Nutrient Deficiency Detection and Yield Loss Prediction in Black Pepper Using U2Net and Ensemble of Shallow CNN and MobileNetV2

ABSTRACT Background Digital image analysis combined with deep learning offers powerful tools for detecting plant nutrient deficiency (ND), a critical challenge in precision agriculture. Aims This study aims to develop an ensemble transfer learning approach for ND detection in black pepper (BP) and evaluate its effectiveness for classification and yield‐loss (YL) prediction. Methods An ensemble of MobileNetV2 and a custom‐made shallow convolutional neural network was implemented, with U2Net‐based background removal to improve feature extraction. The model was validated on the BP Dataset (DS)—BPNutriDef03 (4469 images)—and tested on a rice DS (4399 images). The framework included (1) ND classification using leaf imagery analysis and (2) YL forecasting based on nutrient deficiency severity (NDS). Results The ensemble achieved classification accuracies of 99.22% for BP and 95.14% for rice. The yield prediction based on the NDS model estimated the YL of 27.83% for BP and 33.42% for rice. Conclusions The proposed approach demonstrates robust performance and generalizability, offering a scalable, automated decision‐support system for ND monitoring and yield prediction in precision crop management.