Employing a Convolutional Neural Network Technique to Identify Plant Diseases through Machine Learning

Abstract

The agriculture industry is essential to both economic growth and food security. However, farmers face enormous obstacles as a result of the antiquated and ineffective management of plant diseases in agriculture, particularly in nations like Bangladesh. We provide a novel approach to resolve this problem by utilizing a Convolutional Neural Network (CNN) technology to recognize plant diseases using machine learning. Our suggested CNN model has extraordinary capability in precisely identifying and diagnosing plant illnesses from visual signs. We offer fast and accurate information to farmers for efficient disease control through the integration of machine learning algorithms. In order to improve model performance, the study technique entails gathering a varied collection of plant photos that includes images of various illnesses. The outcomes show how well our suggested strategy works to help farmers. We allow proactive actions like targeted treatments and preventative tactics by providing customers with an AI-based system for identifying plant diseases, thereby decreasing crop losses and maintaining the healthy growth of vegetable crops. The adoption of our suggested remedy would also have wider socioeconomic effects. By raising agricultural production, we support greater food security, higher farmer incomes, and the development of a resilient agricultural environment. Furthermore, the use of cutting-edge AI technology has the potential to inspire younger generations to pursue careers in agriculture, rejuvenating the industry with new ideas and viewpoints. With an attained accuracy of over 74.50% on the testing set, the results of our trials show a promising degree of accuracy. This shows how well the suggested CNN model performs at correctly recognizing and categorizing plant diseases. The model's capacity to identify distinguishing traits from plant photos and provide precise predictions is credited with the high accuracy.