Integrating Remote Sensing and Deep Learning Techniques for Improved Crop Yield Estimation

Abstract

Accurate yield estimation is crucial for making knowledgeable choices about agriculture and food safety around the world. However, many current studies in this place face issues inclusive of neglecting soil properties as predictors, limited generalization throughout special regions, and confined use of climate variables. This study targets to fill those gaps by using the machine and deep learning and techniques to predict wheat yield in 36 districts of Punjab, Pakistan. Using a dataset created with Google Earth Engine, the dataset consists of detailed statistics on soil residences, climate and other relevant agricultural variables or flora indices. The study included an in-intensity evaluation of the dataset, followed by using training and assessment of various machine learning and deep learning techniques, which includes Artificial Neural Networks, RandomForest, GradientBoosting, and XGBoost.The findings revealed that the ANN model encountered problems together with overfitting because of limited data, while GradientBoosting and XGBoost excel in accuracy and robustness. Specifically, GradientBoosting gave an RMSE of 362.4946 and a MAE of one 101.2008 tons/district, whilst XGBoost gave an RMSE of 280.4313 and an MAE of 102.5128 tons/district. Both algorithms confirmed high R² values, indicating their effectiveness for predicting district-level productiveness. The performance of the models was evaluated with the aid of comparing their predictions with the ground reality information provided by using CRS of Punjab, with error metrics given in tons at district level. The insights gained from this study are very essential to farmers, agronomists and policy makers, allowing them to make informed decisions and promote sustainable agricultural practices. This research makes a big contribution to the sphere of yield estimation and highlights factors for future studies, such as expanding the dataset and exploring other modelling techniques including LSTM, to enhance protection of food and sustainable agriculture