Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Identification of Tomato Leaf Diseases based on a Deep Neuro-fuzzy Network

Identification of Tomato Leaf Diseases based on a Deep Neuro-fuzzy Network 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of The Institution of Engineers (India):Series A Springer Journals

Identification of Tomato Leaf Diseases based on a Deep Neuro-fuzzy Network

Loading next page...
 
/lp/springer-journals/identification-of-tomato-leaf-diseases-based-on-a-deep-neuro-fuzzy-b0ZdWQD1J6
Publisher
Springer Journals
Copyright
Copyright © The Institution of Engineers (India) 2022
ISSN
2250-2149
eISSN
2250-2157
DOI
10.1007/s40030-022-00642-4
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Journal of The Institution of Engineers (India):Series ASpringer Journals

Published: May 2, 2022

Keywords: Tomato diseases; Recognition accuracy; The deep neuro-fuzzy network; Fuzzy rules

References