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.

Plant phenotyping relevance

植物の葉画像から栄養欠乏状態を推定する画像解析・深層学習手法を開発し、複数データセットで検証しているため、植物フェノタイピング手法が中心である。

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