Precision agriculture enables data-driven crop monitoring and improved resource utilization. Paddy cultivation requires continuous surveillance and timely fertilizer application because it is sensitive to soil nutrient dynamics, water availability, and climatic conditions. Conventional practices such as manual field inspection and heuristic fertilizer advisory methods are often labor-intensive and subjective, which can reduce decision consistency and contribute to yield variability. To address these limitations, this study proposes a Selfdom Enhanced CatBoost (SECB) framework for remote paddy growth-stage monitoring and fertilizer recommendation. Multispectral remote sensing data collected over multiple seasons are used to compute vegetation indices, including NDVI, GNDVI, RVI, GRVI, and NDRE, to characterize crop vigor and chlorophyll-related variation across growth stages. The proposed SECB improves CatBoost by integrating an Improved Osprey Optimization Algorithm (IOOA) to tune key model parameters, aiming to enhance feature interaction learning and reduce overfitting. In addition, oppositional function-based initialization is applied to improve the exploration capability of IOOA and accelerate convergence. Experimental results show that SECB achieves improved performance over baseline classifiers in terms of accuracy, precision, F1-score, specificity, and AUC. The proposed approach provides reliable growth-stage identification and supports fertilizer recommendations to promote efficient nutrient usage and improved productivity. Overall, the framework offers an automated and scalable decision-support strategy for paddy crop management.
S et al. (2026) studied this question.