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

The objective of this study was to provide a comprehensive overview the recent advancements in use deep learning (DL) agricultural sector. author conducted review studies published between 2016 and 2022 highlight various applications DL agriculture, which include counting fruits, managing water, crop management, soil weed detection, seed classification, yield prediction, disease harvesting. found that DL’s ability learn from large datasets has great promise for transformation agriculture industry, but there are challenges, such as difficulty compiling datasets, cost computational power, shortage experts. aimed address these challenges by presenting his survey resource future research development regarding agriculture.