Authors: Dhole Siddharth Dnyaneshwar, Prof. N. G. Rokade, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Plant diseases, unpredictable weather and soil problems cause major yield losses every year, and farmers in many regions still depend on visual inspection or delayed expert visits to identify diseases. Late detection often leads to excessive use of chemical sprays, which raises costs and harms the environment. This paper reviews AI-powered systems that detect crop diseases early from leaf images and provide smart recommendations for treatment and crop management. The review examines image datasets such as PlantVillage and PlantDoc, convolutional neural networks and transfer learning for disease classification, object detection models for locating lesions in field images, and lightweight networks for offline smartphone use. It also covers how detection results can be combined with weather, soil and crop-stage information to recommend targeted, dose-appropriate interventions and preventive practices. A five-layer architecture comprising data acquisition, preprocessing, a detection engine, a recommendation engine and a farmer-facing application is proposed. The paper identifies research gaps such as the drop in accuracy from laboratory to field images, limited regional crop coverage and the lack of explainable advice, and it concludes that a multilingual, mobile-first assistant combining disease detection with context-aware recommendations can reduce losses and chemical overuse.
DOI: https://doi.org/10.5281/zenodo.24220185
