AI, Ml, and Deep Learning Models for Better Disease Detection in Lemon Plants

Abstract

Lemon plant illnesses must be promptly identified if the health and production of the crop are to be preserved. Traditional techniques of illness identification rely on professional visual inspection, which can be labor-intensive, arbitrary, and prone to mistakes. The use of automated and precise disease diagnosis in lemon trees is made possible by advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL). In this study, we explore the use of several AI, ML, and DL models for enhanced disease detection in lemon trees. The study evaluates the accuracy, precision, and recall of several models, such as random forests (RFs), convolutional neural networks (CNNs), and support vector machines (SVMs).The findings show that in terms of diagnosing and categorizing diseases of lemon plants, DL models, in particular CNNs, perform better than conventional ML models. Utilizing these cutting-edge methods can considerably improve the ability to identify diseases in crops, improving crop management procedures and raising agricultural yields.