Agricultural Land Suitability Classification and Crop Suggestion using LSTM-GBRT Analysis in Semi-arid Ecosystem

Land suitability assessments are essential for future agricultural cropping patterns and activity planning and decision-making. An investigation of a piece of land's suitability for a particular use, such as the cultivation of a certain crop, is known as a land suitability analysis. Land suitability tools have been utilized extensively to discover improved methods of agricultural region management. In the proposed model, preprocessing, feature selection, and model training are the three main components. Since the deep learning method isn't designed to deal with incomplete, inconsistent, or noisy data, preprocessing is necessary for the farming suggestion model's dataset. The feature selection process uses CFS and Minimum-Redundancy-Maximum-Relevance, with CFS yielding the best results in terms of accuracy. A LSTM-GBRT was used to train the model. Their proposed method outperforms LSTM and GBRT. After using the method, the precision went up by 94.62 percent.