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

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

Mohamed Loey (Benha University, Benha, Egypt), Ahmed ElSawy (Benha University, Benha, Egypt) and Mohamed Afify (Benha University, Benha, Egypt)
DOI: 10.4018/IJSSMET.2020040103

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.
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Almost all previous reviews done on plant disease detection were covering traditional machine learning techniques. Recent developments in deep learning were also reviewed independently. Related reviews could be categorized into 1. overviews of deep learning techniques 2. reviews of using traditional machine learning and image processing techniques in plant disease detection 3. reviews of using deep learning in plant disease detection. The former category was only found in one survey paper that discussed using deep learning in agriculture with plant disease detection as one out of many other application fields. These efforts can be summarized in Table 1, and in this section the most representative publications in each class will be discussed.

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