Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Identification of Tomato Leaf Diseases based on a Deep Neuro-fuzzy Network
https://orcid.org/http://orcid.org/0000-0002-4020-6906
The emergence and spread of diseases can reduce the yield of tomato crops, resulting in lower income for farmers. Accurate identification of tomato leaf diseases is an urgent matter for control and treatment. The recognition accuracy has improved with the advancement of deep learning. But because of uncertainty and ambiguity of information, the fuzzy rules, which can describe and process the fuzzy information, are incorporated into deep learning to increase the identification accuracy. In this paper, we adopt a deep neuro-fuzzy neural network to classify tomato leaf diseases. To extract complex features, we adopt the fuzzy inference layer and fuzzy pooling layer in the neuro-fuzzy network. And then input these into the fully connected layer for classification. Based on a big dataset containing 8 kinds of infected and uninfected tomato leaf images, the applied model achieved recognition accuracy of 94.19%. And three evaluation indexes were used to measure the performance. The experimental results prove the advantage of the deep neuro-fuzzy neural network in tomato diseases.
Identification of Tomato Leaf Diseases based on a Deep Neuro-fuzzy Network
https://orcid.org/http://orcid.org/0000-0002-4020-6906
The emergence and spread of diseases can reduce the yield of tomato crops, resulting in lower income for farmers. Accurate identification of tomato leaf diseases is an urgent matter for control and treatment. The recognition accuracy has improved with the advancement of deep learning. But because of uncertainty and ambiguity of information, the fuzzy rules, which can describe and process the fuzzy information, are incorporated into deep learning to increase the identification accuracy. In this paper, we adopt a deep neuro-fuzzy neural network to classify tomato leaf diseases. To extract complex features, we adopt the fuzzy inference layer and fuzzy pooling layer in the neuro-fuzzy network. And then input these into the fully connected layer for classification. Based on a big dataset containing 8 kinds of infected and uninfected tomato leaf images, the applied model achieved recognition accuracy of 94.19%. And three evaluation indexes were used to measure the performance. The experimental results prove the advantage of the deep neuro-fuzzy neural network in tomato diseases.
Identification of Tomato Leaf Diseases based on a Deep Neuro-fuzzy Network
https://orcid.org/http://orcid.org/0000-0002-4020-6906
The emergence and spread of diseases can reduce the yield of tomato crops, resulting in lower income for farmers. Accurate identification of tomato leaf diseases is an urgent matter for control and treatment. The recognition accuracy has improved with the advancement of deep learning. But because of uncertainty and ambiguity of information, the fuzzy rules, which can describe and process the fuzzy information, are incorporated into deep learning to increase the identification accuracy. In this paper, we adopt a deep neuro-fuzzy neural network to classify tomato leaf diseases. To extract complex features, we adopt the fuzzy inference layer and fuzzy pooling layer in the neuro-fuzzy network. And then input these into the fully connected layer for classification. Based on a big dataset containing 8 kinds of infected and uninfected tomato leaf images, the applied model achieved recognition accuracy of 94.19%. And three evaluation indexes were used to measure the performance. The experimental results prove the advantage of the deep neuro-fuzzy neural network in tomato diseases.
Identification of Tomato Leaf Diseases based on a Deep Neuro-fuzzy Network
J. Inst. Eng. India Ser. A
Tian, Xiaole (Autor:in) / Meng, Xiangyan (Autor:in) / Wu, Qiufeng (Autor:in) / Chen, Yiping (Autor:in) / Pan, Jinchao (Autor:in)
Journal of The Institution of Engineers (India): Series A ; 103 ; 695-706
01.06.2022
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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