Deep Learning in Agriculture: A Review
Pallab Bharman *
Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
Sabbir Ahmad Saad
Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
Sajib Khan
Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
Israt Jahan
Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
Milon Ray
Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
Milon Biswas
Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
*Author to whom correspondence should be addressed.
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.
Keywords: Deep learning, machine learning, yield prediction, disease detection, weed detection