Affiliations: [a] National School of Computer Science (ENSI), University of Manouba, Manouba, Tunisia | [b] Faculty of Sciences, University of Gafsa, Gafsa, Tunisia | [c] Faculty of Sciences, University of Sfax, Sfax, Tunisia
Correspondence:
[*]
Corresponding author: Mohamed Elleuch, %****␣his-17-his210002_temp.tex␣Line␣25␣**** National School of Computer Science (ENSI), University of Manouba, Manouba, Tunisia. E-mail: elleuch.mohameds@gmail.com.
Abstract: The main problem in agriculture is the attack of diseases on the leaves of plants and the spread of agricultural pests. For this reason, we will present how to treat certain phenomena of disease in plants, or how to prevent and do the precautionary measures to adopt a modern method to diagnose the deficiency of the leaves elements of the diseased plants. Thus, the deep learning is the most appropriate solution to detect the properties of the leaves and is essential in the tracking of large fields of crops as well as automatically detecting the symptoms of the leaves characteristics as soon as they appear on the plants leaves. In this paper, we clarified the Transfer Learning (TL) architecture for VGG-16 and the other architecture like ResNet to detect plants that suffer from diseases in the sheet due to a lack of ingredient using a set of increased data based on the leaves of healthy and unhealthy plants alike. The experimental results show that significant detection accuracy improvement has been achieved thanks to our proposed model compared to other reported methods.
Keywords: Lack of elements, data augmentation, VGG-16, transfer learning, ResNet
