Detecting and Monitoring Agricultural Lands in Sri Lanka Using Remote-Sensing and Deep Learning

Abstract

Accurate agricultural assessment is essential for improving productivity and ensuring food security in regions like Sri Lanka. This study presents an integrated framework using remote sensing and deep learning to enhance precision agriculture. Multispectral satellite imagery from Sentinel-2 and Landsat, processed via Google Earth Engine (GEE), supports a soft-label Convolutional Neural Network (CNN) for identifying crop types and phenological stages. Geospatial techniques enable precise area mapping and vegetation health evaluation, while Long Short-Term Memory (LSTM) networks model temporal growth dynamics for yield forecasting. The system's web interface provides real-time analytics, interactive maps, and automated alerts on crop performance and underutilized lands, enabling timely, data-driven decisions. This approach offers a scalable, technology-driven pathway for sustainable land management and improved agricultural resilience in Sri Lanka.