AgriSense AI: An Integrated Dual-Engine Deep Transfer Learning and Ensemble Regression Framework for Crop Foliar Disease Diagnosis and Agricultural Commodity Price Forecasting

Abstract— Agricultural productivity and rural economic stability in developing agrarian economies face dual systemic vulnerabilities: unmitigated phytopathological outbreaks and severe market price volatility. This paper presents AgriSense AI, an end-to-end, dual-engine decision-support framework that integrates convolutional transfer learning for automated foliar disease diagnosis with non-linear ensemble regression for regional commodity price forecasting. A computer vision pipeline is constructed over a 15-class subset of the PlantVillage dataset using FastAI and a pre-trained ResNet-18 architecture (128x128 spatial normalization), achieving a validation accuracy of 99.95% (loss: 0.001528) and a macro-averaged Precision, Recall, and F1-Score of 0.9996. Second, a leakage-free econometric pricing engine is developed over Indian agricultural market records (N = 2,794 across 17 encoded features), where a 100-estimator Random Forest Regressor achieves an out-of-sample RMSE of 2,659.49 INR, MAE of 1,450.25 INR, and R2 of 0.7200. Both serialized engines are unified within a production Streamlit web application. Аннотация— В данной работе представлена система AgriSense AI — комплексная двухмодульная интеллектуальная платформа поддержки принятия решений, объединяющая глубокое трансферное обучение (ResNet-18, FastAI) для автоматизированной диагностики 15 классов болезней листьев (точность 99,95%, F1-мера 0,9996) и ансамблевую регрессию случайного леса (Random Forest Regressor, 100 деревьев, R2 = 0,7200, RMSE = 2 659,49 INR, MAE = 1 450,25 INR) для прогнозирования региональных цен на сельскохозяйственные товары без утечки целевых данных.