A Guava Leaf Disease Identification Application

Identification of plant diseases and their early intervention has a great role to play in the horticulture industry. The traditional methods adopted by farmers’ may be time-consuming, costly, and occasionally wrong. With the advent of machine learning, it is now possible to address this issue with lightening speed. In this paper, we attempt to present the best approach suitable for detecting plant leaf diseases specific to guava (Psidium Guajava) tropical plant. We offer a suitable deep convolution neural network (CNN)-based method for diagnosing guava leaf illnesses in order to achieve the diagnosed output. The effectiveness of treatments depends on accurate disease diagnosis. The use of image processing in place of manual or visual detection of Guava leaf diseases alleviates the challenges, time commitment, and inaccuracies that would be encountered.