A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities

被引：41
作者：
Albahar, Marwan [1 ]
机构：
[1] Umm Al Qura Univ, Coll Comp Sci Al Leith, Mecca 21955, Saudi Arabia
来源：
AGRICULTURE-BASEL | 2023年 / 13卷 / 03期
关键词：
agriculture; deep learning; crop management; weed detection; NEURAL-NETWORKS; CLASSIFICATION; INFORMATION; CROP;
D O I：
10.3390/agriculture13030540
中图分类号：
S3 [农学（农艺学）];
学科分类号：
0901 ;
摘要：
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.
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页数：22
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