Predictive Tomato Leaf Disease Detection and Classification: A Hybrid Deep Learning Framework

Tomatoes are a widely used crop in India. Its significance is beneficial to agriculture. Tomatoes are an essential food for humans. Many illnesses can have detrimental effects on a plant's health as well as inhibit its growth. Farmers fail to prevent damaged yields because they assess them too late. The development of an intelligent system with very effective plant disease detection capabilities has garnered increased attention recently. The goal of this study is to identify the most accurate and efficient algorithm by reviewing a range of existing approaches. This review also discusses the benefits and drawbacks of the recommended tactics. In this proposed work, a hybrid CNN and BiLstm model can classify the tomato leaf with 99% accuracy using the plant village dataset. The hybrid learning architecture disease-finding strategy that the review suggested for a tomato leaf ailment produced better results.