A Novel Two-Stage Deep Learning Framework for Plant Disease Detection

This paper presents a two-stage image classification scheme for detecting plant leaf diseases using transfer learning techniques. Their timely detection and correct diagnosis are very important in applying crop management techniques that ensure food security. This model did better compared to the traditional single-stage classification models, since it achieved a high accuracy of 96.82%.The two stages are outstanding in focusing learning at each stage to attain superior accuracy for hierarchical classification. The first stage classifies plant species, and the second stage further classifies the disease. This paper presents an accurate and much more efficient method to undertake plant disease detection; it makes a great deal of difference in early disease intervention and crop yield optimization for sustainable agriculture practices.