Tomato Leaf Disease Detection using Feature Extraction and Machine Learning Techniques

Tomatoes, as one of the most significant fruits, play an important role in Indian cuisine due to their nutritional value and other elements such as antioxidants. With a good climate and appropriate rainfall, the tomato plant generates the highest output, and India is second only to China in tomato production. Tomatoes are recognized as both a commercial and a nutritious crop that contributes to the nation's economy. Though tomatoes can be cultivated all year, obstacles such as heavy rains, temperature swings, fertilizer levels, and moisture stages cause production issues. Diseases caused by fungi, bacteria, and viruses damage the plant and ruin the fruit, causing farmers to lose a lot of money. The infections are detectable in the early stage is avoided by the appearance of the leaves, but it is difficult for human vision. As a result, computer-aided processes are used to identify normal and a typical plant using Machine and deep learning algorithms. This research proposes easy approaches for detecting illnesses in tomato plants by classifying leaves to determine whether it belong to a healthy plant. To extract the best attributes for categorization, feature extraction methods are used. For classification, Random Forest outperforms all other models with 86% accuracy.