IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Deep Learning in Plant Diseases Detection for Agricultural Crops: A Survey

Deep Learning in Plant Diseases Detection for Agricultural Crops: A Survey
View Sample PDF
Author(s): Mohamed Loey (Benha University, Benha, Egypt), Ahmed ElSawy (Benha University, Benha, Egypt)and Mohamed Afify (Benha University, Benha, Egypt)
Copyright: 2020
Volume: 11
Issue: 2
Pages: 18
Source title: International Journal of Service Science, Management, Engineering, and Technology (IJSSMET)
Editor(s)-in-Chief: Ahmad Taher Azar (College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt)and Ghazy Assassa (Benha University, Egypt)
DOI: 10.4018/IJSSMET.2020040103

Purchase

View Deep Learning in Plant Diseases Detection for Agricultural Crops: A Survey on the publisher's website for pricing and purchasing information.

Abstract

Deep learning has brought a huge improvement in the area of machine learning in general and most particularly in computer vision. The advancements of deep learning have been applied to various domains leading to tremendous achievements in the areas of machine learning and computer vision. Only recent works have introduced applying deep learning to the field of using computers in agriculture. The need for food production and food plants is of utmost importance for human society to meet the growing demands of an increased population. Automatic plant disease detection using plant images was originally tackled using traditional machine learning and image processing approaches resulting in limited accuracy results and a limited scope. Using deep learning in plant disease detection made it possible to produce higher prediction accuracies as well as broadened the scope of detected diseases and plant species considered. This article presents a survey of research papers that presented the use of deep learning in plant disease detection, and analyzes them in terms of the dataset used, models employed, and overall performance achieved.

Related Content

Yuan Ren. © 2024. 8 pages.
Hadeel Al-Obaidy, Aysha Ebrahim, Ali Aljufairi, Ahmed Mero, Omar Eid. © 2024. 19 pages.
Anna M. Segooa, Billy M. Kalema. © 2024. 27 pages.
Muath AlShaikh, Waleed Alsemaih, Sultan Alamri, Qusai Ramadan. © 2024. 19 pages.
Jon A. Chilingerian, Mitchell P. V. Glavin. © 2024. 27 pages.
Osama R. S. Ramadan, Mohamed Yasin I. Afifi, Ahmed Yahya. © 2024. 19 pages.
Utsav Upadhyay, Alok Kumar, Gajanand Sharma, Ashok Kumar Saini, Varsha Arya, Akshat Gaurav, Kwok Tai Chui. © 2024. 30 pages.
Body Bottom