Open AccessDeep Learning in Agriculture: A Review
Author(s) -
Pallab Bharman,
Sabbir Ahmad Saad,
Sajib Khan,
Israt Jahan,
Milon Ray,
Milon Biswas
Publication year - 2022
Publication title -
asian journal of research in computer science
Language(s) - English
Resource type - Journals
ISSN - 2581-8260
DOI - 10.9734/ajrcos/2022/v13i230311
Subject(s) - categorization , agriculture , field (mathematics) , computer science , deep learning , artificial intelligence , precision agriculture , data science , machine learning , ranging , yield (engineering) , agricultural engineering , engineering , mathematics , geography , archaeology , pure mathematics , telecommunications , materials science , metallurgy
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.