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AuthorYang, Mengji
AuthorLi, Daiwei
AuthorChen, Minquan
AuthorBouras, Abdelaziz
AuthorTang, Yiqian
AuthorYu, Xi
Available date2023-04-09T08:34:51Z
Publication Date2020
Publication Name2020 3rd International Conference on Artificial Intelligence and Big Data, ICAIBD 2020
ResourceScopus
URIhttp://dx.doi.org/10.1109/ICAIBD49809.2020.9137469
URIhttp://hdl.handle.net/10576/41764
AbstractClassifying the severity of crop diseases is the staple-basic element of the plant pathology for making disease prevent and control strategies. The diagnosis of disease needs timeliness and accuracy. Thanks to the development and popularity of smart phones and mobile networks, this makes possibly to develop mobile applications that can be widely accepted by users in the agricultural community. This paper provides a system that can detect the severity of crop diseases automatically and intelligently through taking photos. The development of this mobile app is based on deep transfer learning that we proposed an improved method with nearly 92% accuracy based on ResNet 50. The significantly high success rate makes the model a very useful advisory or warning tool. This project provides a new idea and solution for the detection of crop diseases in agriculture. 2020 IEEE.
SponsorThis research grateful for ‘AI Challenger 2018’ providing the dataset of crop diseases. It is supported by the open fund (MSSB-2019-02) of Key Laboratory of Pattern Recognition and Intelligent Information Processing, Chengdu University, China and Erasmus+ SHYFTE project (598649-EPP-1-2018-1-FR-EPPKA2-CBHE-JP) which funded with support from the European Commission.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectcrop disease
deep transfer learning
mobile app
ResNet 50
TitleThe Implementation of A Crop Diseases APP Based on Deep Transfer Learning
TypeConference Paper
Pagination22-28
dc.accessType Abstract Only


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