AI Powered Crop Disease Detection System

Crop diseases can reduce agricultural productivity and cause financial losses when symptoms are not recognised early.Farmers may have limited access to timely expert diagnosis, while visual symptoms can be difficult to distinguish acrosscrops and growing conditions. This paper proposes an AI Powered Crop Disease Detection System that analyses images ofcrop leaves and provides a preliminary indication of possible diseases. The proposed workflow includes image acquisition,preprocessing, feature learning using a convolutional neural network (CNN) or a suitable transfer-learning model, diseaseclassification, and a user-facing result with basic preventive guidance. The system is intended to support farmers andagricultural advisers rather than replace laboratory testing or expert judgement. A prototype may use a curated, labelledimage dataset and a web or mobile interface for image submission. Evaluation should examine classification precision,recall, F1-score, robustness to lighting and background variation, and response time. No experimental performance resultsare claimed because the proposed system has not yet been benchmarked. The design emphasises reliable data, clearlimitations, and responsible use of recommendations