Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

Igor Teixeira (), Raul Morais, Joaquim J. Sousa and António Cunha
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Igor Teixeira: Engineering Department, School of Science and Technology, UTAD—University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Raul Morais: Engineering Department, School of Science and Technology, UTAD—University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Joaquim J. Sousa: Engineering Department, School of Science and Technology, UTAD—University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
António Cunha: Engineering Department, School of Science and Technology, UTAD—University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal

Agriculture, 2023, vol. 13, issue 5, 1-24

Abstract: In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

Keywords: deep learning; crop; image classification; aerial imagery (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 references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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