Smartphone-based paddy leaves SPAD value prediction from RGB contact imaging using ensemble deep learning model

Assessment of chlorophyll content is important to understand plant nitrogen status in precision agriculture. Traditional destructive methods for chlorophyll quantification are time-consuming, labor-intensive, and unsuitable for high-throughput phenotyping applications. The SPAD meter (Soil Plant Analysis Development) provides a rapid and non-destructive alternative by measuring leaf greenness as a proxy for chlorophyll content. Recent technological advances in imaging sensors and computational methods have enabled the development of low-cost approaches for predicting SPAD values. In this study, we propose an ensemble deep learning model-based Android application ( SPAD Predictor ) that was developed for predicting the SPAD value from RGB contact imaging. A total of 34 features, including color space features, RGB-derived features, and vegetation indices, were used to develop the model. The model consists of a lightweight Multi-Layer Perceptron (MLP) and Random Forest (RF) Regressor layer with stacking ensemble architecture. A linear regression was used as a meta-model to ensemble the MLP and RF layers. A permutation-based feature importance analysis showed that the a* channel, ExGR, RG, NRI and VARI indices played the most important roles in predicting SPAD value. The proposed ensemble deep learning model yielded R 2 t r a i n i n g of 0.987 and R 2 t e s t i n g of 0.89, RMSE of 3.25. The developed application was successfully deployed and was able to perform image submission, backend communication, prediction generation, result display, and history management. Field-level validation of the developed application yielded R 2 of 0.848, RMSE of 3.068, and MAE of 2.544. These findings indicate that the developed system has practical potential as a low-cost, field-applicable tool for estimating paddy leaf SPAD.