Maximizing Accuracy in Detection and Delineation of Plant Pathology using Efficient Net

Incorrect diagnosis of the numerous illnesses affecting agricultural crops can result in chemical overuse, which can raise input costs, cause further outbreaks with substantial financial losses, and have an adverse effect on the environment. With the improvement in digital cameras along with the evolution of deep learning with machine learning has enabled an automated method to identify plant diseases. Modern agriculture requires precise disease identification methods which are currently highly sought professional identification of these diseases becomes possible through Deep learning algorithms help farmers make preemptive decisions by providing insights to them. Disease diagnosis conducted by human scouts proves to be both time consuming and expensive. Plants subjected to disease-related losses benefit from advanced protection methods developed in plant pathology. Plant pathology research has helped produce certified seed that is disease-free. Cultural practices can now be changed to prevent the majority of diseases with known disease cycles. Thus, in this research article diagnosis of disease and better accuracy is achieved.