Remote Sensing in North African Agriculture: Monitoring Crop Health and Yield Potential

Remote sensing technology has been increasingly adopted in agriculture to monitor crop health and yield potential from a distance. The review synthesizes existing literature on remote sensing applications in agriculture, focusing on methodologies used for crop health assessment and yield prediction. Recent studies have demonstrated that incorporating satellite imagery with machine learning models can achieve an accuracy of up to 85% in predicting crop yields across different geographical regions. Remote sensing offers a robust toolset for precision agriculture, enabling farmers and policymakers to make informed decisions regarding water management, fertilization, and pest control strategies. Investment in remote sensing infrastructure should be prioritised to enhance the effectiveness of these monitoring systems. Additionally, further research is needed to refine models across diverse agricultural environments. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.