Exploring Crop Yield Prediction with Remote Sensing Imagery and AI

Planning for agriculture, allocating resources, and ensuring food security all depend on the accurate and timely estimate of the crop yield. This paper investigates the use of Machine Learning (ML) and Deep Learning methods (DL) to forecast crop yield using remote sensing images. Multispectral and hyperspectral images from remote sensing provide a useful source of data for tracking crop health and estimating prospective yield. This study scrutinizes the advantage of the abundant spectral and spatial data that remote sensing sensors have collected. Convolutional neural networks (CNNs) and Machine learning are utilized for feature extraction and prediction, leveraging the hierarchical representations acquired from remote sensing data. A dataset of ground-truth yield data and historical satellite photos are used to train the DL and ML algorithms. In order to improve the model’s generalization to various geographic locations and cropping seasons, transfer learning techniques can also be used. Moreover, interpretable deep learning methods and attention mechanisms are investigated to improve the model’s transparency and reveal key elements that influence yield prediction. By giving farmers, decision-makers, and other stakeholders timely and precise information on crop output estimates, the research’s findings have the potential to greatly advance precision agriculture methods. Deep learning methods combined with satellite imagery show approximately $\mathbf{9 0 \%}$ accuracy for scalable and effective agricultural production monitoring, which will ultimately help sustainable farming methods.