期刊
TRAITEMENT DU SIGNAL
卷 39, 期 1, 页码 71-77出版社
INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC
DOI: 10.18280/ts.390107
关键词
residual network; NCA; tomato leaf disease; deep learning
There is a growing need for automatic disease detection applications to assist farmers in identifying agricultural product diseases. This study utilized Convolutional Neural Networks (CNN) and ResNet architectures to detect tomato diseases, achieving a high level of accuracy.
The need for automatic disease detection applications that can help farmers in the detection of agricultural product diseases is increasing day by day. Convolutional Neural Network (CNN) is a very popular field in image processing, recognition, and classification. It is seen that CNN architectures are used in the determination of agricultural products. In this study, 3 different ResNet architectures of the features automatically are used in the detection of tomato diseases. The most efficient features obtained from these architectures have been obtained by the NCA algorithm again. The features obtained have been trained with the Cubic SVM machine learning algorithm. Tomato leaves belonging to a total of 10 classes have been trained at 80% and a test performance rate of 98.2% has been achieved.
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