Abstract:
The tomato is one of the most important fruits on earth. It plays an important and useful role in the agricultural production of any country. An increase in tomato diseases causes an increase in the import of tomatoes, which affects the economy of the country. This research improved the dataset with an increase in images from the field (the Plant Village dataset) and proposed a hybrid algorithm composed of support vector machines (SVM) and histogram-oriented gradients (HOG) for real-time detection of late blight tomato disease. The use of the proposed hybrid algorithm in the smart agriculture field for the detection of late blight on tomatoes would increase and protect its production. To enhance the image dataset through the inclusion of early-affected and late-blight tomato leaves. To propose a HOG-based SVM model for early detection of late blight tomato leaf disease. To check the performance of the proposed model in terms of MSE, accuracy, precision, and recall as compared to Decision Tree and KNN. The integration of advanced technology in agriculture has the potential to revolutionize the industry, making it more efficient, sustainable, and profitable. This research work on the early detection of tomato diseases contributes to the growing importance of smart farming, the need for climate-smart agriculture, the rising need to more efficiently utilize natural resources, and the demand for higher crop yields. The proposed hybrid algorithm of SVM and HOG has significant potential for the early detection of late blight disease in tomato plants. The performance of the proposed model against decision tree and KNN algorithms and the results may assist in selecting the best algorithm for future applications. The research work can help farmers make data-driven decisions to optimize crop yield and quality while also reducing the environmental impact of farming practices
Page(s):
55-55
DOI:
DOI not available
Published:
Journal: Abstract Book on International Conference on Sustainable Agriculture and Food Security, August 27-31, 2023 , Volume: 0, Issue: 0, Year: 2023