Multispectral Satellite Imagery and IMSS-CNN-YOLOv8: Intelligent Nutrient Analysis and Targeted Fertilizer Recommendations for Sustainable Rice Farming

Abstract

Nutrients play a fundamental role in crop development, and precise fertilizer application is essential for sustainable paddy cultivation and efficient resource utilization. However, traditional fertilizer management methods frequently neglect regional heterogeneity in nutrient demand, leading to inefficient nutrient use and increased environmental impact. To tackle these drawbacks, this study introduces a novel model combining Improved Multi-Scale Synchronous Convolutional Neural Network (IMSS-CNN) with YOLOv8, for intelligent nutrient monitoring and fertilizer recommendation in rice fields. The system utilizes satellite-acquired multispectral imagery to extract key vegetation indices, including NDVI (Normalized Difference Vegetation Index), GNDVI (Green NDVI), RVI (Ratio Vegetation Index), and GRVI (Green Ratio Vegetation Index), which are converted into pseudo-RGB composites for analysis. YOLOv8 is employed to detect paddy crop growth stages with high accuracy, enabling stage-specific nutrient assessment. The IMSS-CNN architecture captures deep spatial features to analyze crop health and stress, while the Botox Optimization Algorithm (BOA) further refines model performance and prediction reliability. Based on the predicted growth stage, the system calculates optimal nitrogen, phosphorus, and potassium (NPK) requirements for each field zone, providing targeted and timely fertilizer recommendations. The proposed model achieved an accuracy of 95.9%, outperforming existing approaches in both classification and nutrient estimation. This research offers a comprehensive solution to address the gaps in conventional practices by integrating satellite remote sensing, deep learning, and optimization techniques. The outcomes contribute significantly to precision agriculture by reducing fertilizer waste, enhancing nutrient uptake, increasing crop yield, and minimizing ecological harm.