Jianbo Lu^1,2,*, Ruxin Shi², Jin Tong³, Wenqi Cheng⁴, Xiaoya Ma^1,3, Xiaobin Liu²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 525-544, 2023, DOI:10.32604/iasc.2023.038287

Abstract In the deep learning approach for identifying plant diseases, the high complexity of the network model, the large number of parameters, and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources. In this study, a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed. In the proposed model, the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions; the efficient channel attention module is added into the ShuffleNetV2 model network structure… More >

Yang Zhou, Chunjiao Fu, Yuting Zhai, Jian Li, Ziqi Jin, Yanlei Xu^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4501-4517, 2023, DOI:10.32604/cmc.2023.038446

Abstract Accurate identification of rice diseases is crucial for controlling diseases and improving rice yield. To improve the classification accuracy of rice diseases, this paper proposed a classification and identification method based on an improved ShuffleNet V2 (GE-ShuffleNet) model. Firstly, the Ghost module is used to replace the convolution in the two basic unit modules of ShuffleNet V2, and the unimportant convolution is deleted from the two basic unit modules of ShuffleNet V2. The Hardswish activation function is applied to replace the ReLU activation function to improve the identification accuracy of the model. Secondly, an effective channel attention (ECA) module is… More >

Siddiqui Muhammad Yasir¹, Hyunsik Ahn^2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1847-1861, 2023, DOI:10.32604/cmc.2023.035698

Abstract Deep learning has been constantly improving in recent years, and a significant number of researchers have devoted themselves to the research of defect detection algorithms. Detection and recognition of small and complex targets is still a problem that needs to be solved. The authors of this research would like to present an improved defect detection model for detecting small and complex defect targets in steel surfaces. During steel strip production, mechanical forces and environmental factors cause surface defects of the steel strip. Therefore, the detection of such defects is key to the production of high-quality products. Moreover, surface defects of… More >

Abdul Muiz Fayyaz¹, Kawther A. Al-Dhlan², Saeed Ur Rehman¹, Mudassar Raza¹, Waqar Mehmood³, Muhammad Shafiq⁴, Jin-Ghoo Choi^4,*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 507-522, 2022, DOI:10.32604/iasc.2022.016392

Abstract Crops are very important to the financial needs of a country. Due to various diseases caused by different pathogens, a large number of crops have been destroyed. As humanoids, our basic need is food for survival, and the most basic foundation of our food is agriculture. For many developing countries, it is mainly an important source of income. Bacterial diseases are one of the main diseases that cause improper production and a major economic crisis for the country. Therefore, it is necessary to detect the disease early. However, it is not easy for humans to analyze the different leaves of… More >