Crop Recommendation System Using Spatio - Temporal Attention Neural Networks

Crop recommendation systems enable farmers to make data-driven decisions in crop selection to maximize productivity in agriculture. This has been achieved through the adoption of deep learning and machine learning models, such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). However these models struggle to effectively capture temporal patterns and spatial dependencies in agricultural data, though, are difficult to represent by these models themselves, which resulted in the less-than-ideal recommendation accuracies with 92.04% by CNN and 91.36% by LSTM. This paper thus introduces a new approach towards improving crop recommendation accuracy and reliability by using Spatio-Temporal Attention Neural Networks (S-T ANN). The S-T ANN model integrates spatial and temporal attention mechanisms in a bid to capture all complex relationships in agricultural datasets efficiently and use them. Equipped with the advanced methods, S-T ANN offers deeper insights into temporal and environmental variables that influence crop growth. The accuracy of the recommendation presented by the suggested model was 94.52%, outperforming conventional models. This improvement now clearly points out how S-T ANN are capable of making accurate and useful recommendations on sustainable agricultural practices that ensure better crop yield management.