Albahar M. (2023). A survey on deep learning and its impact on agriculture: challenges and opportunities. Agriculture, 01/03/2023, vol. 13, n. 3, p. 1-22.
https://doi.org/10.3390/agriculture13030540
https://doi.org/10.3390/agriculture13030540
Titre : | A survey on deep learning and its impact on agriculture: challenges and opportunities (2023) |
Auteurs : | M. Albahar |
Type de document : | Article |
Dans : | Agriculture (vol. 13, n. 3, March 2023) |
Article en page(s) : | p. 1-22 |
Langues : | Anglais |
Langues du résumé : | Anglais |
Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.6 - Technique Agricole (sols, engrais, mécanisation)Thésaurus IAMM AGRICULTURE ; SYSTEME DE PRODUCTION ; NOUVELLE TECHNOLOGIE ; CONDUITE DE LA CULTURE ; MODE DE CULTURE |
Mots-clés: | APPRENTISSAGE PROFOND |
Résumé : | The objective of this study was to provide a comprehensive overview of the recent advancements in the use of deep learning (DL) in the agricultural sector. The author conducted a review of studies published between 2016 and 2022 to highlight the various applications of DL in agriculture, which include counting fruits, managing water, crop management, soil management, weed detection, seed classification, yield prediction, disease detection, and harvesting. The author found that DL’s ability to learn from large datasets has great promise for the transformation of the agriculture industry, but there are challenges, such as the difficulty of compiling datasets, the cost of computational power, and the shortage of DL experts. The author aimed to address these challenges by presenting his survey as a resource for future research and development regarding the use of DL in agriculture. |
Cote : | En ligne |
URL / DOI : | https://doi.org/10.3390/agriculture13030540 |