System, apparatus and method of non-destructively determining petiole nutrient values in near real time using portable spectrophotometer

Abstract

To improve the crop yield and / or quality.SOLUTION: A system, an apparatus and a method of non-destructively determining petiole nutrient values in near real time from plant leaves in a crop field using a portable spectrophotometer are provided. The method may comprise: (a) taking non-destructive spectral measurements of a leaf of a plant in the crop field using the portable spectrophotometer to generate leaf spectral data; (b) storing the leaf spectral data in a memory; (c) computing, by a processor, the petiole nutrient values based on the stored leaf spectral data; and (d) providing a near real time result indicating the petiole nutrient values.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] This document relates to a system, apparatus and method for non-destructively measuring petiole nutritional value in near real time from plant leaves in the field using a portable spectrophotometer. [Background technology]

[0002]

[0002] Plants require many nutrients to grow and survive. For example, plants may require large amounts of macronutrients such as nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S). Plants may also require small amounts of micronutrients such as manganese (Mn), zinc (Zn), iron (Fe), sodium (Na), copper (Cu), aluminum (Al), and boron (B).

[0003]

[0003] In agricultural applications, if crops do not receive the appropriate amount of each necessary nutrient, the crop yield and / or quality of the harvested crop may be affected. For example, if a plant does not receive enough nutrients, the crop yield and / or quality may be reduced. In some cases, excessive supply of nutrients may also reduce the crop yield and / or quality.

[0004]

[0004] Measuring the nutritional status of a plant can enable corrective actions to be taken to improve crop yield and / or quality. For example, fertilizer formulations and amounts can be adjusted for one or more specific nutrients based on the measured nutritional status of the plant. Summary of the Invention

[0005]

[0005] The following Summary is provided to introduce the reader to the more detailed discussion that follows. The Summary is not intended to limit or define any inventions, whether claimed or yet to be claimed. One or more inventions may reside in any combination or subcombination of the elements or process steps disclosed in any part of this document, including the claims and drawings.

[0006] According to some aspects, a method is provided for non-destructively measuring petiole nutritional value from leaves of plants in an agricultural field in near real time using a portable spectrophotometer. The method may include: (a) performing non-destructive spectral measurements of leaves of plants in an agricultural field using the portable spectrophotometer to generate leaf spectral data, (b) storing the leaf spectral data in a memory, (c) calculating, by a processor, petiole nutritional value based on the stored leaf spectral data, and (d) providing a near real time result indicative of the petiole nutritional value.

[0007]

[0007] According to some aspects, an apparatus is provided for non-destructively measuring petiole nutritional value in near real time from leaves of plants in agricultural fields. The apparatus may include a memory storing a machine learning model trained to provide a petiole nutritional value output based on leaf spectral data input, and at least one processor communicatively coupled to the memory. The at least one processor may be collectively configured to receive leaf spectral data generated by a portable spectrophotometer based on non-destructive spectral measurements of leaves of plants in agricultural fields, store the leaf spectral data in the memory, input the leaf spectral data to the machine learning model, receive an output including a petiole nutritional value measured by the machine learning model based on the leaf spectral data, and provide a near real time result to a mobile device, the near real time result being indicative of a petiole nutritional value.

[0008]

[0008] According to some aspects, a non-transitory computer readable medium is provided that includes instructions executable by a processor. The instructions, when executed, may configure the processor to receive leaf spectral data generated by a portable spectrophotometer based on non-destructive spectral measurements of plant leaves in a field, store the leaf spectral data in a memory, input the leaf spectral data to a machine learning model trained to provide a petiole nutritional value output based on the leaf spectral data input, receive an output including the petiole nutritional value measured by the machine learning model based on the leaf spectral data, and provide a near real-time result indicative of the petiole nutritional value.

[0009]

[0009] According to some aspects, a system for non-destructively measuring petiole nutritional value from leaves of plants in an agricultural field in near real time is provided. The system may include a portable spectrophotometer and a remote server. The portable spectrophotometer may be configured to generate leaf spectral data based on the non-destructive spectral measurement of the leaves of the plants in the agricultural field, and to transmit the leaf spectral data for receipt by the remote server via a network. The remote server may include a processor and a memory, and the memory stores a machine learning model trained to provide a petiole nutritional value output based on the leaf spectral data input. The remote server may be configured to receive the leaf spectral data, store the leaf spectral data in the memory, input the leaf spectral data to the machine learning model, receive the petiole nutritional value measured by the machine learning model based on the leaf spectral data, and provide a near real time result indicative of the petiole nutritional value to a mobile device.

[0010]

[0010] The drawings included in this specification are intended to illustrate various examples of the items, methods, and devices of the specification and are not intended to limit the scope of the content taught in any way. [Brief description of the drawings]

[0011] [Figure 1]

[0011] FIG. 1 shows a plant leaf whose nutritional value can be measured according to one embodiment. [Diagram 2]

[0012] FIG. 1 is a schematic diagram illustrating a system for non-destructively measuring petiole nutritional value from plant leaves in a field in near real time, according to one embodiment. [Diagram 3]

[0013] FIG. 3 is a schematic diagram of results showing petiole nutritional value provided by the system of FIG. 2 and displayed graphically on a mobile device. [Figure 4]

[0014] FIG. 1 is a schematic diagram showing an apparatus for non-destructively measuring petiole nutritional value from plant leaves in agricultural fields in near real time, according to one embodiment. [Diagram 5]

[0015] 1 is a flow chart illustrating an exemplary method for non-destructively measuring petiole nutritional value from plant leaves in the field in near real time using a portable spectrophotometer. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012]

[0016] Numerous embodiments are described herein and are presented for illustrative purposes only. The described embodiments are not intended to be limiting in any way. The present invention is broadly applicable to numerous embodiments, as will be readily apparent from the disclosure herein. Those skilled in the art will appreciate that the present invention can be implemented with modifications and variations without departing from the teachings disclosed herein. Although certain features of the present invention may be described with reference to one or more specific embodiments or drawings, it should be understood that such features are not limited to the use in the one or more specific embodiments or drawings referenced in describing them.

[0013]

[0017] The terms "an embodiment," "embodiment," "embodiments," "the embodiment," "the outline," "one or more embodiments," "some embodiments," and "one embodiment" mean "one or more (but not all) embodiments of the invention," unless expressly specified otherwise.

[0014]

[0018] The terms "including," "comprising," and variations thereof mean "including but not limited to," unless expressly specified otherwise. A list of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms "a," "an," and "the" mean "one or more," unless expressly specified otherwise.

[0015]

[0019] As used herein and in the claims, two or more parts are considered to be "coupled," "connected," "attached," "joined," "attached," or "fixed" when the parts are joined or operated together either directly or indirectly (i.e., through one or more intermediate parts) so long as a link occurs. As used herein and in the claims, two or more parts are considered to be "directly coupled," "directly connected," "directly attached," "directly joined," "directly attached," or "directly fixed" when the parts are connected in physical contact with one another. As used herein, two or more parts are considered to be "rigidly coupled," "rigidly connected," "rigidly attached," "rigidly joined," "rigidly attached," or "rigidly fixed" when the parts are connected such that they move as one while maintaining a constant orientation with respect to one another. None of the terms "coupled," "connected," "attached," "joined," "attached," and "fixed" distinguish the manner in which two or more parts are joined together.

[0016]

[0020] Additionally, although method steps may be described (in this disclosure and / or in the claims) sequentially, such methods may be configured to operate in alternative orders. In other words, any sequence or order of steps that may be described does not indicate a requirement that the steps be performed in that order. Steps of methods described herein may in fact be performed in any order. Additionally, some steps may be performed simultaneously.

[0017]

[0021] As used in this specification and the claims, a group of elements is considered to perform an action "collectively" if the action is performed by any one of the elements in the group, or if the action is performed collaboratively by two or more (or all) elements in the group.

[0018]

[0022] As used in this specification and the claims, unless specifically noted otherwise, a first element will be considered to be "received within" a second element if at least a portion of the first element is received within the second element.

[0019]

[0023] Some elements herein may be identified by a portion number consisting of a base number followed by an alphabetic or subscript numeric suffix (e.g., 112a or 1121). Multiple elements herein may be identified by portion numbers that share a base number together and differ by their suffixes (e.g., 1121, 1122, and 1123). All elements with a common base number may be collectively or generically referred to using the base number without a suffix (e.g., 112).

[0020]

[0024] As used herein and in the claims, terms such as "top," "bottom," "above," "below," "upper," "vertical," "higher," "upper," and similar terms generally relate to an orientation that is aligned (e.g., parallel) with gravity. Terms such as "distal," "proximal," and similar terms generally relate to an orientation that is perpendicular (e.g., perpendicular) to gravity. However, the terms referred to in this paragraph do not imply any particular alignment between elements. For example, a first element may be considered to be "vertically above" a second element, with the first element being higher than the second element, regardless of whether the first element is vertically aligned with the second element.

[0021]

[0025] Generally, plant tissue tests for measuring the nutritional status of plants are destructive and do not provide real-time results. For example, farmers may harvest petioles from plants in the field for measurement. The harvested petioles are shipped to a laboratory for testing. Depending on the test being performed, it may be necessary to destructively remove a large amount of petioles.

[0022]

[0026] The laboratory can then perform plant tissue testing and provide the petiole nutritional value to the farmer. The process of petiole removal, shipping, testing, and result notification can take several weeks to complete. This delay undermines the effectiveness of any corrective action taken based on the results (e.g., adjusting fertilizer formulations and amounts) because the plant nutrients being measured may have changed during the intervening period.

[0023]

[0027] The disclosed system, apparatus, and method can provide a non-destructive and near real-time measurement of the nutritional status of a plant using a portable spectrophotometer. Molecules absorb light at frequencies that are characteristic of their structure. The portable spectrophotometer can be used to make non-destructive spectral measurements that indicate the presence and concentration of various molecules (nutrients) in the probed area. The portable spectrophotometer can generate leaf spectral data (based on the non-destructive spectral measurements) that can be used to measure the nutritional status of the plant. The plant leaves or petioles do not need to be removed from the plant for testing. The disclosed system, apparatus, and method can provide the nutritional status of a plant without destroying any of the plants used for testing.

[0024]

[0028] Moreover, the disclosed systems, devices, and methods can perform analysis and provide results in near real time, for example, within 10-20 seconds. This can allow for immediate implementation of any necessary corrective action (based on the measured nutritional status of the plant). As used herein and in the claims, an action is considered to be performed in "near real time" if it is performed in less than one minute from the time the action is initiated.

[0025]

[0029] In canopy reflectance measurements, a ground or airborne platform is used to measure the ratio of the amount of light leaving the canopy to the amount of incoming light. Canopy reflectance measurements can be used to calculate crop nutritional status. However, canopy reflectance measurements are often subject to atmospheric and soil interference. The disclosed systems, apparatus, and methods are capable of probing plant leaves using a portable spectrophotometer, which avoids the atmospheric and / or soil interference associated with performing canopy reflectance measurements using ground or airborne platforms.

[0026]

[0030] 1, there is shown a plant 10 whose nutritional status may be measured using the disclosed systems, devices, or methods. In the illustrated example, the plant 10 includes a stem 14, a leaf 18, and a petiole 22 that attaches the leaf 18 to the stem 14. The leaf 18 includes a blade 26 and a number of veins 30.

[0027]

[0031] The surface area of ​​the leaf blade 26 may be significantly greater compared to the petiole 22, and therefore spectral reflectance measurements may be taken from the leaf blade 26 instead of the petiole 22. The nutritional value measured may therefore indicate the nutritional value corresponding to the leaf blade instead of the petiole nutritional value.

[0028]

[0032] The nutritional value of the petiole is not the same as the nutritional value measured on other parts of the leaf (e.g., the blade). Currently and historically, farmers have sent the petiole to a laboratory, and the laboratory has sent back the petiole nutritional value. Thus, farmers generally understand what corrective actions are needed based on the laboratory petiole nutritional value, but not on the nutritional value associated with other parts of the plant.

[0029]

[0033] The disclosed systems, devices, and methods can take spectral measurements from a plant's leaf blade to generate leaf spectral data and calculate petiole nutritional value based on the leaf spectral data. Providing the petiole nutritional value (as opposed to the nutritional value associated with the leaf) can enable a user to select and apply known and understood corrective actions.

[0030]

[0034] 2, a schematic diagram of a system 100 for non-destructively measuring petiole nutritional value in near real time from leaves 18 of plants in a field 34 is shown, according to one embodiment. As shown, the system 100 may include a portable spectrophotometer 104 and a remote server 108.

[0031]

[0035] In the illustrated example, the portable spectrophotometer 104 and the remote server 108 are communicatively coupled using a network 112. The network 112 may include a communications network, such as the Internet, a wide area network (WAN), a local area network (LAN), or another type of network. In other examples, the portable spectrophotometer 104 and the remote server 108 may not be connected to a common network, but may communicate using intermediate devices and / or communications networks.

[0032]

[0036] The field 34 may include a plurality of plants 10 (e.g., at least 500 plants). Each plant 10 may have one or more leaves 18. The plants 10 may include any suitable plants grown in agricultural applications. For example, the plants 10 may include wheat, corn, soybeans, potatoes, etc. The field 34 may be located outdoors or inside a greenhouse.

[0033]

[0037] The portable spectrophotometer 104 may have any suitable design that is portable and capable of making non-destructive spectral measurements of the leaves of the plant 10. The portable spectrophotometer 104 may be sized and designed to be carried by an operator while the operator uses the portable spectrophotometer to make spectral measurements. For example, the portable spectrophotometer 104 may be battery powered and designed to be carried by the operator 38 in a backpack.

[0034]

[0038] A light source within the portable spectrophotometer 104 can generate incident light on the leaves, and a detector within the portable spectrophotometer 104 can measure the light reflected from the leaves of the plant 10. The portable spectrophotometer 104 can generate spectral data for the leaves based on the reflected spectral measurements.

[0035]

[0039] In some embodiments, the portable spectrophotometer 104 may perform spectral measurements and generate leaf spectral data in the visible and / or near infrared wavelength range. For example, the portable spectrophotometer 104 may generate leaf spectral data in a wavelength range of 400 nm to 2500 nm. In other examples, the portable spectrophotometer 104 may generate leaf spectral data in a smaller (e.g., 400 nm to 2000 nm) or larger (e.g., 350 nm to 2500 nm) wavelength range. A smaller wavelength range may enable faster completion of the spectral measurements and / or require fewer resources to calculate the petiole nutritional value. A larger wavelength range may enable measurement of petiole nutritional value with higher accuracy and / or detection of a larger number of nutrients. The wavelength range may be adjusted manually or automatically based on the nutritional value being measured. For example, the operator 38 may manually adjust the wavelength range. In other examples, the wavelength range may be adjusted automatically, for example by the remote server 108.

[0036]

[0040] The portable spectrophotometer 104 may perform spectral measurements at discrete wavelength intervals within the wavelength range being measured. For example, the portable spectrophotometer 104 may perform spectral measurements at 0.5 nm wavelength intervals within the wavelength range being measured. The wavelength intervals used during the measurements may be adjusted manually or automatically based on the nutritional value being measured. For example, the operator 38 may adjust the wavelength interval manually. In other examples, the wavelength intervals may be adjusted automatically, for example by the remote server 108. Different wavelength intervals may be used, ranging from 0.1 nm to 10 nm. A smaller wavelength interval may enable a more accurate measurement of nutritional value and / or a larger number of nutrients to be measured. A larger wavelength interval may enable a faster measurement of nutritional value and / or require fewer computing resources to measure nutritional value.

[0037]

[0041] In some embodiments, the portable spectrophotometer 104 may be a NIRS™ DS2500 Analyzer or an ASD™ FieldSpec4 Spectroradiometer, while in other embodiments, the portable spectrophotometer 104 may be any other suitable spectrophotometer.

[0038]

[0042] The portable spectrophotometer 104 may be configured to transmit the generated leaf spectral data for receipt by the remote server 108. In the illustrated example, the portable spectrophotometer 104 may transmit the generated leaf spectral data to the remote server 108 over the network 112. In other examples, the portable spectrophotometer 104 may transmit the generated leaf spectral data to an intermediate device, such as a mobile device 42 of the operator 38. The portable spectrophotometer 104 may transmit the generated leaf spectral data to the mobile device 42 using point-to-point communication or a local area network (e.g., Bluetooth™), and the mobile device 42 may then transmit the leaf spectral data to the remote server 108 using a wide area network / Internet.

[0039]

[0043] The mobile device 42 may be any suitable device that is portable and capable of transmitting and receiving data over a communications network (e.g., a wireless communication network such as a cellular network and / or a Wi-Fi network). For example, the mobile device 42 may be a laptop, tablet device, or smartphone with wireless communications capabilities. The mobile device 42 may be a dedicated companion device designed for use with the portable spectrophotometer 104. In some embodiments, the mobile device 42 may be a general-purpose device that includes proprietary software for communicating with the portable spectrophotometer 104 and / or for analyzing data received from the portable spectrophotometer 104. In some embodiments, the functionality of the portable spectrophotometer 104 and the mobile device 42 may be provided by a single device.

[0040]

[0044] In the illustrated example, the remote server 108 includes a processor 116 and a memory 120. The processor 116 may control the operation of the remote server 108. The processor 116 may be any suitable processor, controller, or digital signal processor capable of providing sufficient processing power depending on the configuration, purpose, and requirements of the system 100, as known to those skilled in the art. For example, the processor 116 may be a high-performance general-purpose processor. For example, the processor 116 may include a standard processor, such as an Intel® processor or an AMD® processor. Alternatively, the processor 116 may include multiple processors, each configured to perform a different dedicated task. Alternatively, specialized hardware (e.g., a graphics processing unit (GPU)) may be used to provide some of the functionality provided by the processor 116.

[0041]

[0045] The memory 120 may include one or more of a random access memory (RAM), a read only memory (ROM), a hard drive, and a flash memory (e.g., a solid state drive). The remote server 108 may store the received leaf spectral data in the memory 120. For example, the remote server 108 may receive the leaf spectral data generated by the portable spectrophotometer 104 and store the received leaf spectral data in the memory 120.

[0042]

[0046] The memory 120 may also store a machine learning model trained to provide a petiole nutritional value output based on the leaf spectral data input. The machine learning model may be generated and trained by the remote server 108. In some embodiments, the remote server 108 may receive a machine learning model generated by an external device. The remote server 108 may train the received machine learning model and store the trained machine learning model in the memory 120. In other embodiments, the trained machine learning model may be received by the remote server 108 from an external device. The remote server 108 may store the trained machine learning model in the memory 120.

[0043]

[0047] The leaf spectral data input provided to the machine learning model may include, for example, leaf spectral data generated by the portable spectrophotometer 104 by taking spectral measurements from the plant leaf blade. The petiole nutritional value output provided by the machine learning model may include, for example, petiole nutritional value measured for one or more nutrients such as N, P, K, Ca, Mg, S, Mn, Zn, Fe, B, Cu, Al, and Na. The petiole nutritional values ​​for different nutrients may be expressed as percentages (of total mass), g / kg, and / or parts per million (ppm).

[0044]

[0048] The machine learning model may be any suitable machine learning model based on the input data to be analyzed, the output data requirements, the available training data, and / or the available computing resources. In some embodiments, the machine learning model may be a multiple linear regression (MLR) model. A Lasso subset selection method may be performed on the MLR model to generate the Lasso regression model. In other embodiments, the machine learning model may be a different model, for example a polynomial regression model.

[0045]

[0049] Training data for the machine learning model may be generated by taking spectral measurements of a plant (e.g., reflectance spectral measurements from a leaf blade portion) and generating spectral data of the leaf that acts as a predictor (x) for the machine learning model. The corresponding petiole of the plant may be removed and used for laboratory histology analysis. The results of the laboratory histology analysis of the petiole may act as the response (y) for the machine learning model. A model of correlation may be constructed between the response (y) and the predictor (x). Equation (1) below describes the correlation coefficient β i 1 provides an example of an MLR model using

[0046]

number

[0047]

[0050] If the number of predictors (x) is greater than the number of responses (y), this can result in overfitting of the MLR model. The accuracy of the MLR model can be improved by using subset selection methods to select a subset of predictors. In some embodiments, Lasso MLR modeling can be used as the subset selection method. In other embodiments, other subset selection methods such as stepwise subset selection or Bayesian model averaging can be used.

[0048]

[0051] Lasso MLR modeling may be used to identify the most informative least redundant predictors (wavelengths) using a complexity parameter (λ) that controls the amount of shrinkage; the larger the value of λ, the more severe the penalty for non-zero coefficients in the model may be, resulting in more shrinkage imposed on the coefficient values. The Lasso regression model may be generated by selecting the value of λ that minimizes the root mean square error (RMSE). The selected λ parameter may measure the number of coefficients that make up the Lasso regression model, and is selected as the one with the greatest explanatory power in relation to the target predictor. Model training and performance evaluation may be performed using, for example, 5-fold cross-validation with the value of λ selected based on the minimum RMSE. Table 1 below shows the number of bands, areas, and values ​​of the first four major wavebands resulting from Lasso MLR modeling of different elements, e.g., leaf spectral data obtained from potato plants.

[0049] [Table 1]

[0050]

[0052] The processor 116 may input the received (e.g., from the portable spectrophotometer 104) and / or stored (e.g., in the memory 120) leaf spectral data into the trained machine learning model. The machine learning model can measure the petiole nutritional value based on the leaf spectral data. For example, the machine learning model can measure the petiole nutritional value for N, P, K, Ca, Mg, S, Mn, Zn, Fe, B, Cu, Al, and Na based on the leaf spectral data generated by performing spectral measurements on the leaf blades of potato plants. The processor 116 can receive the petiole nutritional value measured by the machine learning model.

[0051]

[0053] The processor 116 can provide near real-time results indicative of the petiole nutritional value to a mobile device. For example, the processor 116 can provide the near real-time results to a mobile device 42 of an operator 38. For example, the processor 116 can provide the near real-time results to the mobile device 42 in less than one minute (e.g., within 0.01 to 30 seconds) from when the portable spectrophotometer transmits the spectral measurement. This can enable the operator 38 to non-destructively collect near real-time results indicative of the petiole nutritional value from multiple plants in a field.

[0052]

[0054] Reference is now made to Figures 2 and 3. Figure 3 shows a schematic diagram of results provided by the system 100 and displayed graphically on the mobile device 42, showing petiole nutritional value. The mobile device 42 may include a touch screen display 46 that provides an interactive display to the operator 38. The touch screen display 46 may provide multiple display portions 50a-50c. For example, the display portion 50a may provide a graphical display of the measured petiole nutritional value. In the illustrated example, the display portion 50a provides a bar graph displaying the measured petiole nutritional value for six nutrients. In other embodiments, the display 46 may not be configured to detect touch input.

[0053]

[0055] Display unit 50b may provide additional information regarding the measured petiole nutritional value, such as the number of leaf spectral measurements made, a confidence level associated with the measured petiole nutritional value, etc. Display unit 50c may provide recommended corrective actions based on the measured petiole nutritional value. In some examples, one or more of display units 50 may include other information not related to the results indicating the petiole nutritional value.

[0054]

[0056] Referring now to FIG. 4, a schematic diagram of an apparatus 200 for non-destructively measuring petiole nutritional value from leaves of plants in a field in near real time is shown. In some embodiments, the apparatus 200 may be implemented as a server device (e.g., the remote server 108 shown in FIG. 2). In other embodiments, the apparatus 200 may be implemented using any other suitable hardware components. For example, the apparatus 200 may be implemented as a mobile device 42 (FIGS. 2 and 3), may be integrated with the portable spectrophotometer 104 (FIG. 2), or may be implemented as a companion device usable with the portable spectrophotometer 104 (FIG. 2).

[0055]

[0057] 4, the apparatus 200 includes a memory 208, an application 212, an output device 216, a display device 220, a secondary storage device 224, a processor 228, an input device 232, and a communication device 236. One or more (or all) of the memory 208, the application 212, the output device 216, the display device 220, the secondary storage device 224, the processor 228, the input device 232, and the communication device 236 may be communicatively coupled by wires and / or wirelessly.

[0056]

[0058] In some embodiments, the device 200 includes any one or more of a plurality of the following: memory 208, an application 212, an output device 216, a display device 220, a secondary storage device 224, a processor 228, an input device 232, and a communication device 236. In some embodiments, the device 200 does not include one or more of the application 212, the secondary storage device 224, a network connection, an input device 232, an output device 216, a display device 220, and a communication device 236.

[0057]

[0059] In at least one embodiment, the device 200 includes a connection to a network 204, such as a wired or wireless connection to the Internet or a private network. In some cases, the network 204 includes other types of computer or telecommunications networks. The device 200 may receive leaf spectral data via the network 204 generated by a portable spectrophotometer (e.g., portable spectrophotometer 104 of FIG. 2) based on non-destructive spectral measurements of leaves of plants (e.g., potato plants) in a field (e.g., field 34 of FIG. 2).

[0058]

[0060] In some embodiments, the apparatus 200 may receive the leaf spectral data directly from a portable spectrophotometer, over a wire or wirelessly, while in other embodiments, the apparatus 200 may receive the leaf spectral data generated by the portable spectrophotometer from an intermediate device (e.g., the mobile device 42 of FIG. 2).

[0059]

[0061] The memory 208 may include one or more of random access memory (RAM) and read only memory (ROM). In some embodiments, the memory 208 stores one or more applications 212 for execution by the processor 228. The applications 212 correspond to software modules that include computer-executable instructions for performing operations for the functions and methods described herein.

[0060]

[0062] The memory 208 may store a machine learning model trained to provide a petiole nutritional value output based on the leaf spectral data input. The machine learning model may be generated and trained by the device 200. In some embodiments, the device 200 may receive a generated machine learning model from an external device (e.g., via the network 204) and train the received machine learning model. In some embodiments, the device 200 may receive a trained machine learning model from an external device (e.g., via the network 204) and store the trained machine learning model in the memory 208. The machine learning model may be any suitable model, for example, a Lasso regression model.

[0061]

[0063] In some embodiments, the memory 208 may also store received leaf spectral data, measured petiole nutritional value, and / or results derived from the petiole nutritional value (e.g., graphical representations of the petiole nutritional value). The stored data may be retrieved by a user for historical data analysis.

[0062]

[0064] Secondary storage device 224 may include any suitable non-transitory computer-readable medium containing instructions executable by a processor (e.g., processor 228). For example, secondary storage device 224 may include a hard drive, a floppy drive, a CD drive, a DVD drive, a Blu-ray drive, a solid-state drive, flash memory, or other type of non-volatile data storage. Processor 228 may execute instructions contained on secondary storage device 224 to perform operations for the functions and methods described herein.

[0063]

[0065] In some embodiments, device 200 stores information in a remote storage device, such as cloud storage, accessible over a network, such as network 204 or another network. In some embodiments, device 200 stores information distributed across multiple storage devices, such as memory 208 and secondary storage device 224 (i.e., each of the multiple storage devices stores some of the information and the multiple storage devices collectively store all of the information). Thus, as used herein and in the claims, storing data on a storage device can mean storing the data in a local storage device, storing the data in a remote storage device, or storing the data in a distributed manner across multiple storage devices, each of which can be local or remote.

[0064]

[0066] The input device 232 can include any device for inputting information into the apparatus 200. For example, the input device 232 can be a keyboard, a keypad, a cursor device, a touch screen, a camera, or a microphone. The input device 232 can also include input ports and wireless communications (e.g., Bluetooth or 802.11x) for wired and wireless connections to external devices.

[0065]

[0067] Display device 220 may include any type of device for presenting visual information. For example, display device 220 may be a computer monitor, a flat screen display, a projector, or a display panel.

[0066]

[0068] The output device(s) 216 may include any type of device for presenting a hard copy of information, such as, for example, a printer. The output device(s) 216 may also include other types of output devices, such as, for example, speakers. In at least one embodiment, the output device(s) 216 includes one or more of an output port and wireless communication (e.g., Bluetooth, or 802.11x) for wired and wireless connections to external devices.

[0067]

[0069] The communications device 236 may have any design suitable for receiving analog and / or digital inputs and for providing analog and / or digital outputs, in some embodiments, the communications device 236 may include separate modules for analog and digital signals.

[0068]

[0070] Processor 228 may be any device capable of executing applications, computer readable instructions, or programs. The applications, computer readable instructions, or programs may be stored in memory 208 or secondary storage device 224, or may be received from remote storage accessible, for example, via network 204. Processor 228 may be a high performance general purpose processor, a standard processor (e.g., an Intel® processor or AMD® processor), specialized hardware (e.g., a GPU), or a multi-processing device that collectively perform the functions provided by processor 228.

[0069]

[0071] The processor 228 may input the leaf spectral data (e.g., the leaf spectral data stored in the memory 208) into a trained machine learning model (e.g., the trained machine learning model stored in the memory 208). The machine learning model may determine the petiole nutritional value based on the leaf spectral data. The processor 228 may receive an output from the machine learning model including the petiole nutritional value determined by the machine learning model.

[0070]

[0072] In some embodiments, the processor 228 can provide near real-time results indicative of the petiole nutritional value to a mobile device (e.g., mobile device 42 of FIG. 2). The petiole nutritional value can be displayed graphically on the mobile device. In other embodiments, the processor 228 can provide results to the display device 220, and the petiole nutritional value can be displayed graphically on the display device 220.

[0071]

[0073] FIG. 4 shows a schematic diagram of an example of the hardware of the device 200. In alternative embodiments, the device 200 includes fewer, additional, or different components. In addition, although aspects of the implementation of the device 200 are described as being stored in memory, those skilled in the art will appreciate that these aspects can also be stored on or read from other types of computer program products or computer readable media, such as a hard disk, floppy disk, CD, or DVD; a carrier wave from the Internet or other network; or other forms of secondary storage devices, including RAM or ROM. For example, the device 200 can include a non-transitory computer readable medium that stores computer readable instructions that, when executed by the processor 228, configure the processor 228 to perform the methods described herein.

[0072]

[0074] Referring now to Figure 5, a flow chart is shown illustrating an exemplary method 300 for non-destructively measuring petiole nutritional value from plant leaves in the field in near real time using a portable spectrophotometer. Method 300 can be performed, for example, using system 100 (Figure 2) or device 200 (Figure 4), also referenced below in Figures 2 and 4.

[0073]

[0075] The method 300 may be performed while carrying the portable spectrophotometer in the field. For example, the portable spectrophotometer 104 may be carried on a person or vehicle in the field 34.

[0074]

[0076] At 308, non-destructive spectral measurements of leaves of plants in the field may be made using a carried spectrophotometer to generate leaf spectral data. For example, non-destructive spectral measurements of leaves of potato plants may be made using the portable spectrophotometer 104. The portable spectrophotometer 104 may generate the leaf spectral data based on the spectral measurements.

[0075]

[0077] At 312, the leaf spectral data may be stored in memory. In some embodiments, the generated leaf spectral data may be transmitted over a network to a remote server and stored in memory of the remote server. For example, the portable spectrophotometer 104 may transmit the generated leaf spectral data over the network 112 to the remote server 108, which may store the leaf spectral data in memory 120.

[0076]

[0078] In other embodiments, the generated leaf spectral data may be transmitted to a mobile device, and the mobile device may transmit the leaf spectral data over a network to a remote server. The leaf spectral data may be stored in a memory of the remote server. For example, the portable spectrophotometer 104 may transmit the generated leaf spectral data to the mobile device 42, and the mobile device 42 may transmit the leaf spectral data over the network 112 to the remote server 108. The remote server 108 may store the leaf spectral data in memory 120.

[0077]

[0079] At 316, the processor may calculate the petiole nutritional value based on the stored leaf spectral data. The processor may calculate the petiole nutritional value by inputting the leaf spectral data into a machine learning model trained to provide a petiole nutritional value output based on the leaf spectral data input. In response, the machine learning model may output the petiole nutritional value to the processor. For example, the processor 116 (or the processor 228) may input the leaf spectral data into a trained machine learning model stored in the memory 120 (or the memory 208). The machine learning model may be a Lasso regression model.

[0078]

[0080] At 320, near real-time results indicative of the petiole nutritional value may be provided. In some embodiments, the near real-time results may be provided by transmitting the petiole nutritional value to a mobile device. The petiole nutritional value may then be displayed graphically on the mobile device. For example, the remote server 108 or the apparatus 200 may provide the near real-time results by transmitting the petiole nutritional value to the mobile device 42. The mobile device 42 may display the petiole nutritional value graphically.

[0079]

[0081] In some embodiments, if there are additional leaves for measurement, method 300 may proceed to 308. For example, method 300 may loop from 308 to 320 for at least five other plant leaves in the field within 10 minutes. This may allow near real-time results to be obtained from multiple leaves within a short period of time that indicate the petiole nutrient value corresponding to the non-destructive spectral measurement. This may then allow the farm to simultaneously react to the returned petiole nutrient value (e.g., by adjusting fertilizer mix and / or amount) before time has passed that changes the measured plant nutrients. This may improve the effectiveness of corrective actions, which may improve crop yield and / or volume.

[0080]

[0082] Although the above provides examples of embodiments, it will be understood that some features and / or functions of the described embodiments can be modified without departing from the spirit and principles of operation of the described embodiments. Therefore, the foregoing is intended to be illustrative and non-limiting of the present invention, and those skilled in the art will appreciate that other variations and modifications can be made without departing from the scope of the present invention as defined in the claims appended hereto. The claims should not be limited by the preferred embodiments and examples, but should be given the broadest interpretation consistent with the description as a whole.

[0081] item

[0083] Item 1: A method for non-destructively measuring petiole nutritional value from leaves of plants in an agricultural field in near real time using a portable spectrophotometer, the method including: (a) taking non-destructive spectral measurements of leaves of plants in an agricultural field using the portable spectrophotometer to generate leaf spectral data; (b) storing the leaf spectral data in a memory; (c) calculating by a processor petiole nutritional value based on the stored leaf spectral data; and (d) providing a near real-time result indicative of the petiole nutritional value.

[0082]

[0084] Item 2: The method of item 1, wherein providing includes transmitting the petiole nutritional value to a mobile device.

[0083]

[0085] Item 3: The method of item 1 or 2, wherein providing further comprises graphically displaying the petiole nutritional value on a mobile device.

[0084]

[0086] Item 4: A method according to any one of items 1 to 3, wherein storing includes transmitting the leaf spectral data to a remote server via a network, the remote server comprising a memory and a processor.

[0085]

[0087] Item 5: A method according to any one of items 1 to 4, wherein storing includes the mobile device receiving the leaf spectral data, and the mobile device transmitting the leaf spectral data to a remote server via a network, the remote server comprising a memory and a processor.

[0086]

[0088] Item 6: A method according to any of items 1 to 5, wherein providing includes transmitting the petiole nutritional value from a remote server to the mobile device and graphically displaying the petiole nutritional value on the mobile device.

[0087]

[0089] Item 7: The method according to any one of items 1 to 6, further comprising repeating steps (a) to (d) for at least five other plant leaves in the field within 10 minutes.

[0088]

[0090] Item 8: The method according to any one of Items 1 to 7, wherein the plant is a potato plant.

[0089]

[0091] Item 9: The method of any of items 1 to 8, wherein the calculating includes inputting the leaf spectral data into a machine learning model trained to provide petiole nutritional value based on the leaf spectral data input, and the machine learning model outputs the petiole nutritional value.

[0090]

[0092] Item 10: The method of any of items 1 to 9, wherein the machine learning model is a Lasso regression model.

[0091]

[0093] Item 11: An apparatus for non-destructively measuring petiole nutritional value from leaves of plants in a agricultural field in near real time, the apparatus comprising: a memory storing a machine learning model trained to provide a petiole nutritional value output based on a leaf spectral data input; and at least one processor communicatively coupled to the memory, wherein the at least one processor is collectively configured to: receive leaf spectral data generated by a portable spectrophotometer based on a non-destructive spectral measurement of leaves of plants in the agricultural field; store the leaf spectral data in the memory; input the leaf spectral data to the machine learning model; receive an output including the petiole nutritional value measured by the machine learning model based on the leaf spectral data; and provide a near real-time result to a mobile device, wherein the near real-time result is indicative of the petiole nutritional value.

[0092]

[0094] Item 12: The apparatus described in Item 11, wherein the petiole nutritional value is displayed graphically on the mobile device.

[0093]

[0095] Item 13: An apparatus described in Item 11 or 12, wherein the leaf spectral data generated by the portable spectrophotometer is received from a mobile device via a network.

[0094]

[0096] Item 14: The device according to any of items 11 to 13, wherein the plant is a potato plant.

[0095]

[0097] Item 15: The apparatus of any of items 11 to 14, wherein the machine learning model is a Lasso regression model.

[0096]

[0098] Item 16: The device of any of items 11 to 15, wherein the memory includes one or more of a random access memory (RAM), a read-only memory (ROM), a hard drive, and a flash memory.

[0097]

[0099] Item 17: A non-transitory computer-readable medium including instructions executable by a processor, the instructions, when executed, configuring the processor to receive leaf spectral data generated by a portable spectrophotometer based on non-destructive spectral measurements of plant leaves in a field, store the leaf spectral data in a memory, input the leaf spectral data to a machine learning model trained to provide a petiole nutritional value output based on the leaf spectral data input, receive an output including the petiole nutritional value measured by the machine learning model based on the leaf spectral data, and provide a near real-time result indicative of the petiole nutritional value.

[0098]

[0100] Item 18: The non-transitory computer-readable medium of Item 17, wherein the leaf spectral data generated by the portable spectrophotometer is received from a mobile device over a network.

[0099]

[0101] Item 19: The non-transitory computer-readable medium of items 17 or 18, wherein the plant is a potato plant.

[0100]

[0102] Item 20: The non-transitory computer-readable medium of any of items 17 to 19, wherein the machine learning model is a Lasso regression model.

[0101]

[0103] Item 21: The non-transitory computer-readable medium of any of items 17 to 20, wherein the memory includes one or more of a random access memory (RAM), a read-only memory (ROM), a hard drive, and a flash memory.

[0102]

[0104] Item 22: A system for non-destructively measuring petiole nutritional value from leaves of plants in a field in near real time, the system comprising: a portable spectrophotometer configured to generate leaf spectral data based on non-destructive spectral measurements of leaves of plants in the field and to transmit the leaf spectral data for receipt by a remote server via a network; and a remote server comprising a processor and a memory, the memory storing a machine learning model trained to provide a petiole nutritional value output based on the leaf spectral data input, the remote server configured to receive the leaf spectral data, store the leaf spectral data in the memory, input the leaf spectral data to the machine learning model, receive the petiole nutritional value measured by the machine learning model based on the leaf spectral data, and provide a near real-time result indicative of the petiole nutritional value to a mobile device.

[0103]

[0105] Item 23: The system described in Item 22, wherein the petiole nutritional value is displayed graphically on a mobile device.

[0104]

[0106] Item 24: A system described in Item 22 or 23, wherein the portable spectrophotometer is configured to transmit the leaf spectral data to a mobile device, and the mobile device transmits the leaf spectral data to a remote server.

[0105]

[0107] Item 25: The system according to any one of items 22 to 24, wherein the plant is a potato plant.

[0106]

[0108] Item 26: The system of any of items 22 to 25, wherein the machine learning model is a Lasso regression model.

[0107]

[0109] Item 27: The system of any of items 22 to 26, wherein the memory includes one or more of a random access memory (RAM), a read-only memory (ROM), a hard drive, and a flash memory.

Claims

1. A method for non-destructively measuring the petiole nutritional value of plant leaves in agricultural fields in near real time using a portable spectrophotometer, wherein the method is: (a) In order to generate spectral data of leaves, non-destructive spectral measurements of plant leaves in the farmland are performed using the portable spectrophotometer. (b) storing the spectral data of the leaf in memory, (c) The nutritional value of the petiole is calculated by a processor based on the stored spectral data of the leaf, and (d) To provide near real-time results showing the nutritional value of the petiole, Methods that include...

2. The method according to claim 1, wherein the provision includes transmitting the petiole nutritional value to a mobile device.

3. The method according to claim 2, further comprising providing a graph display of the petiole nutritional value on the mobile device.

4. The method according to claim 1, wherein the storage includes transmitting the spectral data of the leaf to a remote server via a network, the remote server comprising the memory and the processor.

5. The method according to claim 1, wherein the storage includes a mobile device receiving the spectral data of the leaf, and the mobile device transmitting the spectral data of the leaf to a remote server via a network, the remote server comprising the memory and the processor.

6. The method according to claim 5, wherein the provision includes transmitting the petiole nutritional value from the remote server to the mobile device, and displaying the petiole nutritional value graphically on the mobile device.

7. The method according to claim 1, further comprising repeating steps (a) to (d) for at least five other plant leaves in the farmland within 10 minutes.

8. The method according to claim 1, wherein the plant is a potato plant.

9. The method according to any one of claims 1 to 8, wherein the calculation comprises inputting the leaf spectral data into a machine learning model trained to provide petiole nutritional value based on the leaf spectral data input, and the machine learning model outputs the petiole nutritional value.

10. The method according to claim 9, wherein the machine learning model is a Lasso regression model.

11. A device for non-destructively measuring the petiole nutritional value of plant leaves in agricultural land in near real time, wherein the device is Memory for storing a machine learning model trained to provide petiole nutritional value output based on leaf spectral data input, At least one processor that is communicatively coupled to the memory, The at least one processor comprises, Based on non-destructive spectral measurements of plant leaves in the aforementioned farmland, the spectral data of the leaves generated by a portable spectrophotometer is received. To store the spectral data of the leaf in the memory, The spectral data of the aforementioned leaves is input to the machine learning model. To receive an output including the petiole nutritional value measured by the machine learning model based on the spectral data of the leaf, To provide near real-time results to mobile devices, The configuration is such that the near real-time results show the petiole nutritional value. Device.

12. The apparatus according to claim 11, wherein the nutritional value of the petiole is displayed graphically on the mobile device.

13. The apparatus according to claim 11, wherein spectral data of the leaf generated by the portable spectrophotometer is received from the mobile device via a network.

14. The apparatus according to claim 11, wherein the plant is a potato plant.

15. The apparatus according to claim 11, wherein the machine learning model is a Lasso regression model.

16. The apparatus according to any one of claims 11 to 15, wherein the memory includes one or more of random access memory (RAM), read-only memory (ROM), a hard drive, and flash memory.

17. A non-temporary computer-readable medium containing instructions that can be executed by a processor, wherein, when the instructions are executed, Based on non-destructive spectral measurements of plant leaves in agricultural fields, the spectral data of leaves generated by a portable spectrophotometer is received. The spectral data of the aforementioned leaf is stored in memory, The spectral data of the leaf is input to a machine learning model trained to provide petiole nutritional value output based on the spectral data input of the leaf. The system receives an output including the petiole nutritional value measured by the machine learning model based on the spectral data of the leaf, and To provide near real-time results showing the nutritional value of the petiole, The processor comprises, Non-temporary computer-readable media.

18. The non-temporary computer-readable medium according to claim 17, wherein the spectral data of the leaf generated by the portable spectrophotometer is received from a mobile device via a network.

19. The non-temporary computer-readable medium according to claim 17, wherein the plant is a potato plant.

20. The non-temporary computer-readable medium according to claim 17, wherein the machine learning model is a Lasso regression model.

21. The non-temporary computer-readable medium according to any one of claims 17 to 20, wherein the memory includes one or more of random access memory (RAM), read-only memory (ROM), a hard drive, and flash memory.

22. A system for non-destructively measuring the petiole nutritional value of plant leaves in agricultural fields in near real time, wherein the system is Based on non-destructive spectral measurements of plant leaves in the aforementioned agricultural land, spectral data of the leaves is generated, and To transmit the spectral data of the leaf for reception by a remote server over a network, The system consists of a portable spectrophotometer and The remote server comprises a processor and memory, Equipped with, The memory stores a machine learning model trained to provide petiole nutritional value output based on leaf spectral data input, and the remote server, To receive spectral data of the aforementioned leaves, To store the spectral data of the leaf in the memory, The spectral data of the aforementioned leaves is input to the machine learning model. The system receives the petiole nutritional value measured by the machine learning model based on the spectral data of the leaf, and To provide a mobile device with near real-time results showing the nutritional value of the petiole, Composed, system.

23. The system according to claim 22, wherein the nutritional value of the petiole is displayed graphically on the mobile device.

24. The system according to claim 22, wherein the portable spectrophotometer is configured to transmit spectral data of the leaf to the mobile device, and the mobile device transmits spectral data of the leaf to the remote server.

25. The system according to claim 22, wherein the plant is a potato plant.

26. The system according to claim 22, wherein the machine learning model is a Lasso regression model.

27. The system according to any one of claims 22 to 26, wherein the memory includes one or more of random access memory (RAM), read-only memory (ROM), a hard drive, and flash memory.

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