Advancing Agricultural Crop Recognition: The Application of LSTM Networks and Spatial Generalization in Satellite Data Analysis

Abstract

This study addresses the challenge of accurate crop detection using satellite data, focusing on the application of Long Short-Term Memory (LSTM) networks. The research employs a “spatial generalization” approach, where a model trained on one geographic area is applied to another area with similar vegetation conditions during the growing season. LSTM networks, which are capable of learning long-term temporal dependencies, are used to overcome the limitations of traditional machine learning techniques. The results indicate that LSTM networks, although more computationally expensive, provide a more accurate solution for crop recognition compared with other methods such as Multilayer Perceptron (MLP) and Random Forest algorithms. The accuracy of LSTM networks was found to be 93.7%, which is significantly higher than the other methods. Furthermore, the study showed a high correlation between the real and model areas of arable land occupied by different crops in the municipalities of the study area. The main conclusion of this research is that LSTM networks, combined with a spatial generalization approach, hold great promise for future agricultural applications, providing a more efficient and accurate tool for crop recognition, even in the face of limited training data and complex environmental variables.