AI-Based Rice Plant Disease Detection and Treatment Recommendation System using EfficientNet-B0

Brown Spot, Leaf Blast and Hispa are among the diseases that most reduce the yield and quality of rice, a staple crop for much of the world. Detecting them early and correctly protects harvests. At present, diagnosis relies on farmers or agricultural scientists examining plants by eye, a slow and subjective process that does not scale and is often out of reach in remote areas. This paper presents a web-based system that uses EfficientNet-B0, a compact convolutional network, to classify a rice leaf photograph as Healthy, Brown Spot, Leaf Blast or Hispa and to report a confidence score. The model is trained on the RiceLeafs dataset from Kaggle. On a 20-image test set it reached 90% accuracy, with a response time of about 180 ms on a CPU. Based on the predicted class, the system retrieves symptoms, causes and contributing factors from a knowledge base, along with chemical, organic, traditional, fertiliser and preventive recommendations and short farmer tips. Prediction history is stored, and the interface serves both farmers and administrators. Because EfficientNet-B0 delivers strong accuracy at low computational cost, it suits deployment in this agricultural setting and supports timely decisions that reduce crop loss.