Integrating GeoAI and Deep Learning for Sustainable Agriculture: Sentinel-2 Based Crop Mapping and Yield Prediction for Selected Crops in Sokoto, Nigeria

Abstract

Sustainable agriculture, particularly in semi-arid areas, depends on precise crop mapping and yield forecasting. In this study, Random Forest (RF) classification and regression implemented in Google Earth Engine (GEE) are combined with GeoAI and machine learning techniques. It evaluates crop distribution and predicts yields in Sokoto State, Nigeria, using Sentinel-2 surface reflectance imagery. 3,167.57 hectares of cropland were analysed using 690 cloud-filtered Sentinel-2 scenes that were gathered between January and December of 2023.  Crop classification demonstrated good reliability in identifying rice, pepper, and onion crops with an overall accuracy of 99.7% and a Kappa coefficient of 0.82. With a coefficient of determination (R²) of 0.84 and a Pearson correlation of 0.92, the Random Forest yield model predicted crop yields, demonstrating a very significant positive link between observed and anticipated yields. With a little positive bias of +9,244 kg/ha, the model's root mean squared error (RMSE) and mean absolute error (MAE) were 10,870 kg/ha and 9,244 kg/ha, respectively.  According to yield figures, the average yield was 20,971 kg/ha, with a range of 7,000 to 40,000 kg/ha. These findings show how precise crop mapping and yield estimation can be achieved by combining GeoAI, multispectral satellite data, and machine-learning approaches. The results assist better decision-making, sustainable farming methods, and increased food security in semi-arid regions by providing agricultural scientists and policymakers with practical insights.