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

Artur Gafurov (), Svetlana Mukharamova, Anatoly Saveliev and Oleg Yermolaev
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Artur Gafurov: Institute of Environmental Sciences, Kazan Federal University, 420008 Kazan, Russia
Svetlana Mukharamova: Institute of Environmental Sciences, Kazan Federal University, 420008 Kazan, Russia
Anatoly Saveliev: Institute of Environmental Sciences, Kazan Federal University, 420008 Kazan, Russia
Oleg Yermolaev: Institute of Environmental Sciences, Kazan Federal University, 420008 Kazan, Russia

Agriculture, 2023, vol. 13, issue 9, 1-23

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.

Keywords: remote sensing; MODIS; classification; crops; random forest; multilayer perceptron; long short-term memory (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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