Identification of tomato leaf diseases based on a deep neuro-fuzzy network
By: Tian, Xiaole
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Contributor(s): Meng, Xiangyan.
Publisher: New York Springer 2022Edition: Vol.103(2), June.Description: 695-706p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series ASummary: 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.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-1764 |
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.
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