Lightweight Multi-View Fusion Network for Non-Destructive Chlorophyll and Nitrogen Content Estimation in Tea Leaves Using Front and Back RGB Images

Accurate estimation of chlorophyll and nitrogen content in tea leaves is essential for effective nutrient management. This study introduces a proof-of-concept dual-view RGB regression framework developed under controlled scanner conditions. Paired adaxial and abaxial images of Yun Kang 10 tea leaves were collected from four villages in Lincang, Yunnan, alongside corresponding soil and plant analyzer development (SPAD) and nitrogen measurements. A lightweight dual-input CoAtNet backbone with streamlined Bneck modules was designed, and three fusion strategies, Pre-fusion, Mid-fusion, and Late-fusion, were systematically compared. Ten-fold cross-validation revealed that Mid-fusion delivered the best performance (R2 = 94.19% ± 1.75%, root mean square error (RMSE) = 3.84 ± 0.65, MAE = 3.00 ± 0.45) with only 1.92 M parameters, outperforming both the single-view baseline and other compact models. Transferability was further validated on a combined smartphone–scanner dataset, where the framework maintained robust accuracy. Overall, these findings demonstrate a compact and effective system for non-destructive biochemical trait estimation, providing a strong foundation for future adaptation to field conditions and broader crop applications.