Multi-Stage Classification of Pomegranate Anthracnose Disease Severity Levels with CNN and SVM

This study applies a Convolutional Neural Network (CNN) model along with Support Vector Machines (SVM) to categorize the degrees of anthracnose disease severity. Various crops are susceptible to the fungal disease anthracnose, which results in severe damage and decreased yield. 88.93% overall accura...

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Bibliographic Details
Published in2023 4th International Conference on Smart Electronics and Communication (ICOSEC) pp. 1117 - 1121
Main Authors Thakur, Sahil, Malik, Dhruv, Kukreja, Vinay, Sharma, Rishabh, Yadav, Rishika, Joshi, Kireet
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
Agriculture
Anthracnose disease
Convolutional Neural Network
Convolutional neural networks
Crops
Electric potential
Machine learning
Neural networks
Pomegranate
Severity levels
Support vector machines
Task analysis
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Summary:This study applies a Convolutional Neural Network (CNN) model along with Support Vector Machines (SVM) to categorize the degrees of anthracnose disease severity. Various crops are susceptible to the fungal disease anthracnose, which results in severe damage and decreased yield. 88.93% overall accuracy was attained by the combined CNN-SVM model, according to the experimental results. The macro average values of precision, recall, and F1-scores for each class show satisfactory performance, and they are 53.27% on average. Overall accuracy across all classes is shown by the weighted average values to be 88.93%, and the same accuracy is also shown by the micro average values. These results demonstrate the CNN-SVM model's capability for various classification tasks and its accuracy in classifying the provided dataset. The research paper offers insightful information on the application of cutting-edge machine learning methods for identifying agricultural diseases, which can aid in the advancement of sustainable agricultural practices.
DOI:10.1109/ICOSEC58147.2023.10276047