A review of the use of convolutional neural networks in agriculture
Visualitza/Obre
Autor/a
Kamilaris, A.
Prenafeta-Boldú, F. X.
Data de publicació
2018-06-25ISSN
0021-8596
Resum
Deep learning (DL) constitutes a modern technique for image processing, with large potential. Having been successfully applied in various areas, it has recently also entered the domain of agriculture. In the current paper, a survey was conducted of research efforts that employ convolutional neural networks (CNN), which constitute a specific class of DL, applied to various agricultural and food production challenges. The paper examines agricultural problems under study, models employed, sources of data used and the overall precision achieved according to the performance metrics used by the authors. Convolutional neural networks are compared with other existing techniques, and the advantages and disadvantages of using CNN in agriculture are listed. Moreover, the future potential of this technique is discussed, together with the authors’ personal experiences after employing CNN to approximate a problem of identifying missing vegetation from a sugar cane plantation in Costa Rica. The overall findings indicate that CNN constitutes a promising technique with high performance in terms of precision and classification accuracy, outperforming existing commonly used image-processing techniques. However, the success of each CNN model is highly dependent on the quality of the data set used.
Tipus de document
Article
Versió del document
Versió acceptada
Llengua
English
Matèries (CDU)
63 - Agricultura. Silvicultura. Zootècnia. Caça. Pesca
Pàgines
29
Publicat per
Cambridge University Press
Publicat a
Journal of Agricultural Science
Citació
Kamilaris, A., and F. X. Prenafeta-Boldú. 2018. "A Review Of The Use Of Convolutional Neural Networks In Agriculture". The Journal Of Agricultural Science 156 (3): 312-322. Cambridge University Press (CUP). doi:10.1017/s0021859618000436.
Número de l'acord de la subvenció
EC/H2020/665919/EU/Opening Sphere UAB-CEI to PostDoctoral Fellows/P-SPHERE
Programa
Sostenibilitat en Biosistemes
Aquest element apareix en la col·lecció o col·leccions següent(s)
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