Influencing factors analysis in pear disease recognition using deep learning
- PDF / 1,448,893 Bytes
- 13 Pages / 595.276 x 790.866 pts Page_size
- 15 Downloads / 241 Views
Influencing factors analysis in pear disease recognition using deep learning Fang Yang 1 & Fuzhong Li 1 & Kai Zhang 1 & Wuping Zhang 1 & Shancang Li 2 Received: 6 August 2020 / Accepted: 23 November 2020 # The Author(s) 2020
Abstract Influencing factors analysis plays an important role in plant disease identification. This paper explores the key influencing factors and severity recognition of pear diseases using deep learning based on our established pear disease database (PDD2018), which contains 4944 pieces of diseased leaves. Using the deep learning neural networks, including VGG16, Inception V3, ResNet50 and ResNet101, we developed a “DL network + resolution” scheme that can be used in influencing factors analysis and diseases recognition at six different levels. The experimental results demonstrated that the resolution is directly proportional to disease recognition accuracy and training time and the recognition accuracies for pear diseases are up to 99.44%,98.43%, and 97.67% for Septoria piricola (SP), Alternaria alternate (AA), and Gymnosporangium haracannum (GYM), respectively. The results also shown that a forward suggestion on disease sample collection can significantly reduce the false recognition accuracy. Keywords Pear diseases dataset . Disease recognition . Deep learning (DL) . Transfer learning . Resolution
1 Introduction Pear cultivation has a history of more than 3000 years in China, and it is planting in a large area covering 80% of area in the world [1]. Pear diseases and their contagiousness can significantly affect the normal growth of pear trees. As a result, scientific diagnosis measures are of crucial important to avoid misuse of prescriptions, excessive application of pesticides, pesticide residues, can cause significant reduction of yield of pear etc. Also, it can increase the cost of disease prevention and control and reduction of economic benefits together with the enthusiasm of farmers. Even worse, it can cause food safety problems [1]. With the concerns of experts in agricultural technology promotion and the increasingly labor This article is part of the Topical Collection: Special Issue on P2P Computing for Deep Learning Guest Editors: Ying Li, R.K. Shyamasundar, Yuyu Yin, Mohammad S. Obaidat * Fuzhong Li [email protected] * Shancang Li [email protected] 1
School of Software, Shanxi Agricultural University, Jinzhong 030801, Shanxi, China
2
Department of Computer Science, University of the West of England, Bristol BS16 1QY, UK
costs, it is difficult and expensive for professionals to help farmers diagnose diseases and there are urgent demands for the disease automatic recognition technology that could detect, identify and possibly cure the diseases of pear trees. Plant diseases recognition based on leaf lesions images is a promising method that has been widely studied and successfully applied for fruits, vegetables, and crops [2–4]. It is lowcost, simple, and convenient way compare to molecules, volatile organic compounds, and spectrum methods. At present, there are mainl
Data Loading...
