Optimizing Cotton Yield Forecasting With Deep Learning and Multispectral Remote Sensing

Precise forecasting of cotton yields is crucial for enhancing agricultural productivity and optimizing supply chain efficiency. Traditional forecasting techniques often inadequately account for intricate environmental interactions that influence crop development. This research utilizes ResNet-50, a deep Convolutional Neural Network (CNN), to examine multispectral remote sensing data for accurate yield estimates. The approach analyses high-resolution satellite images, extracting essential vegetation indicators and spatial patterns to improve forecasting precision. The system underwent training and validation using a dataset including 10,000 multispectral images representing diverse climatic and soil conditions. Experimental findings indicate that the ResNet-50 model achieved an accuracy of 98%, achieved by an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.96, indicating a robust connection between predicted and actual yield values. The Root Mean Square Error (RMSE) was reduced to 0.59 tons per acre, improving traditional regression-based models. A comparative examination using other CNN architectures revealed that ResNet-50 enhanced accuracy compared to traditional deep learning models. These results underscore the possibility of integrating deep learning with remote sensing for accurate yield prediction. The proposed approach improves data-driven decision-making for farmers and policymakers, facilitating sustainable precision agriculture and efficient resource management.