Image processing and its algorithms in detecting the diseases in computer science.

Background

[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Generally, the crop diseases are identified if the person or farmer has the prior knowledge about the disease. Many times, the crop get affected and it also destroy the whole field and nearby.
[0004] In some reported cases, the crop sample has been collected and send to the laboratories to test the sample. Once the sample get checked, the crop disease has been identified.
[0005] The traditional systems are time-consuming; which results the destruction of crop.
[0006] In Indian patent 202141057273, the crop detection mechanism is discussed which is related to banana leaves disease using IoT devices with the aid of a new neural network and other algorithms.
[0007] The Indian patent 202111054299 discloses the system using sensors to monitor the crops and data has been sent via communication network.
[0008] The Indian patent 202111052444 discussed the hybrid algorithm of Generative Adversarial Networks (GAN) to train the deep learning network and deep learning algorithms with customized network which yields better results of the image classification through greater accuracy of the image. The device provides better results for classification of the paddy crops for disease detection automatically.
[0009] The Indian patent 202111045797 discussed a system and method in the field of health and safety. The patent was directed to a system and method for infectious disease surveillance. The method can comprise: determining, through a location determination sensor of a wearable computing device, wherein the device comprises input means arranged to receive disease information; acquiring, at cloud server, the determined current location and disease information from the device; generating, a map of disease outbreak spread based on received location information and the disease information, wherein the map comprises the geographical location of each wearable computing device; and transmitting, the generated map to wearable computing device or one or more computing devices.
[00010] The various approaches are available, still the farmers are not getting the major help.
[00011] However, the present invention will monitor all parameters to identify the disease on the same moment of time.
[00012] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the
definition of that term provided herein applies and the definition of that term in the reference does not apply.
[00013] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[00014] 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.
[00015] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.
[00016] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

Objects of the Invention

[00017] An object of the present disclosure is to overcome one or more drawbacks associated with conventional mechanisms.
[00018] An object of the present invention is to identify and stop the crop disease as early as possible.
[00019] An object of the present invention is to restraining crop disease and helps farmer to protect his crops.
[00020] An object of the present invention is to provide automatic crop disease detection system.
[00021] An object of the present disclosure is also helps in informing the national or regional organizations to get aware about the crop disease and how the disease is spreading.
[00022] An object of the present to provide a smart and faster way to identify the ratio of crop disease in the whole field.

Summary

[00023] The present invention relates to an advance system and method for crop disease detection system using cloud computing. More particularly, to a system and method for determining the crop disease information of a crop.
[00024] In an aspect, the present invention provides a system for monitoring the crop disease by capturing the crop image by the cameras and applying machine learning and image processing algorithms to detect the crop disease.
[00025] In another aspect, the present disclosure provides a method for informing the government and other private organizations to get aware of the disease spreading in the crops.
[00026] In an embodiment, the one or more diseases are selected on the sample images of the crop to check the efficiency.
Brief Description of the Drawings

[00027] FIG. 1 illustrates an exemplary architecture of a system for crop disease detection, in accordance with an embodiment of the present disclosure.
[00028] FIG. 2 illustrates exemplary functional instructions for crop disease detection, in accordance with embodiments of the present disclosure.

Detailed Description

[00029] The following discussion provides many example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed.
[00030] The present invention relates to a system and method for crop disease detection using cloud computing. More particularly, to a system and method for determining the crop information and how it identifies the crop disease using multiple algorithms.
[00031] FIG. 1 illustrates an exemplary architecture of a system 100 for crop field, in accordance with an embodiment of the present disclosure. The system 100 can be implemented to determine crop growing constraints and compliance by one or more crop samples available in 102 field boundaries. 104 represents the crops in the field which can comprise disease on the crop. 104a shows the diseased crop. The crop is monitored by the cameras 106 and the captured image is sent via network communication 108.
[00032] In an embodiment, the captured image of crop is stored by the cloud server 110. The cloud server takes all the data and monitors it using an algorithm to identify the image with crop diseases or like that.
[00033] Once the image is processed and disease identified, the data is stored on 112, the local repository. If the disease is found in the crop, the information will be shared with the governmental or other organizations 114.
[00034] FIG. 2 illustrates exemplary functional instructions 200 for crop disease detection using cloud computing system, in accordance with embodiments of the present disclosure. As illustrated, the functional steps comprise of the installation of high resolution cameras in the field 202. Module 204, the high-resolution cameras captured the image; and 206 perform the pre-processing on the captured crop image.
[00035] In an embodiment, the processing of the crop image of findings 208 is applied, which when executed receives data related to the crop gets stored on the cloud server 210. Checking for the disease is through the image captured from camera 206, and the disease has been identified 212.
[00036] Once the disease is identified, the alarming signal 214, is shown on the local dashboard, and the appropriate details send to the government or other organizations 216.
[00037] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or any other type of processing circuit.
[00038] In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K- Nearest Neighbours, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering
– Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
[00039] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00040] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-
exclusive manner, indicating that the referenced elements, components, or
steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

Advantages of the Invention

[00041] An advantage of the present invention is to overcome one or more drawbacks associated with conventional mechanisms.
[00042] An advantage of the present invention is to identify and stop the crop disease as early as possible.
[00043] An advantage of the present invention is to restraining crop disease and helps farmer to protect his crops.
[00044] An advantage of the present invention is to provide automatic crop disease detection system.
[00045] An advantage of the present invention is also helps in informing the national or regional organizations to get aware about the crop disease and how the disease is spreading.
[00046] An object of the present to provide a smart and faster way to identify the ratio of crop disease in the whole field.

Claims

We Claim:

1. A system for advance crop disease detection system using cloud computing comprising of:
A set of good quality cameras, Cloud server, Image detection and feature extraction application, and the database of crop diseases.
• The cameras will capture the images of crops on regular interval.
• The images are to be stored on the cloud server.
• The captured images will be sent to the image software and application to recognize the crop diseases and other abnormalities.
• The crop disease database to identify the name of disease based on its characteristics.

2. The system of claim 1, helps in identifying the crop disease without taking much time and without sending the sample of the disease to the national crop laboratory, which is a time-consuming process. This testing is through the algorithms used to fetch the identifiable marks and the shape of the leaf, or a crop, or its stem.

3. The camera images also check the abnormalities on then crop and any newly detected insect attack, or any disease.

4. The present system will store the records in the database, and also shares the information with government or semi-government organizations as per requirement. The alarming signal is shown on the screen to notify about the crop disease.