Disease Detection in Apple Using Machine Learning

Abstract

Abstract: Crop production is a key component in the agricultural industry. The crop's quality directly affects the rate of output. The crop infection factor emerges as a crucial component of quality. Most of the time, diseases during cultivation are found using conventional methods such as Naked Eye Surveys, Surveys through Experts, etc. The processing time and cost of this process are enormous. High-quality output requires automatic severity detection. The project's goal is to provide a real-time, affordable method of identifying fruit (Apple) disease. Here, disease identification is accomplished via a machine learning-based method. The illness in an Apple is identified by image processing. The 'You Only Look Once'(YOLO) CNN-based algorithm is used to do feature extraction and classification. Based on the nature and degree of the sickness that is detected by this system, a comprehensive report is provided.