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المؤلفYang, Mengji
المؤلفLi, Daiwei
المؤلفChen, Minquan
المؤلفBouras, Abdelaziz
المؤلفTang, Yiqian
المؤلفYu, Xi
تاريخ الإتاحة2023-04-09T08:34:51Z
تاريخ النشر2020
اسم المنشور2020 3rd International Conference on Artificial Intelligence and Big Data, ICAIBD 2020
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/ICAIBD49809.2020.9137469
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41764
الملخصClassifying 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.
راعي المشروعThis 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعcrop disease
deep transfer learning
mobile app
ResNet 50
العنوانThe Implementation of A Crop Diseases APP Based on Deep Transfer Learning
النوعConference Paper
الصفحات22-28
dc.accessType Abstract Only


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