FIELD OF INVENTION

[0001] Embodiments of the present disclosure relates food safety and security followed by analysis of risk related to agricultural finance,insurance and exports; amore particularly to a system and method for seed quality rapid assessment.
BACKGROUND
[0002] Agricultural sector plays a strategic role in course of economic development of a country. Its role in the economic development of agri based countries including developing and underdeveloped countries is critical in food security, self-reliance and foreign currency accumulation by exports. However, to make agricultural and horticulture profitable, in the present world, it has to be done at a large scale with integration of technology. With onset of increased management of technological tools and applications, its benefits to this agricultural sector may help radically. Inadequate outcomes due to overseeing defects in crops and lack of smarter implementations may deter expansion of the agriculture. It is need of hour to augment conventional techniques to examine crops, seasonal pattern changes and aftermath of harvesting in general with modern techniques. Rapid seed quality detection requires custom feature extraction model due to complexity of images.
[0003] There are high resolution cameras present in research labs and scientist may detect impurities from a sample of seed taken from these high-resolution cameras, that are costly, requires controlled environment and time consuming. However, this is not sufficient as the sample of the seed may be of N number in order to detect the impurities. Sometimes, farmers may want to detect impure seeds at that moment of time. The farmers may not have such high-resolution cameras present with them and the farmer may not have any technical knowledge related to resolution, pixels and the like. Currently, determining germination quality of the seed is quite complex even with the help of existing tools.
[0004] Hence, there is a need for an improved a system and method for seed quality rapid assessment.
SUMMARY
[0005] This summary is provided to introduce a selection of concepts, in a simple manner, which is further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the subject matter nor to determine the scope of the disclosure.
[0006] Embodiments of the present disclosure comprises an Artificial Intelligence (AI) based computing system for determining quality of seeds and germination pattern. The AI based computing system comprises one or more hardware processors and a memory coupled to the one or more hardware processor. The memory comprises a plurality of modules in the form of programmable instructions executable by the one or more hardware processors. The plurality of modules comprises an image receiver module configured to receive one or more images of a plurality of seeds from one or more electronic devices. The plurality of modules further comprises an operation performing module configured to perform one or more operations on the received one or more images to enhance quality of the received one or more images. The plurality of modules further comprises a value determination module configured to determine one or more computational values between one or more channels associated with the enhanced one or more images based one or more thresholds. The one or more channels correspond to Red, Green and Blue (RGB) channels. The plurality of modules further comprises feature determination module configured to determine one or more features corresponding to each of the plurality of seeds in the enhanced one or more images based on the determined one or more computational values by using an image processing based AI model. The one or more features comprises variance, energy, contrast, correlation, dissimilarity, and homogeneity. The plurality of modules further comprises a region detection module configured to detect one or more regions in the enhanced one or more images based on one or more colors, pixel value analysis of the enhanced one or more images, the determined one or more computational values and the extracted one or more features by using the image processing based AI model. The plurality of modules further comprises a data management module configured to perform at least one of or a combination of predicting germination pattern of the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, moisture content and texture of the plurality of seeds by using the image processing based AI model. The germination pattern corresponds to ratio of good and bad quality of seeds in the plurality of seeds. The data management module may be further configured to perform detecting mixture of different types of seeds in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. The data management module may be further configured to perform determining percentage of one or more impurities in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by utilizing the image processing based AI model. The one or more impurities comprise seed of different crop/s, husk, soil particles and stones. The data management module may be further configured to perform detecting a set of deformed seeds in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. A data output module configured to output the predicted germination pattern, the detected mixture of different types of seeds, the detected percentage of the one or more impurities and the detected set of deformed seeds on user interface screen of the one or more electronic devices.
[0007] Embodiments of another disclosure comprises an Artificial Intelligence (AI) based method for determining quality of seeds. The AI based method comprises receiving, by one or more hardware processors, one or more images of a plurality of seeds from one or more electronic devices. The AI based method further comprises performing, by the one or more hardware processors, one or more operations on the received one or more images to enhance quality of the received one or more images. The AI based method further comprises determining, by the one or more hardware processors, one or more computational values between one or more channels associated with the enhanced one or more images based one or more thresholds. The one or more channels correspond to Red, Green and Blue (RGB) channels. The AI based method further comprises determining, by the one or more hardware processors, one or more features corresponding to each of the plurality of seeds in the enhanced one or more images based on the determined one or more computational values by using an image processing based AI model. The one or more features comprises variance, energy, contrast, correlation, dissimilarity, and homogeneity. The AI based method further comprises detecting, by the one or more hardware processors, one or more regions in the enhanced one or more images based on one or more colors, pixel value analysis of the enhanced one or more images, the determined one or more computational values and the extracted one or more features by using the image processing based AI model. The AI based method further comprises performing, by the one or more hardware processors, at least one of or a combination of predicting germination pattern of the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, moisture content and texture of the plurality of seeds by using the image processing based AI model. The germination pattern corresponds to ratio of germinating and non germinating seeds in the plurality of seeds. The AI based method further comprises further performing, by the one or more hardware processors, at least one of or a combination of detecting mixture of different types of seeds in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. The AI based method further comprises further performing, by the one or more hardware processors, at least one of or a combination of determining percentage of one or more impurities in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. The one or more impurities comprise: seed type, husk, soil particles and stones. The AI based method further comprises further performing, by the one or more hardware processors, at least one of or a combination of detecting a set of deformed seeds in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. The AI based method further comprises further performing, by the one or more hardware processors, at least one of or a combination of outputting, by the one or more hardware processors, the predicted germination pattern, the detected mixture of different types of seeds, the detected percentage of the one or more impurities and the detected set of deformed seeds on user interface screen of the one or more electronic devices.
[0008] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF DRAWINGS
[0009] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0010] FIG. 1 is a block diagram depicting a seed quality rapid assessment environment, in accordance with an embodiment of the present disclosure;
[0011] FIG. 2 is a block diagram depicting plurality of modules of an AI based computing system, in accordance with an embodiment of the present disclosure; and
[0012] FIG. 3 is a process flowchart depicting an Artificial Intelligence (AI) based method for determining quality of seeds, in accordance with an embodiment of the present disclosure.
[0013] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0014] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated online platform, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0015] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
[0016] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, subsystems, elements, structures, components, additional devices, additional subsystems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0018] Throughout this document, the terms browser and browser application may be used interchangeably to mean the same thing. In some aspects, the terms web application and web app may be used interchangeably to refer to an application, including metadata, that is installed in a browser application. In some aspects, the terms web application and web app may be used interchangeably to refer to a website and/or application to which access is provided over a network (e.g., the Internet) under a specific profile (e.g., a website that provides email service to a user under a specific profile). The terms extension application, web extension, web extension application, extension app and extension may be used interchangeably to refer to a bundle of files that are installed in the browser application to add functionality to the browser application. In some aspects, the term application, when used by itself without modifiers, may be used to refer to, but is not limited to, a web application and/or an extension application that is installed or is to be installed in the browser application.
[0019] A computer system (standalone, client or server computer system) configured by an application may constitute a “module” (or “subsystem”) that is configured and operated to perform certain operations. In one embodiment, the “module” or “subsystem” may be implemented mechanically or electronically, so a module may comprise dedicated circuitry or logic that is permanently configured (within a special-purpose processor) to perform certain operations. In another embodiment, a “module” or “subsystem” may also comprise programmable logic or circuitry (as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations.
[0020] Accordingly, the term “module” or “subsystem” should be understood to encompass a tangible entity, be that an entity that is physically constructed permanently configured (hardwired) or temporarily configured (programmed) to operate in a certain manner and/or to perform certain operations described herein.
[0021] Referring now to the drawings, and more particularly to FIGs. 1 through 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[0022] A computer system (standalone, client or server computer system) configured by an application may constitute a “module” that is configured and operated to perform certain operations. In one embodiment, the “module” may be implemented mechanically or electronically, so a module may comprise dedicated circuitry or logic that is permanently configured (within a special-purpose processor) to perform certain operations. In another embodiment, a “module” may also comprise programmable logic or circuitry (as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations.
[0023] Accordingly, the term “module” should be understood to encompass a tangible entity, be that an entity that is physically constructed permanently configured (hardwired) or temporarily configured (programmed) to operate in a certain manner and/or to perform certain operations described herein.
[0024] FIG. 1 is a block diagram depicting a seed quality rapid assessment environment 100, in accordance with an embodiment of the present disclosure. The seed quality rapid assessment environment 100 comprises one or more electronic devices 102, AI based computing system 104, plurality of modules 106 and a network 108. The one or more electronic devices 102 are mobile devices utilized to capture image of a seed or set of seeds. Detailed explanation on the AI based computing system 104 and the plurality of modules 106 is explained in FIG. 2. The network 108 may be a wireless network or a wired network. The present invention is conceptualized based on AI algorithm. The present invention deals with pattern recognition in pixel differences in its neighbors. This may predict quality of seed and may rely on texture. The present invention’s model is improvised and focused on custom image feature extraction engineering. The present invention may detect presence of chemical on seed from the captured image within thirty seconds’ time. The present invention predicts germination pattern of seeds based on processed image and algorithm. The present invention may be a mobile application which is trained to predict the germination pattern of the seeds from a single image. The present invention relies on samples to identify behavioral changes after the seeds are influenced by chemicals. The samples may be images of good seeds, deformed seeds, and chemically treated seeds. Here, Histogram and Oriented Gradient (HOG) is applied on a pre-processed image which provides magnitude and orientation (angle) from its neighbours if pixel difference is significant in positive or negative orientation and if magnitude increases. This helps in distinguishing between quality of seeds and its degree to its quality measurement. As their intensity may be different and gradient value may be different with respect to its neighbours. The present invention calculates impurity ratio from a given set of images of seeds by image processing. The calculation of the impurity ratio is performed with the help of image quantization of colours and threshold tuning. The following are the steps utilized in calculation of the impurity ratio. At step one, pixel intensity or values are identified from segmented region which are affected by disease and is stored separately for each type of the disease. At step two, features are present in the form of pixels for one segment if the seed is affected by a disease. At step three, a new input image is pre-processed and segmented. Further, the image is checked for any segmented area closest to any pre-defined feature. If yes, then name of the disease affected on the seed is returned with respect to the feature identified. Further, HOG (Histogram and Oriented Gradients) is applied which provides magnitude and orientation (angle) from its neighbours if pixel difference is significant in positive or negative orientation and if magnitude increases. This helps in distinguishing between quality of seeds and its degree to its quality measurement. As their intensity may be different and gradient value may be different with respect to its neighbours. The present invention utilises a stepwise approach to identify region of interest where the present invention potentially detects anomaly in infected area of the seed.
[0025] FIG. 2 is a block diagram depicting plurality of modules 106 of an AI based computing system 104, in accordance with an embodiment of the present disclosure.
[0026] The processor(s) 202, as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0027] The memory 206 includes a plurality of modules 106 stored in the form of executable program which instructs the processor 202 via a bus 204 to perform the method steps illustrated in FIG. 2.
[0028] The AI based computing system 104 comprises one or more hardware processors and a memory coupled to the one or more hardware processors. The memory 206 comprises the plurality of modules 106 in the form of programmable instructions executable by the one or more hardware processors.
[0029] The plurality of modules 106 comprises an image receiver module 208 configured to receive one or more images of a plurality of seeds from one or more electronic devices 102.
[0030] The plurality of modules 106 further comprises an operation performing module 210 configured to perform one or more operations on the received one or more images to enhance quality of the received one or more images. Further, in performing the one or more operations on the received one or more images to enhance the quality of the received one or more images, the operation preforming module 210 is configured to enhance contrast of the received one or more images by using CLAHE (Contrast Limited Adaptive Histogram Equalization) technique, convert size of the received one or more images to 256*256 size upon enhancing contrast of the received one or more images and convert the received one or more images from Blue, Green, Red (BGR) format to Red, Green, Blue (RGB) format upon converting size of the received one or more images.
[0031] The plurality of modules 106 further comprises a value determination module 212 configured to determine one or more computational values between one or more channels associated with the enhanced one or more images based one or more thresholds. For every color there is a specific color range which is utilized to identify the color. For example, [47,75,52] and [200,225,23] may be the range. Further, the one or more channels correspond to RGB channels. The value determination module 212 is further configured to analyze the one or more colors in the enhanced one or more images based on the pixel value analysis and the one or more channels and determine the one or more computational values between the one or more channels associated with the enhanced one or more images based on the one or more thresholds and result of the analysis.
[0032] The plurality of modules 106 further comprises a feature determination module 214 configured to determine one or more features corresponding to each of the plurality of seeds in the enhanced one or more images based on the determined one or more computational values by using an image processing based AI model. The one or more features comprises variance, energy, contrast, correlation, dissimilarity, and homogeneity. The threshold value provided is based on exploratory analysis of extracted features. K-Nearest Neighbour (KNN) algorithm is utilized on the extracted features to predict healthiness of the seeds. The features may be extracted features.
[0033] The plurality of modules 106 further comprises a region detection module 216 configured to detect one or more regions in the enhanced one or more images based on one or more colors, pixel value analysis of the enhanced one or more images, the determined one or more computational values and the extracted one or more features by using the image processing based AI model.
[0034] The plurality of modules 106 further comprises a data management module 218 configured to perform at least one of or a combination of predicting germination pattern of the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, moisture content and texture of the plurality of seeds by using the image processing based AI model. The germination pattern corresponds to ratio of good and bad quality of seeds in the plurality of seeds. The data management module 218 is further configured to further perform at least one of or a combination of detecting mixture of different types of seeds in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. Examples of the mixture of seed comprises a set of seeds from Uttar Pradesh or set of seeds from Punjab and the like. The mixture of seeds may also be the seeds of different traits, different quality, age variation and the like. The data management module 218 is further configured to further perform at least one of or a combination of determining percentage of one or more impurities in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. The one or more impurities comprises husk, soil particles and stones. The data management module 218 is further configured to further perform at least one of or a combination of detecting a set of deformed seeds in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. The data management module 218 is further configured to correlate the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds with each other by using the image processing based AI model. The data management module 218 is further configured to predict the germination pattern of the plurality of seeds based on the result of correlation. Four environmental factors such as light, water, oxygen, and temperature affect germination. All plants have specific germination requirements based on ecological adaptations and environmental cues that trigger germination for that species. In this case, how many seeds are germinating out of ten seeds from a sample of the seeds is termed as the germination pattern. If seven seeds are germinating out of ten seeds then, it may be termed as seventy percentage germination pattern. The data management module 218 is further configured for detecting the mixture of different types of seeds in the plurality of seeds based on the result of correlation. The data management module 218 is further configured for determining the percentage of the one or more impurities in the plurality of seeds based on the result of correlation. The data management module 218 is further configured for detecting the set of deformed seeds in the plurality of seeds based on the result of correlation. Seeds are sorted into six categories. A first category is normal seedlings which possess essential structures that are indicative of their ability to produce useful mature plants under favourable field conditions. A second category is abnormal seedlings which does not have all essential structures or is damaged, deformed or decayed and prevents normal development. A third category is seeds which are neither hard nor dormant or have not produced any part of a seedling and are termed as dead seeds. A fourth category is viable seeds. Other than hard seed which fail to germinate when provided with prescribed germination conditions are known as dormant seeds. A fifth category is fresh seeds which have imbibed moisture however have failed to germinate and may be dormant. A sixth category is hard seeds which remain hard at end of a test period because their impermeable seed coats prevent absorption of water.
[0035] The plurality of modules 106 further comprises a data output module 220 configured to output the predicted germination pattern, the detected mixture of different types of seeds, the detected percentage of the one or more impurities and the detected set of deformed seeds on user interface screen of the one or more electronic devices 102.
[0036] The AI based computing system 104 further comprises a chemical determination module 222 configured to detect one or more behavioral changes in the plurality of seeds based on the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model. The chemical determination module 222 is further configured to determine if the plurality of seeds are exposed to one or more chemicals based on the detected one or more behavioral changes, the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model. The chemical determination module 222 is further configured to detect the one or more chemical based on the detected one or more behavioral changes, the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model upon determining that the plurality of seeds are exposed to the one or more chemicals. The one or more chemicals detected are outputted on the user interface screen of the one or more electronic devices 102. Examples of one or more behavioral changes may be a texture appearance which are influenced differently by chemical treatment or deposition of the one or more chemical on a surface.
[0037] FIG. 3 is a process flowchart depicting an Artificial Intelligence (AI) based method 300 for determining quality of seeds, in accordance with an embodiment of the present disclosure. At step 302, one or more images of a plurality of seeds are received by one or more hardware processors, from one or more electronic devices 102. At step 304, one or more operations are performed by one or more hardware processors, on the received one or more images to enhance quality of the received one or more images. At step 306, one or more computational values are determined by one or more hardware processors, between one or more channels associated with the enhanced one or more images based one or more thresholds. The one or more channels correspond to Red, Green, and Blue (RGB) channels. At step 308, one or more features are determined by one or more hardware processors, corresponding to each of the plurality of seeds in the enhanced one or more images based on the determined one or more computational values by using an image processing based AI model. The one or more features comprises variance, energy, contrast, correlation, dissimilarity, and homogeneity. At step 310, one or more regions are detected by one or more hardware processors, in the enhanced one or more images based on one or more colors, pixel value analysis of the enhanced one or more images, the determined one or more computational values and the extracted one or more features by using the image processing based AI model. At step 312, at least one of or a combination given below is performed such as predicting germination pattern of the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, moisture content and texture of the plurality of seeds by using the image processing based AI model. The germination pattern corresponds to ratio of good and bad quality of seeds in the plurality of seeds. At the step 312, mixture of different types of seeds are detected in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. At the step 312, percentage of one or more impurities in the plurality of seeds are determined based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. The one or more impurities comprises husk, soil particles and stones. At the step 312, a set of deformed seeds in the plurality of seeds is detected based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. At the step 312, the predicted germination pattern, the detected mixture of different types of seeds, the detected percentage of the one or more impurities and the detected set of deformed seeds are outputted by one or more hardware processors, on user interface screen of the one or more electronic devices 102.
[0038] In an embodiment of the present disclosure, the AI based method 300 further comprises performing the one or more operations on the received one or more images to enhance the quality of the received one or more images which comprises enhancing contrast of the received one or more images by using CLAHE (Contrast Limited Adaptive Histogram Equalization) technique, converting size of the received one or more images to 256*256 size upon enhancing contrast of the received one or more images and converting the received one or more images from Blue, Green, Red (BGR) format to Red, Green, Blue (RGB) format upon converting size of the received one or more images. The AI based method 300 further comprises determining the determined one or more computational values between the one or more channels associated with the enhanced one or more images based on the one or more thresholds comprises analyzing the one or more colors in the enhanced one or more images based on the pixel value analysis and the one or more channels and determining the one or more computational values between the one or more channels associated with the enhanced one or more images based on the one or more thresholds and result of the analysis. The AI based method 300 further comprises predicting the germination pattern, detecting the mixture of different types of seeds, determining the percentage of the one or more impurities and detecting the set of deformed seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model. The image processing based AI model comprises correlating the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds with each other by using the image processing based AI model, predicting the germination pattern of the plurality of seeds based on the result of correlation, detecting the mixture of different types of seeds in the plurality of seeds based on the result of correlation, determining the percentage of the one or more impurities in the plurality of seeds based on the result of correlation and detecting the set of deformed seeds in the plurality of seeds based on the result of correlation. The AI based method 300 further comprises detecting one or more behavioral changes in the plurality of seeds based on the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model, determining if the plurality of seeds are exposed to one or more chemicals based on the detected one or more behavioral changes, the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model and detecting the one or more chemical based on the detected one or more behavioral changes, the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model upon determining that the plurality of seeds are exposed to the one or more chemicals.
[0039] The present invention comprises an algorithm to detect the seed quality. The present invention first preprocesses a received image. The present invention further performs image enhancement. The image enhancement for contrast improvement of dark images is performed by an algorithm called CLAHE (Contrast Limited Adaptive Histogram Equalization). Further, the received image is resized to 256x256 size. Next, the received image is converted from RGB to BGR. Next, input image is scaled. The present invention further performs colour analysis based on pixels with the help of a sliding window. Here, colour based on interests are analysed with the help of pixel value analysis. For example, yellow or brown. This is analysed based on R, G and B channels. Computational values are measured between channels or tensors based on thresholds identified. In this case, relative colour range varies from seed to seed as the range of the colour. The present invention further performs removing noise and background from the captured images. Here, the noise and the background are removed other than object of interests based on the colour and the pixel value analysis. A Hue Saturation Value (HSV) model is generated for the received image. The present invention further performs feature extraction and texture analysis. Here, GLCM is utilized for measuring closeness of distribution of elements. Variance, energy, contrast, correlation, dissimilarity, and homogeneity are captured and computed as feature set for image analysis. Observations are measured in context to the mixture of different seeds, presence of impurities such as husk and soil particles, good quality and bad quality seeds and also the presence to the chemicals. Significant distinguishable patterns are observed with the help of the present invention’s approach when influenced to a specific set of chemicals to determine which chemicals have been introduced. The present invention further comprises a model prediction algorithm. Here, regions are identified on the captured images based on the colour and pixel value analysis. Texture and features of image of the seed are analysed. Ratio of good and bad quality seeds are identified from mixture. Percentage of Impurities such as husk, soil and stones are identified. Poor quality grains are evaluated if soaked in chemicals (evaluated against texture, colour and pixels) and the chemical utilized are identified if it’s part of training. Overall spread of the poor-quality seed are utilized to estimate the proportion of over all quality of the grains from the sample. Multiple iteration of the process with multiple samples from the same source justifies better accuracy.
[0040] In an embodiment, the present invention comprises the following advantages. The present invention may detect presence of chemical on seed from the captured image. The present invention predicts germination pattern of seeds based on processed image and algorithm. The present invention relies on samples to identify behavioral changes after the seeds are influenced with chemicals. In this case, HOG (Histogram and Oriented Gradients) is applied which provides magnitude and orientation (angle) from its neighbours if pixel difference is significant in positive or negative orientation and if magnitude increases. This helps in distinguishing between quality of seeds and its degree to its quality measurement. As their intensity may be different and gradient value may be different with respect to its neighbours. The present invention calculates impurity ratio from a given set of images of seeds by image processing. The following are the steps utilized in calculation of impurity ratio. At step one, pixel intensity or values are identified from segmented region which are affected by disease and is stored separately for each type of disease. At step two, features are present in the form of pixels for one segment if the seed if affected by a disease. At step three, a new input image is pre-processed and segmented. Further, the image is checked for any segmented area closest to any pre-defined feature. If yes, then name of the disease affected on the seed is returned with respect to the feature identified. Further, HOG (Histogram and Oriented Gradients) is applied which provides magnitude and orientation(angle) from its neighbours if the pixel difference is significant in positive or negative orientation and if magnitude increases. This helps in distinguishing between quality of seeds and its degree to its quality measurement. As their intensity may be different and gradient value may be different with respect to its neighbours. The present invention may perform seed quality rapid assessment for a single image captured by one or more electronic devices 102. The AI based algorithm can detect impurities, deformed seeds, mixture of different seeds and germination pattern of seeds from a mixed sample by image processing. The present invention may be a mobile application which is trained to predict germination pattern of seeds from a single image.
[0041] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[0042] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[0043] The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random-access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD.
[0044] Input/output (I/O) devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.
[0045] A representative hardware environment for practicing the embodiments may include a hardware configuration of an information handling/computer system in accordance with the embodiments herein. The system herein comprises at least one processor or central processing unit (CPU). The CPUs are interconnected via system bus 204 to various devices such as a random-access memory (RAM), read-only memory (ROM), and an input/output (I/O) adapter. The I/O adapter can connect to peripheral devices, such as disk units and tape drives, or other program storage devices that are readable by the system. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein.
[0046] The system further includes a user interface adapter that connects a keyboard, mouse, speaker, microphone, and/or other user interface devices such as a touch screen device (not shown) to the bus to gather user input. Additionally, a communication adapter connects the bus to a data processing network, and a display adapter connects the bus to a display device which may be embodied as an output device such as a monitor, printer, or transmitter, for example.
[0047] A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. When a single device or article is described herein, it will be apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.
[0048] The specification has described a method and a system for. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words "comprising," "having," "containing," and "including," and other similar forms are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[0049] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

WE CLAIM:

1. An Artificial Intelligence (AI) based computing system (104) for determining quality of seeds, the AI based computing system (104) comprising:
one or more hardware processors; and
a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of modules (106) in the form of programmable instructions executable by the one or more hardware processors, wherein the plurality of modules (106) comprises:
an image receiver module (208) configured to receive one or more images of a plurality of seeds from one or more electronic devices (102);
an operation performing module (210) configured to perform one or more operations on the received one or more images to enhance quality of the received one or more images;
a value determination module (212) configured to determine one or more computational values between one or more channels associated with the enhanced one or more images based one or more thresholds, wherein the one or more channels correspond to Red, Green and Blue (RGB) channels;
a feature determination module (214) configured to determine one or more features corresponding to each of the plurality of seeds in the enhanced one or more images based on the determined one or more computational values by using an image processing based AI model, wherein the one or more features comprise: variance, energy, contrast, correlation, dissimilarity and homogeneity;
a region detection module (216) configured to detect one or more regions in the enhanced one or more images based on one or more colors, pixel value analysis of the enhanced one or more images, the determined one or more computational values and the extracted one or more features by using the image processing based AI model;
a data management module (218) configured to perform at least one of or a combination of:
predicting germination pattern of the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, moisture content and texture of the plurality of seeds by using the image processing based AI model, wherein the germination pattern corresponds to ratio of good and bad quality of seeds in the plurality of seeds;
detecting mixture of different types of seeds in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model;
determining percentage of one or more impurities in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model, wherein the one or more impurities comprise: husk, soil particles and stones; and
detecting a set of deformed seeds in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model; and
a data output module (220) configured to output the predicted germination pattern, the detected mixture of different types of seeds, the detected percentage of the one or more impurities and the detected set of deformed seeds on user interface screen of the one or more electronic devices (102).

2. The AI based computing system (104) as claimed in claim 1, wherein in performing the one or more operations on the received one or more images to enhance the quality of the received one or more images, the operation preforming module is configured to:
enhance contrast of the received one or more images by using CLAHE (Contrast Limited Adaptive Histogram Equalization) technique;
convert size of the received one or more images to 256*256 size upon enhancing contrast of the received one or more images; and
convert the received one or more images from Blue, Green, Red (BGR) format to Red, Green, Blue (RGB) format upon converting size of the received one or more images.

3. The AI based computing system (104) as claimed in claim 1, wherein in determining the determined one or more computational values between the one or more channels associated with the enhanced one or more images based on the one or more thresholds, the value determination module (212) is configured to:
analyze the one or more colors in the enhanced one or more images based on the pixel value analysis and the one or more channels; and
determine the one or more computational values between the one or more channels associated with the enhanced one or more images based on the one or more thresholds and result of the analysis.

4. The AI based computing system (104) as claimed in claim 1, wherein in predicting the germination pattern, detecting the mixture of different types of seeds, determining the percentage of the one or more impurities and detecting the set of deformed seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model, the data management module (218) is configured to:
correlate the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds with each other by using the image processing based AI model;
predict the germination pattern of the plurality of seeds based on the result of correlation;
detecting the mixture of different types of seeds in the plurality of seeds based on the result of correlation;
determining the percentage of the one or more impurities in the plurality of seeds based on the result of correlation; and
detecting the set of deformed seeds in the plurality of seeds based on the result of correlation.

5. The AI based computing system (104) as claimed in claim 1, further comprises a chemical determination module (222) configured to:
detect one or more behavioral changes in the plurality of seeds based on the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model;
determine if the plurality of seeds are exposed to one or more chemicals based on the detected one or more behavioral changes, the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model; and
detect the one or more chemical based on the detected one or more behavioral changes, the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model upon determining that the plurality of seeds are exposed to the one or more chemicals.

6. An Artificial Intelligence (AI) based method for determining quality of seeds, the AI based method comprising:
receiving, by one or more hardware processors, one or more images of a plurality of seeds from one or more electronic devices (102);
performing, by the one or more hardware processors, one or more operations on the received one or more images to enhance quality of the received one or more images;
determining, by the one or more hardware processors, one or more computational values between one or more channels associated with the enhanced one or more images based one or more thresholds, wherein the one or more channels correspond to Red, Green and Blue (RGB) channels;
determining, by the one or more hardware processors, one or more features corresponding to each of the plurality of seeds in the enhanced one or more images based on the determined one or more computational values by using an image processing based AI model, wherein the one or more features comprise: variance, energy, contrast, correlation, dissimilarity and homogeneity;
detecting, by the one or more hardware processors, one or more regions in the enhanced one or more images based on one or more colors, pixel value analysis of the enhanced one or more images, the determined one or more computational values and the extracted one or more features by using the image processing based AI model;
performing, by the one or more hardware processors, at least one of or a combination of:
predicting germination pattern of the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, moisture content and texture of the plurality of seeds by using the image processing based AI model, wherein the germination pattern corresponds to ratio of good and bad quality of seeds in the plurality of seeds;
detecting mixture of different types of seeds in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model;
determining percentage of one or more impurities in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model, wherein the one or more impurities comprise: husk, soil particles and stones; and
detecting a set of deformed seeds in the plurality of seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model; and
outputting, by the one or more hardware processors, the predicted germination pattern, the detected mixture of different types of seeds, the detected percentage of the one or more impurities and the detected set of deformed seeds on user interface screen of the one or more electronic devices (102).

7. The AI based method as claimed in claim 6, wherein performing the one or more operations on the received one or more images to enhance the quality of the received one or more images comprises:
enhancing contrast of the received one or more images by using CLAHE (Contrast Limited Adaptive Histogram Equalization) technique;
converting size of the received one or more images to 256*256 size upon enhancing contrast of the received one or more images; and
converting the received one or more images from Blue, Green, Red (BGR) format to Red, Green, Blue (RGB) format upon converting size of the received one or more images.

8. The AI based method as claimed in claim 6, wherein determining the determined one or more computational values between the one or more channels associated with the enhanced one or more images based on the one or more thresholds comprises:
analyzing the one or more colors in the enhanced one or more images based on the pixel value analysis and the one or more channels; and
determining the one or more computational values between the one or more channels associated with the enhanced one or more images based on the one or more thresholds and result of the analysis.

9. The AI based method as claimed in claim 6, wherein predicting the germination pattern, detecting the mixture of different types of seeds, determining the percentage of the one or more impurities and detecting the set of deformed seeds based on the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds by using the image processing based AI model comprise:
correlating the detected one or more regions, the extracted one or more features, the one or more colors, the moisture content and the texture of the plurality of seeds with each other by using the image processing based AI model;
predicting the germination pattern of the plurality of seeds based on the result of correlation;
detecting the mixture of different types of seeds in the plurality of seeds based on the result of correlation;
determining the percentage of the one or more impurities in the plurality of seeds based on the result of correlation; and
detecting the set of deformed seeds in the plurality of seeds based on the result of correlation.

10. The AI based method as claimed in claim 6, further comprises:
detecting one or more behavioral changes in the plurality of seeds based on the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model;
determining if the plurality of seeds are exposed to one or more chemicals based on the detected one or more behavioral changes, the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model; and
detecting the one or more chemical based on the detected one or more behavioral changes, the extracted one or more features, the one or more colors, the pixel value analysis and the texture of the plurality of seeds by using the image processing based AI model upon determining that the plurality of seeds are exposed to the one or more chemicals.