Improvisation in detection of pomegranate leaf disease using transfer learning techniques

Abstract

To provide the growing world population with food and satisfy their fundamental requirements, agriculture is a vital industry. The cultivation of cereals and vegetables is indispensable for both human sustenance and the worldwide economy. Many farmers in rural areas suffer substantial losses because they rely on manual monitoring of crops and lack sufficient information and disease detection methods. Digital farming techniques may provide a novel way to swiftly and simply identify illnesses in the leaves of plants. This article uses image processing and transfer learning techniques for identifying plant leaf ailments and taking preventative action in the agriculture business in order to address these problems. Global food security and agricultural productivity are seriously threatened by leaf disease. Crop losses may be considerably decreased, and crop output can be increased by promptly identifying and diagnosing leaf diseases. Deep learning can mitigate the adverse impact of artificially picking disease spot data, enhance objectivity in extracting plant disease traits, and expedite the advancement of new technologies. This article presents a novel approach using deep learning to diagnose leaf diseases. This article advances the development of efficient and successful techniques for recognizing and diagnosing leaf diseases, which will eventually aid farmers and maintain the security of the world's food supply.