Projection of Plant Leaf Disease Using Support Vector Machine Algorithm

These worries may be lessened with the use of an automated system. The three most prevalent ailments of turmeric leaves-Leaf blast, Bacterial blight, and Brown spot-are suggested to be automatically diagnosed in this study. Depending on the severity of the sickness, prescriptions for fertilizers and...

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Bibliographic Details
Published in2023 International Conference on Recent Advances in Science and Engineering Technology (ICRASET) pp. 1 - 6
Main Authors S, Senthil Pandi, P, Kumar, T A, Salman Latheef
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.11.2023
Subjects
Crops
Feature extraction
Image color analysis
K-Means
Prediction algorithms
Random Forest
Segmentation
Support vector machines
Visualization
Web servers
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Summary:These worries may be lessened with the use of an automated system. The three most prevalent ailments of turmeric leaves-Leaf blast, Bacterial blight, and Brown spot-are suggested to be automatically diagnosed in this study. Depending on the severity of the sickness, prescriptions for fertilizers and/or insecticides may also be given. K-means clustering is used to separate the damaged area from the image of the turmeric leaf. Color, texture, and form are employed in visual material to differentiate between various illnesses. Using a Support Vector Machine (SVM) classifier, the type of illness affecting turmeric leaves is determined. After identification, it is recommended to implement a prophylactic measure that can help individuals and agricultural groups respond appropriately to these diseases. The accuracy, specificity, and similarity criteria have been used to assess the performance of the experimental decision tree technique findings on turmeric pictures. The suggested approach performs better than the current back propagation method, according to the experimental data.
DOI:10.1109/ICRASET59632.2023.10419981