Publication

Abstract : Global potato production is seriously affected by the spread of potato plant diseases, which has far-reaching effects, including decreased crop yield, financial loss and food security challenges. Early detection and accurate diagnosis of these diseases are essential for enabling preventative measures. The proposed architecture combines deep learning and machine learning methods to create disease detection models. The experimental results show that the developed automated disease detection system has achieved an accuracy of 98.83% in identifying potato plant diseases and thus establishing its competence in providing accurate and reliable disease identification.