Plant Disease Classification in Segmented Images Using Computer Vision

Abstract

Agriculture productivity has a significant impact on the lives of people and economies because of the growing human population. In agriculture, plant diseases are a big problem since they result in severe crop losses and financial hardship for farmers. Traditional disease detection and categorization methods take a long time and are subjective, so automated and effective methods are required. Computer vision techniques have recently shown promise as tools for classifying plant diseases. To provide a precise and dependable system for disease detection and management, this article gives a thorough study on computer vision approaches for plant disease categorization. The research uses a variety of approaches, such as feature extraction, image pre-processing, and machine learning algorithms. Benchmark datasets are used for comparative study and performance evaluation of various methods. The outcomes show how effective computer vision techniques are at precisely diagnosing and categorising plant diseases.