Deep Learning-Based Approach for Plant Disease Classification

Sustainable intensification in Indian agriculture aims to improve agricultural productivity without harming natural resources or the environment. Plant diseases that harm the leaves of plants halt the development of each species. Pre-emptive detection and accurate classification of diseases are critical for impactful rehabilitation of many conditions which helps to ensure crop prevention and the future spread of disease. Reducing plant illnesses, boosting plant health, and increasing food crop yield are all greatly aided by early and precise investigation and diagnosis of plant disease. There is a need for automated cost-effective, accessible, and reliable methods to diagnose botanical disorder without the need for laboratory examination and expert opinion since plant disease specialists are not readily available in rural places. In this study, we describe a thorough technique for diagnosing plant illnesses in potato leaves using convolutional neural networks and compare it to competing algorithms. The proposed methodology leverages the power of deep learning and traditional machine learning techniques to achieve accurate and reliable disease classification. Our study contributes to the advancement of plant pathology and can aid in the prompt detection and management of phytopathology, thereby improving agricultural productivity and food security.