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Research Article Open access CC BY 4.0

Deep Learning in Agriculture: A Review

Pallab Bharman, Sabbir Ahmad Saad, Sajib Khan, Israt Jahan, Milon Ray, Milon Biswas

Asian Journal of Research in Computer Science · pp. 28–47 · Published 15 Feb 2022

10.9734/ajrcos/2022/v13i230311

Abstract

Deep learning (DL) is a kind of sophisticated data analysis and image processing technology, with good results and great potential. DL has been applied to many different fields, and it is also being applied to the agricultural field. This paper presents a wide-ranging review of research with regards to how DL is applied to agriculture. The analyzed works were categorized in yield prediction, weed detection, and disease detection. The articles presented here illustrate the benefits of DL to agriculture through filtering and categorization. Farm management systems are turning into real-time AI-enabled applications that give in-depth insights and suggestions for farmer's decision support by using the proper utilization of DL and sensor data.

Deep learning machine learning yield prediction disease detection weed detection

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