that the temperature of the produce is above the first threshold temperature and below a second threshold temperature,

determining that the produce is over-ripe comprises determining that the temperature of the produce is above the second threshold temperature and below a third threshold temperature,

determining that the produce is infected comprises that the temperature of the produce is above the third threshold temperature, and

determining that the produce is rotten comprises that the temperature of the produce is below the second threshold temperature.

30. The method of claim 29, wherein said determining that the produce is unripe further comprises determining that the temperature of the produce was not previously above the first threshold temperature, and

wherein said determining that the produce is rotten further comprises determining that the temperature of the produce was previously above the first threshold temperature.

31. The method of claim 23, wherein said determining the quality of the produce includes determining whether the produce is not spoiled or spoiled.

32. The method of claim 31, wherein the produce temperature information stored in the database comprises a first temperature range corresponding to not- spoiled produce and a second temperature range corresponding to spoiled produce.

33. The method of claim 32, wherein the first temperature range comprises temperatures lower than temperatures of the second temperature range.

34. The method of claim 31, wherein the produce temperature information stored in the database comprises a first temperature curve corresponding to not spoiled produce, a second temperature curve corresponding to spoiled produce, and a third temperature curve corresponding to spoiled produce, and wherein the third temperature curve comprises temperatures lower than temperatures of the second temperature curve.

35. The method of claim 34, further comprising:

receiving a plurality of images of the produce; and

wherein said comparing the temperature of the produce to produce

temperature information comprises comparing the temperature trend to the first temperature curve, the second temperature curve, and the third temperature curve.

36. The method of claim 31, wherein said determining that the produce is not spoiled comprises determining that the temperature of the produce was not previously above a threshold temperature, and

wherein said determining that the produce is spoiled comprises determining

(a) that the temperature of the produce is above the threshold temperature or (b) that the temperature of the produce is below the threshold temperature and was previously above the threshold temperature.

37. The method of claim 23, wherein said determining the quality of the produce comprises determining if the produce is ripe.

38. The method of claim 37, wherein the produce temperature information stored in the database comprises a first temperature range corresponding to ripe produce.

39. The method of claim 38, wherein said determining that the produce is ripe comprises determining that the temperature of the produce is within the first temperature range and that the temperature of the produce was not previously above the first temperature range.

40. The method of claim 23, further comprising displaying on a user interface an indication of the quality of the produce.

41. The method of claim 23, wherein the produce is a single piece of produce.

42. The method of claim 23, wherein the produce comprises multiple pieces of produce.

43. The method of claim 23, wherein the temperature information is obtained from a reference produce of known quality.

44. The method of claim 23, wherein determining the temperature of the produce includes determining a difference between an average temperature of the produce and an ambient temperature.

45. The method of claim 23, further comprising monitoring the

temperature of the produce over time to determine a temperature trend of the produce, and wherein determining a quality of the produce includes comparing the trend of the produce to one or more trends of a reference produce stored in the database.

46. A non-transitory computer-readable medium including computer- readable instructions that, upon execution by a processor, cause a device to:

receive a database comprising produce temperature information corresponding to a quality of a reference produce over time;

receive an image of produce from a thermal imaging device;

determine a temperature of the produce based on the received image; and determine the quality of the produce based on the temperature of the produce and the produce temperature information stored in the database.

47. The non-transitory computer-readable medium of claim 46, wherein to determine the quality of the produce, the computer-readable instructions cause the device to determine whether the produce is unripe, ripe, over-ripe, infected, or rotten.

48. The non-transitory computer-readable medium of claim 46, wherein to determine the quality of the produce, the computer-readable instructions cause the device to determine if the produce is spoiled.

49. The non-transitory computer-readable medium of claim 46, wherein to determine the quality of the produce, the computer-readable instructions cause the device to determine if the produce is ripe.