Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/449354
Title: Design and Development of A Model to Classify Crop Foliar Diseases
Researcher: Naik, Akruti N
Guide(s): Thaker, Hetal R
Keywords: Computer Science
Computer Science and Image Processing
Computer Science Artificial Intelligence
Crop Disease
Decision Tree
Engineering and Technology
KNN
Logistic Regression
Moong Leaf
SVM
University: Atmiya University
Completed Date: 2022
Abstract: a controlled environment is a data item (image) that comprises only a single subject (leaf) and newlinea white background. In an uncontrolled environment, an image contains the Mung leaf, newlinebackground noise like stems, ground, other Mung leaves, etc. In combined environment images newlineof both the controlled and uncontrolled environments are merged together. Seven different newlineclassifiers namely Support Vector Machine (SVM), KNN (K Nearest Neighbor), AdaBoost newline(Adaptive Boosting), GaussianNB (Gaussian Naive Bayes), DTC (Decision Tree Classifier), newlineLogisticRegression and Custom CNNs with different architectures have been trained and newlinecompared to each other. newlineResearcher aims at detecting 3 mung leaf disease categories and a healthy leaf category. newlineThe model extracts complex features of various diseases. Early detection will help farmers to newlineimprove their productivity. The main objective was to automate Mung Leaf disease newlineidentification using advanced machine learning and deep learning approaches and image data. newlineAmong all the classifiers the custom CNN achieved performs well and achieved highest newlineaccuracy in all the three environments. Custom CNN achieves 99.24% of training and 95.05% newlineof testing accuracy in controlled environment. In uncontrolled environment custom CNN newlineachieves 99.69% training and 87.88% of testing accuracy. In combined environment custom newlineCNN achieves 98.81% of training and 90.68% of testing accuracy. The results shows high newlinepotentiality of machine vision for recognition of diseased leaves. An interface is developed newlinewhere user can input and image. Here are user can select image from either single leaf newline(controlled environment), photo captured from the field itself (uncontrolled environment). newlineImage given as input by interface will be given to model for classification whether it is healthy newlineor having disease and if it is affected by disease then which disease the leaf has i.e. amongst newlinethe three Cercospora Leaf Spot, Powdery Mildew, and Yellow Mosaic Virus. Interface is just a medium to interact with model, and model works as an
Pagination: 245
URI: http://hdl.handle.net/10603/449354
Appears in Departments:Computer Science

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01_title.pdfAttached File223.76 kBAdobe PDFView/Open
02_prelim pages.pdf1.3 MBAdobe PDFView/Open
03_content.pdf453.06 kBAdobe PDFView/Open
04_abstract.pdf368.02 kBAdobe PDFView/Open
05_chapter 1.pdf2.55 MBAdobe PDFView/Open
06_chapter 2.pdf696.7 kBAdobe PDFView/Open
07_chapter 3.pdf1.12 MBAdobe PDFView/Open
08_chapter 4.pdf6.79 MBAdobe PDFView/Open
09_chapter 5.pdf735.61 kBAdobe PDFView/Open
10_annexures.pdf6.36 MBAdobe PDFView/Open
80_recommendation.pdf453.88 kBAdobe PDFView/Open
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