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

Deep Learning in Plant Diseases Detection for Agricultural Crops: A Survey
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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 (Prince Sultan University, Riyadh, Kingdom of Saudi Arabi and Benha University, Egypt) and Ghazy Assassa (Benha University, Egypt)
DOI: 10.4018/IJSSMET.2020040103


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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.

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