000			a
999			_c17688 _d17688
003			OSt
005			20220928150036.0
008			220928b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_918165 _a Tian, Xiaole
245			_aIdentification of tomato leaf diseases based on a deep neuro-fuzzy network
250			_aVol.103(2), June
260			_aNew York _bSpringer _c2022
300			_a695-706p.
520			_aThe 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.
650		0	_94642 _aHumanities and Applied Sciences
700			_918166 _aMeng, Xiangyan
773	0		_tJournal of the institution of engineers (India): Series A _x 2250-2149 _dSwitzerland Springer
856			_uhttps://link.springer.com/article/10.1007/s40030-022-00642-4 _yClick here
942			_2ddc _cAR