Integration of InceptionResNetV2 with VGG19 for sugarcane leaf disease recognition
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Abstract
The development of sugarcane leaf disease recognition techniques is challenging due to the diversity of disease characteristics expressed through variations in leaf color, shape, texture, and spatial distribution patterns, which are complex and influenced by environmental conditions. Therefore, advanced image processing and machine learning techniques are essential for accurately recognizing the sugarcane leaf disease. This study presented an integration of InceptionResNetV2 with VGG19 networks using convolutional layers as the main components of the architecture, due to the advantages of the inception module for diverse feature analysis combined with residual connections to address the issue of data loss in deep networks. The proposed model was trained and tested on sugarcane leaf disease dataset and compared with the previous results. Experimental results showed that the model achieved an accuracy of 99.25%, a loss of 0.0263, and a training time of 24.55 minutes. The results of this study not only demonstrated the model's effectiveness in recognizing sugarcane leaf disease but also provided important guidelines for developing plant disease technologies on an industrial scale.
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