Crop diseases are one of the major factors affecting crop yields and quality, causing significant economic losses to farmers and threatening food security. Detecting crop diseases at an early stage is crucial for timely and effective control measures. In recent years, the use of image processing and machine learning techniques for crop disease detection has gained increasing attention due to their high accuracy and efficiency.
MATLAB, a popular programming language for scientific computing, provides powerful tools and functions for image processing and machine learning. These tools can be used to extract features from crop images, classify them using machine learning algorithms, and detect the presence of diseases. The application of MATLAB in crop disease detection has the potential to revolutionize the agricultural industry by providing an efficient and cost-effective method for disease monitoring and control. This project explores the various techniques and algorithms used for crop disease detection using MATLAB and their potential impact on the agricultural sector.