Description:TECHNICAL FIELD
[0001] The present disclosure relates, in general, to bioinformatics and data analysis, and more specifically, relates to a device and method for non-destructive estimation of the biochemical composition of a foliar member.

BACKGROUND
[0002] The present technologies use various optical techniques to assess leaf chlorophyll content quickly and non-destructively. Those methods are based on the absorbance or reflectance of certain wavelengths of light by intact leaves. In general, hand-held chlorophyll meters measure absorbance by the leaf of two different wavelengths in the spectral domain of red and near-infrared. As an output, they calculate index values e.g., Soil Plant Analysis Development (SPAD) value, chlorophyll content index (CCI) value that specify leaf chlorophyll content. Compared to hand-held chlorophyll absorbance meters, reflectance spectroscopy detects much more spectral data generating a great number of spectral information for interpretation. However, a proper selection of the abundant information sometimes poses a key problem for researchers. The limitations of handheld chlorophyll meters include:
• Limited penetration depth: Most of the existing devices usually measure chlorophyll content in a small area of the leaf's surface. As a result, they may not represent the overall chlorophyll content of the entire leaf accurately.
• Influence of leaf structure: The accuracy of handheld chlorophyll meters can be affected by leaf thickness and structure. Leaves with a high degree of variegation or complex leaf shapes may yield less accurate results.
• Light interference: External light conditions can interfere with the measurements, leading to variability in readings. Shade or direct sunlight can influence the readings, necessitating careful positioning of the device.
• Calibration: Handheld chlorophyll meters require periodic calibration to maintain accuracy, and improper calibration can lead to less reliable results.
• Inability to perform multiple tests at a single click
[0003] Multispectral and remote sensing devices are used for large-scale monitoring of chlorophyll content across fields and agricultural areas. They use aerial or satellite imagery and multispectral sensors to gather data. However, the limitations of multispectral and remote sensing devices include
• Cost and data processing: Multispectral and remote sensing devices can be expensive, and the processing of large datasets requires specialized software and expertise.
• Spatial resolution: The spatial resolution of aerial or satellite imagery may not be sufficient for precise measurements of chlorophyll content at the individual plant level.
• Environmental interference: Cloud cover, atmospheric conditions, and shadows can affect the accuracy of remote sensing measurements.
[0004] Yet another example includes a continuous excitation fluorimeter designed to measure the Kautsky Induction or Chlorophyll Fluorometers, Fast Chlorophyll Fluorescence Induction. The systems use focused, high-intensity light from red LED’s to induce a fast chlorophyll fluorescence response from a dark-adapted sample. Continuous systems require the use of a special leafclip system. This is a multi-purpose tool that provides dark adaptation for the sample (required for the measurement of maximum photochemical efficiency), defines the measurement area on the sample and prevents ambient light leakage into the highly sensitive photodiode used by the instrument for chlorophyll fluorescence detection. In addition to this, A modulated chlorophyll fluorometer uses sophisticated electronics to separate chlorophyll fluorescence from ambient light. The systems achieve this using a rapid pulsing excitation light in order to induce a corresponding pulsed fluorescence emission. The fluorometer uses a highly sensitive photodiode to detect and record the pulsed fluorescence signal and to ignore any non-pulsed signal. However, the limitations of chlorophyll fluorometers include
• Complexity: Chlorophyll fluorometers require a deeper understanding of the underlying principles and may require specialized training to operate correctly.
• Higher Cost: Chlorophyll fluorometers are generally more expensive than handheld chlorophyll meters, making them less accessible to some users.
• Specific Applications: While useful for research and precision farming, chlorophyll fluorometers may not be as practical for routine agricultural use due to their complexity and cost.
[0005] Therefore, it is desired to overcome the drawbacks, shortcomings, and limitations associated with existing solutions, and develop a cost-effective device that offers a comprehensive solution for non-destructively estimating total nutritional status and enhances plant health assessment and monitoring processes.

OBJECTS OF THE PRESENT DISCLOSURE
[0006] An object of the present disclosure relates, in general, to bioinformatics and data analysis, and more specifically, relates to a device and method for non-destructive estimation of biochemical composition of a foliar member.
[0007] Another object of the present disclosure is to provide a device that allows for simultaneous measurement of total chlorophyll, nitrogen, and magnesium content in a green leaf with a single click. This multi-test capability streamlines the analysis process and provides comprehensive insights.
[0008] Another object of the present disclosure is to provide a device that offers a cost-effective solution compared to existing chlorophyll meters, making advanced plant analysis accessible across different segments of society.
[0009] Another object of the present disclosure is to provide a device that uniquely combines the convenience of total chlorophyll, nitrogen, and magnesium measurement in one device, simplifying the testing procedure.
[0010] Another object of the present disclosure is to provide a device with IoT-enabled cloud connectivity, so real-time results can be accessed remotely. The real-time results can be accessed even in the absence of an internet connection. Additionally, the device generates timely advice on fertilizer treatments, nutritional adjustments based on the obtained data.
[0011] Another object of the present disclosure is to provide a device that features an intuitive and user-friendly interface, making it easily usable by researchers, farmers, and students alike.
[0012] Another object of the present disclosure is to provide a compact and lightweight device, along with battery operation, which makes the device highly portable. Its handheld nature enables convenient on-site measurements for real-time plant health monitoring.
[0013] Another object of the present disclosure is to provide a device that stands out for its rapid analysis capabilities, providing accurate results quickly. This enables efficient assessments of large plant populations in field conditions.
[0014] Another object of the present disclosure is to provide the non-destructive measurement of chlorophyll, nitrogen, and magnesium content in leaves and empowers researchers and farmers to monitor plant health without causing harm to the plants themselves.
[0015] Yet another object of the present disclosure is to provide a versatile and user-friendly device for comprehensive plant analysis and monitoring.

SUMMARY
[0016] The present disclosure relates, in general, to bioinformatics and data analysis, and more specifically, relates to a device and method for non-destructive estimation of the biochemical composition of a foliar member. The main objective of the present disclosure is to overcome the drawbacks, limitations, and shortcomings of the existing device and solution, by providing a cutting-edge battery-operated handheld device that amalgamates LED technology, digital data, advanced computing, and cloud connectivity to provide accurate estimations of biochemical composition in leaves. The real-time results can be accessed even in the absence of an internet connection. The biochemical composition in leaves include chlorophyll, total nitrogen, and total magnesium content in leaves. With its ability to calibrate for different plant types, transmit data to the cloud, and offer real-time feedback through the display, the device empowers plant researchers, farmers, and enthusiasts to make informed decisions about plant health and nutrition.
[0017] The present disclosure relates to a device for non-destructive estimation of biochemical composition of a foliar member, the device includes a holder configured to noninvasively accommodate a blade of the foliar member in a specified position. A light source with filter configured to emit a specific wavelength of light directed onto the foliar member interacts with the pigments of the foliar member inducing the reflection of specific wavelengths. A sensor located adjacent to the light source, the sensor captures a set of signals of the reflected light from the foliar member and converts to digital data. A processor operatively coupled to the sensor, the processor configured to receive, from the sensor, the digital data of the reflected light from the foliar member.
[0018] The processor can analyse the received digital data to extract a set of features. The set of features pertains to color patterns, and texture variations associated with biochemical composition present in the foliar member. The biochemical composition pertains to chlorophyll, nitrogen, and magnesium present within the foliar member. Further, the processor generates indices for the extracted features present in the foliar member. Based on the generated indices for the extracted features, the processor is configured to estimate the content of the biochemical composition present in the analysed foliar member to determine the health and nutrient status of the foliar member.
[0019] In addition, the processor transmits the estimated content in conjunction with the identifier associated with the analyzed foliage member to the cloud-based server via internet communication. The processor is operatively coupled to a learning engine, the learning engine is trained to estimate the content of biochemical composition in the foliar member. The learning engine is configured to execute a combination of computer vision algorithms based on artificial intelligence for estimating chlorophyll, nitrogen, and magnesium content directly on the device. The processor performs a calibration process to ensure that the indices generated by the device are optimized when dealing with different types of foliage members.
[0020] Moreover, the device enables the user to select a specific index associated with the foliar member, wherein upon selection of the relevant index associated with the chosen foliar member, the processor is fine-tuned to estimate accurate results for the specific foliar member being analyzed.
[0021] Besides, the device is powered by a battery, allowing it to operate without the need for a constant external power source and is equipped with global positioning system (GPS) and mapping capabilities, which collectively facilitate the effective evaluation and monitoring of chlorophyll, nitrogen and magnesium levels across extensive geographical regions.
[0022] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0023] The following drawings form part of the present specification and are included to further illustrate aspects of the present disclosure. The disclosure may be better understood by reference to the drawings in combination with the detailed description of the specific embodiments presented herein.
[0024] FIG. 1 illustrates an exemplary device for non-destructive estimation of the biochemical composition of foliar members, in accordance with an embodiment of the present disclosure.
[0025] FIG. 2 illustrates exemplary functional components of the system in accordance with an embodiment of the present disclosure.
[0026] FIG. 3 illustrates an exemplary flow chart of a method for non-destructive estimation of biochemical composition of the foliar member, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0027] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0028] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0029] The present disclosure relates, in general, to bioinformatics and data analysis, and more specifically, relates to a device and method for non-destructive estimation of the biochemical composition of a foliar member. The proposed device disclosed in the present disclosure overcomes the drawbacks, shortcomings, and limitations associated with the conventional device by providing a battery-operated, hand-held IoT-enabled machine learning-based device that offers a comprehensive solution for non-destructively estimating total chlorophyll, nitrogen, and magnesium content in green leaves. Its unique combination of features makes it a cost-effective tool that simplifies and enhances plant health assessment and monitoring processes. The present disclosure can be described in enabling detail in the following examples, which may represent more than one embodiment of the present disclosure.
[0030] The advantages achieved by the device of the present disclosure can be clear from the embodiments provided herein. The non-destructive device can assess chlorophyll content without harming the plant, where some traditional techniques involve physically removing plant material for analysis, potentially damaging the plant and reducing its ability to photosynthesize. Sophisticated laboratory equipment and other non-destructive equipment currently available in the market used for chlorophyll extraction and analysis can be expensive and may require specialized training for operation, whereas the proposed device is portable, highly affordable, and easy to use, making them accessible to a wider range of users.
[0031] In agriculture, it is essential to monitor plant health and nutrient status across large fields. The proposed device is equipped with GPS and mapping capabilities that enable efficient assessment and monitoring of chlorophyll levels along with the leaf nitrogen and magnesium across vast areas. Traditional laboratory-based methods and most of the existing non-destructive analyzers often require sample collection and subsequent analysis, leading to delays in obtaining results. In contrast, the proposed device provides real-time measurements, allowing for immediate decisions and actions. Lack of knowledge on nitrogen, magnesium and chlorophyll content of a leaf and plant can hinder the process of decision-making in plant health monitoring. The proposed device can measure total chlorophyll, magnesium and nitrogen from the leaf in a single click giving proper estimation of the plant health.
[0032] Visual assessments of plant health based on leaf color are subjective and may not accurately reflect the actual chlorophyll content or the plant's overall health. The proposed device provides objective and precise measurements, reducing the potential for human error. Traditional visual inspection of plants might not detect early signs of stress or nutrient deficiencies, as visible symptoms may appear only at later stages. The proposed device allows for early detection, enabling timely interventions to address issues before they escalate. Measuring chlorophyll content through traditional methods often involved laborious and destructive techniques. These methods require chemical extraction of chlorophyll from leaves, which is time-consuming, requires specialized equipment, and results in leaf damage. The proposed device uniquely combines the convenience of total chlorophyll, nitrogen, and magnesium measurement in one device, simplifying the testing procedure. The description of terms and features related to the present disclosure shall be clear from the embodiments that are illustrated and described; however, the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents of the embodiments are possible within the scope of the present disclosure. Additionally, the invention can include other embodiments that are within the scope of the claims but are not described in detail with respect to the following description.
[0033] FIG. 1 illustrates an exemplary device for non-destructive estimation of the biochemical composition of foliar members, in accordance with an embodiment of the present disclosure.
[0034] Referring to FIG. 1, the Internet of things (IoT) based device 100 (also referred to as device 100, herein) for non-destructive estimation of the biochemical composition of the foliar member. The foliar member can be a plant, leaf or any combination thereof. The device 100 is configured to assess the health and nutritional status of plant leaves with precision. Weighing a mere 200 grams, this portable device comprises several crucial components that work harmoniously to deliver accurate and actionable information about the analyzed biochemical composition of the leaf, where the biochemical composition includes chlorophyll, total nitrogen, total magnesium content and any combination thereof. The device 100 can include the holder 102, light source with filter 104, sensor 106, a processor 108 and server 110.
[0035] The holder 102 is configured to noninvasively accommodate a blade of the foliar member in a specified position. The controlled holder 102 is a mechanism designed to maintain a consistent and fixed distance between the light source 104, and the sensor 106. This precise positioning ensures reducing variability in the analysis results. The device 100 operates in a non-destructive manner, where the device can assess the biochemical content of the leaves without causing harm to the leaves. This is essential for research, agricultural monitoring, and other applications where leaf health needs to be preserved.
[0036] The light source with filter 104 configured to emit a specific wavelength of light directed onto the foliar member interacts with the pigments of the foliar member inducing the reflection of specific wavelengths. The light source 104 is equipped with a high-intensity light-emitting diode (LED) light source. The LED emits a specific wavelength of light optimized for plant analysis. When directed towards the leaf, the light interacts with the leaf's pigments, causing them to reflect certain wavelengths. This reflected light carries crucial information about the biochemical composition of the leaf.
[0037] The sensor 106 located adjacent to the light source 104 captures a set of signals of the reflected light from the foliar member and converts it to digital data. The sensor 106 i.e., optical sensor/digital camera is positioned alongside the LED light source 104. The sensor 106 is capable of capturing digital data of the leaf's surface. The reflected light is captured by the sensor. These digital data are then subjected to further analysis.
[0038] The processor 108 operatively coupled to the sensor 106, the processor coupled to a memory, the memory storing instructions executable by the processor to receive the digital data of the reflected light from the foliar member. The processor 108 can analyse the received digital data to extract a set of features, the set of features pertains to color patterns, and texture variations associated with the biochemical composition present in the foliar member, where the biochemical composition pertains to chlorophyll, nitrogen, and magnesium present within the foliar member.
[0039] The processor 108 generates indices for the extracted features present in the foliar member. Based on the generated indices for the extracted features, the processor is configured to estimate the content of the biochemical composition present in the analysed foliar member to determine the health and nutrient status of the foliar member. For instance, if the device predicts low chlorophyll and nitrogen levels but high magnesium content for a particular leaf, it might suggest that the plant is experiencing nutrient deficiencies. Alternatively, if the device predicts balanced nutrient levels, the plant might be considered healthy.
[0040] Moreover, the processor 108 can include electronics module that hosts complex algorithms based on artificial intelligence and computer vision principles. These algorithms analyze the digital data captured by the sensor, extracting intricate information about the leaf's pigments and nutritional elements. The computing electronics plays a crucial role in deriving indices that correlate to chlorophyll, total nitrogen, and total magnesium content in the leaf.
[0041] In the implementation of an embodiment, the computer vision algorithm responsible for estimating chlorophyll, nitrogen, and magnesium content is executed directly on the device. The device captures digital data of green leaves using its built-in camera. These digital data contain visual information about the leaves' properties, such as color and texture. The captured digital data are then processed locally on the device using the computer vision algorithm. The algorithm applies a series of mathematical operations, filters, and techniques to analyze the digital data and extract relevant features. The algorithm identifies specific visual features related to chlorophyll, nitrogen, and magnesium content within the leaves. These features could include color patterns, texture variations, and other characteristics indicative of nutrient content. Based on the extracted features, the algorithm applies its estimation model. The algorithm generates estimation values for chlorophyll, nitrogen, and magnesium content. These values provide insights into the health and nutrient status of the analyzed leaves.
[0042] The device offers the user the ability to employ regression techniques, specifically exponential and 2-degree polynomial regression, within the estimation model. The device 100 enables the user to select a specific index associated with the foliar member e.g., plant or tree index, wherein upon selection of relevant index associated with the chosen foliar member, the processor is fine-tuned to estimate accurate results for the specific foliar member being analyzed.
[0043] These regression techniques are mathematical algorithms used to fit a curve or line to a set of data points. In this context, they serve as tools for calibrating the device's estimation model automatically. The device introduces the capability for the user to select a specific plant or tree index. A plant index refers to a predefined set of parameters or characteristics associated with a particular type of plant or tree. By choosing a relevant index, the user tailors the estimation model to account for the inherent variations in nutrient content estimation across different plant species. For instance, different plants might have distinct leaf structures, colors, and nutrient absorption rates. By selecting the appropriate plant index, the estimation model can be fine-tuned to provide more accurate results for the specific plant being analyzed.
[0044] The processor 108 transmits the estimated content in conjunction with the identifier associated with the analyzed foliage member to the cloud-based server 110 via internet communication. The processor 108 performs a calibration process to ensure that the indices generated by the device are optimized when dealing with different types of foliage member. The device is equipped with a global positioning system (GPS) and mapping capabilities, which collectively facilitate the effective evaluation and monitoring of chlorophyll concentrations, as well as nitrogen and magnesium levels across extensive geographical regions.
[0045] The computing electronics possess the capability to transmit the analysis results and associated indices to the cloud-based server 110. This enables users to access their data remotely and collaborate with experts for further insights. The ability to transmit data to the cloud is a key feature that enhances the versatility and usability of the device. In addition, the device includes an integrated display, which provides real-time feedback and results to the user. This display showcases essential information, such as the analysis progress, calibration status, and possibly even an initial assessment of the plant's health. The display enhances the user experience by offering immediate insights.
[0046] Moreover, the processor performs a calibration process to ensure that the indices generated by the device are optimized when dealing with different types of foliage member. The users can calibrate the device to optimize its performance for specific types of plants or leaves. This customization ensures that the indices derived from the analysis are accurate and relevant to the target plant species. The calibration process fine-tunes the instrument's algorithms and settings to achieve the best possible results. The operating parameters for the device are shown in Table 1 below.

Parameter Range
Chlorophyll index 0 – 100
Nitrogen index 0 – 100
Magnesium index 0 – 100
Table 1: Operating parameters for the device
[0047] The device 100 is powered by a battery, allowing it to operate without the need for a constant external power source. This portability ensures that it can be used in various environments without restrictions. The device is designed to be held and operated by hand. This ergonomic design enables users to easily carry and maneuver the device while conducting tests on leaves. The device is equipped with Internet of Things (IoT) capabilities, which means it can connect to the internet. This connectivity allows the device to communicate and exchange data with other devices or servers, facilitating remote monitoring, data storage, and analysis.
[0048] Thus, the present invention overcomes the drawbacks, shortcomings, and limitations associated with existing solutions, and provides a non-destructive device 100 that offers a breakthrough solution for assessing chlorophyll content without harming plants, addressing the limitations of traditional techniques that involve physically removing plant material. These approaches risk plant damage and hinder photosynthesis. Unlike expensive laboratory equipment or complex analyzers, the device is portable, affordable, and user-friendly, expanding access to a wider user base. In agriculture, effective monitoring of plant health and nutrient status across large fields is crucial. With integrated GPS and mapping capabilities, the device enables efficient evaluation of chlorophyll, leaf nitrogen, and magnesium levels across extensive areas. Unlike time-consuming traditional methods that require sample collection and subsequent analysis, the device provides real-time measurements, enabling immediate decision-making. The device's capability to simultaneously measure total chlorophyll, magnesium, and nitrogen in a single click facilitates accurate plant health assessment.
[0049] While subjective visual assessments of plant health can misrepresent chlorophyll content and overall well-being, the proposed device ensures objective and accurate measurements, minimizing the potential for human error. Moreover, the early detection capability of the device addresses a common challenge of traditional visual inspections missing early signs of nutrient deficiencies, allowing timely interventions. Unlike labor-intensive and damaging traditional techniques involving chemical chlorophyll extraction, the device offers a streamlined, non-destructive approach, revolutionizing plant health monitoring.
[0050] FIG. 2 illustrates exemplary functional components 200 of the system in accordance with an embodiment of the present disclosure.
[0051] In an aspect, the device 100 can include one or more processor(s) 108. The one or more processor(s) 108 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 108 are configured to fetch and execute computer-readable instructions stored in a memory. The memory may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0052] An interface(s) 202. The interface(s) 202 may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 202 may facilitate communication of the device 100. The interface(s) 202 may also provide a communication pathway for one or more components of the device 100. Examples of such components include, but are not limited to, processing engine(s) 204.
[0053] The processing engine(s) 204 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 204. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 204 may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 204 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 204. In such examples, the device 100 may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to device 100 and the processing resource. In other examples, the processing engine(s) 204 may be implemented by electronic circuitry and can include learning engine 206 and other engine 208.
[0054] In an exemplary embodiment, the dimensions of the device 100 are approximately L: 150mm, W: 90mm, H: 40mm, with a weight of around 300 gm. The primary display interface is a touch screen with a length of 14.47 cm and a resolution of 480x854 pixels or higher. It features a sensor of 8 MP or higher and operates on Android 6.0 or above. The processor is 1.3 GHz or higher, with a RAM of 1 GB or more and a ROM of 8 GB or more. The device is powered by a lithium-ion battery with a lithium content of 0.75 gm, rated at 3000 mAh (9.25 Wh). The input rating is 5VDC 2000mA, and its maximum power consumption is 10W. It supports communication technologies including Wi-Fi, 3G or higher, and Bluetooth. The accessories include a clamp for loading sample leaves, a stylus, an adapter, and a data cable. The enclosure, sample holder, and stylus are made of ABS material. The device is classified as IPN0N0 for protection against water and particulate ingress. The electronics and adapter bear the marking IS 13252 (Part 1)/IEC 60950-1. Included components are the battery and instructional manual. The adapter's rating is input 100-240VAC~ 50/60Hz, 0.5A, output: 5VDC 2000mA.
[0055] FIG. 3 illustrates an exemplary flow chart of a method for non-destructive estimation of biochemical composition of the foliar member, in accordance with an embodiment of the present disclosure.
[0056] Method 300 includes at block 302, the holder configured to noninvasively accommodate a blade of the foliar member in a specified position. At block 304, the light source with filter configured to emit a specific wavelength of light directed onto the foliar member interacts with the pigments of the foliar member inducing the reflection of specific wavelengths. The sensor is located adjacent to the light source, the sensor captures a set of signals of the reflected light from the foliar member and converts to digital data. The processor operatively coupled to the sensor, the processor configured to receive the digital data of the reflected light from the foliar member.
[0057] At block 306, the processor can analyse the received digital data to extract a set of features, the set of features pertains to color patterns, texture variations associated with biochemical composition present in the foliar member. At block 308, the processor can generate indices for the extracted features present in the foliar member, where based on the generated indices for the extracted features, the processor is configured to estimate the content of the biochemical composition present in the analysed foliar member to determine the health and nutrient status of the foliar member.
[0058] It will be apparent to those skilled in the art that the device 100 of the disclosure may be provided using some or all of the mentioned features and components without departing from the scope of the present disclosure. While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT INVENTION
[0059] The present invention provides a device that allows for simultaneous measurement of total chlorophyll, nitrogen, and magnesium content in a green leaf with a single click. This multi-test capability streamlines the analysis process and provides comprehensive insights.
[0060] The present invention provides a device that offers a cost-effective solution compared to existing chlorophyll meters, making advanced plant analysis accessible across different segments of society.
[0061] The present invention provides a device that uniquely combines the convenience of total chlorophyll, nitrogen, and magnesium measurement in one device, simplifying the testing procedure.
[0062] The present invention provides a device with IoT-enabled cloud connectivity, so real-time results can be accessed remotely. The real-time results can be accessed even in the absence of an internet connection. Additionally, the device generates timely advice on fertilizer treatments, nutritional adjustments based on the obtained data.
[0063] The present invention provides a device that features an intuitive and user-friendly interface, making it easily usable by researchers, farmers, and students alike.
[0064] The present invention provides a compact and lightweight device, along with battery operation, making the device highly portable. Its handheld nature enables convenient on-site measurements for real-time plant health monitoring.
[0065] The present invention provides a device that stands out for its rapid analysis capabilities, providing accurate results quickly. This enables efficient assessments of large plant populations in field conditions.
[0066] The present invention provides non-destructive measurement of chlorophyll, nitrogen, and magnesium content in leaves, and empowers researchers and farmers to monitor plant health without causing harm to the plants themselves.
[0067] The present invention provides a versatile and user-friendly device for comprehensive plant analysis and monitoring.
, Claims:1. A device (100) for non-destructive estimation of the biochemical composition of a foliar member, the device comprising:
a holder (102) configured to noninvasively accommodate a blade of the foliar member in a specified position;
a light source with filter (104) configured to emit a specific wavelength of light directed onto the foliar member interacts with the pigments of the foliar member inducing the reflection of specific wavelengths;
a sensor (106) located adjacent to the light source, the sensor captures a set of signals of the reflected light from the foliar member and converts to a digital data;
a processor (108) operatively coupled to the sensor, the processor coupled to a memory, the memory storing instructions executable by the processor to:
receive, from the sensor, the digital data of the reflected light from the foliar member;
analyse the received digital data to extract a set of features, the set of features pertains to color patterns, texture variations associated with biochemical composition present in the foliar member; and
generate indices for the extracted features present in the foliar member,
wherein, based on the generated indices for the extracted features, the processor is configured to estimate the content of the biochemical composition present in the analysed foliar member to determine the health and nutrient status of the foliar member.
2. The device as claimed in claim 1, wherein the biochemical composition pertains to chlorophyll, nitrogen, and magnesium present within the foliar member.
3. The device as claimed in claim 1, wherein the processor (108) transmits the estimated content in conjunction with the identifier associated with the analyzed foliage member to a cloud-based server via internet communication.
4. The device as claimed in claim 1, wherein the processor (108) is operatively coupled to a learning engine (206), the learning engine trained to estimate the content of biochemical composition in the foliar member.
5. The device as claimed in claim 4, wherein the learning engine is configured to execute a combination of computer vision algorithms based on artificial intelligence for estimating chlorophyll, nitrogen, and magnesium content directly on the device.
6. The device as claimed in claim 1, wherein the processor (108) performs a calibration process to ensure that the indices generated by the device are optimized when dealing with different types of the foliage member.
7. The device as claimed in claim 1, wherein the device enables the user to select a specific index associated with the foliar member, wherein upon selection of relevant index associated with the chosen foliar member, the processor is fine-tuned to estimate accurate results for the specific foliar member being analyzed.
8. The device as claimed in claim 1, wherein the device (100) is equipped with global positioning system (GPS) and mapping capabilities, which collectively facilitate the effective evaluation and monitoring of chlorophyll, nitrogen and magnesium levels across extensive geographical regions.
9. A method (300) for non-destructive estimation of biochemical composition of a foliar member, the method comprising:
accommodating (302), by a holder, a blade of the foliar member noninvasively in a specified position;
receiving (304), at a processor, from a sensor, a digital data of the reflected light from the foliar member, the sensor located adjacent to a light source captures a set of signals of the reflected light from the foliar member and converts into the digital data, wherein the light source with filter configured to emit a specific wavelength of light directed onto the foliar member interacts with the pigments of the foliar member inducing the reflection of specific wavelengths;
analysing (306), at the processor, the received digital data to extract a set of features, the set of features pertains to colosr patterns, texture variations associated with biochemical composition present in the foliar member; and
generating (308), at the processor, indices for the extracted features present in the foliar member wherein, based on the generated indices for the extracted features, the processor is configured to estimate the content of the biochemical composition present in the analysed foliar member to determine the health and nutrient status of the foliar member.