An Investigation into Farming Suitability Prediction using Advanced ML Models

In recent years, there has been a surge of interest in utilizing machine learning (ML) techniques to tackle the challenge of predicting farming suitability. This research presents a novel approach to predicting farming suitability holistically, by employing a hybrid of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. LSTMs excel at extracting temporal sequences from climatic and soil data, thus ensuring complete prediction insights. On the other hand, CNNs excel at extracting spatial characteristics from satellite and landscape imagery. Our unified model was trained on data from a variety of environmental types and weather records spanning several agricultural regions over the years. The findings reveal that the combined CNN-LSTM model out- performed the individual ML models in predicting agricultural suitability, with greater accuracy and fewer prediction errors. Furthermore, the model exhibited adaptability in low-historical-data regions. The synergistic effect of CNN and LSTM not only provides new avenues for research in agricultural ML applications, but also demonstrates the potential of hybrid models. Considering the growing emphasis on sustainable agriculture and efficient land use, such predictive models will play a crucial role in ensuring food security and sustainable agricultural practices in the coming decades.