Machine learning system for accurate and reliable detection for plant diseases

Abstract

Crop pests and diseases present substantial threats to global farming, affecting both yield and food security. To overcome these problems, a capable machine learning (ML) system that is specifically designed to detect crop diseases with high precision and dependability is essential. By implementing machine-learning algorithms with deep-learning models, our system can analyze crop images and deliver accurate detection results. In pre-processing, the model uses advanced image processing techniques to collect required features from plant images, such as color, texture, and shape characteristics. The obtained features are then used to train an efficient machine-learning model on a vast dataset of different diseased plant images. The system is intended to precisely identify the overall condition of plants by analyzing the patterns that have been acquired from the image data. Additionally, it will offer clear explanations for its predictions. The system’s efficiency was verified through an in-depth testing process, and the results overwhelmingly show its high precision in comparison with traditional methods. This system will be very effective for farmers and agricultural professionals to make better decisions and successfully manage plant health.