Intelligent System to Analysis of Plant Diseases using Machine Learning Techniques

For the efficient monitoring and control of crop health, the timely and precise diagnosis of plant diseases is essential. Traditional techniques of disease diagnosis rely on visual examination by human experts, which can be laborious, arbitrary, and liable to mistakes. Machine learning approaches have recently shown promise as intelligent and automated solutions for analysing plant diseases. In this paper, we offer an intelligent system for the investigation and identification of plant diseases that makes use of machine learning methods. The method uses a large library of plant photos that includes both healthy and diseased plants in various states of health. Pre-processing is done on the dataset to extract pertinent properties including colour, texture, and shape. In order to effectively identify and categorise the ailments of the plant and support decision-making in real time, a system was developed that makes use of a camera sensor module. The SVM), CNN, with a linear nucleus, and the Support Vector Machine with a polysomic nucleus are three machine learning techniques that are highlighted in the paper. The recommended intelligent system performs a very accurate and efficient analysis of plant diseases. By accepting photos of plant samples taken using different technologies, such as cell-phones or drones, it offers real-time disease detection. By effectively identifying and managing plant diseases, the system can be used in agricultural settings, enabling farmers and agricultural specialists to reduce crop losses and maximise agricultural productivity.