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\n  \n 2022\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches.\n \n \n \n\n\n \n Raki, H.; González-Vergara, J.; Aalaila, Y.; Elhamdi, M.; Bamansour, S.; Guachi-Guachi, L.; and Peluffo-Ordoñez, D.\n\n\n \n\n\n\n Volume 1643 CCIS 2022.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@book{\n title = {Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches},\n type = {book},\n year = {2022},\n source = {Communications in Computer and Information Science},\n keywords = {Convolutional neural networks,Deep learning,Smart farming},\n volume = {1643 CCIS},\n id = {00db5c55-3459-3cc0-88a9-a9123168643a},\n created = {2023-01-27T11:35:14.242Z},\n file_attached = {false},\n profile_id = {8dfcede1-2f4c-3600-8b67-8b0c1f334b84},\n last_modified = {2023-01-27T11:35:14.242Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {Automatic crop classification using new technologies is recognized as one of the most important assets in today’s smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).},\n bibtype = {book},\n author = {Raki, H. and González-Vergara, J. and Aalaila, Y. and Elhamdi, M. and Bamansour, S. and Guachi-Guachi, L. and Peluffo-Ordoñez, D.H.},\n doi = {10.1007/978-3-031-19647-8_3}\n}
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\n Automatic crop classification using new technologies is recognized as one of the most important assets in today’s smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).\n
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