Zhilin Li^1,2, Yuxin Li¹, Chunyu Yan¹, Peng Yan¹, Xiutong Li¹, Mei Yu¹, Tingchi Wen^4,5, Benliang Xie^1,2,3,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 679-694, 2024, DOI:10.32604/cmc.2024.051526

Abstract Diseases in tea trees can result in significant losses in both the quality and quantity of tea production. Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations. However, existing methods face challenges such as a high number of parameters and low recognition accuracy, which hinders their application in tea plantation monitoring equipment. This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves, to address these challenges. The proposed method first embeds a Coordinate Attention (CA) module into the original MobileNetV2 network, enabling the model to locate disease More >

Javed Rashid^1,2, Imran Khan¹, Irshad Ahmed Abbasi³, Muhammad Rizwan Saeed⁴, Mubbashar Saddique^5,*, Mohamed Abbas^6,7

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3897-3920, 2023, DOI:10.32604/cmc.2023.040356

Abstract Many plant species have a startling degree of morphological similarity, making it difficult to split and categorize them reliably. Unknown plant species can be challenging to classify and segment using deep learning. While using deep learning architectures has helped improve classification accuracy, the resulting models often need to be more flexible and require a large dataset to train. For the sake of taxonomy, this research proposes a hybrid method for categorizing guava, potato, and java plum leaves. Two new approaches are used to form the hybrid model suggested here. The guava, potato, and java plum More >

Shah Faisal¹, Kashif Javed¹, Sara Ali¹, Areej Alasiry², Mehrez Marzougui², Muhammad Attique Khan^3,*, Jae-Hyuk Cha^4,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 895-914, 2023, DOI:10.32604/cmc.2023.039781

Abstract Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset More >

Xiaorui Zhang^1,2,3,*, Jie Zhou², Wei Sun^3,4, Sunil Kumar Jha⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1123-1137, 2022, DOI:10.32604/cmc.2022.024589

Abstract The key to preventing the COVID-19 is to diagnose patients quickly and accurately. Studies have shown that using Convolutional Neural Networks (CNN) to analyze chest Computed Tomography (CT) images is helpful for timely COVID-19 diagnosis. However, personal privacy issues, public chest CT data sets are relatively few, which has limited CNN's application to COVID-19 diagnosis. Also, many CNNs have complex structures and massive parameters. Even if equipped with the dedicated Graphics Processing Unit (GPU) for acceleration, it still takes a long time, which is not conductive to widespread application. To solve above problems, this paper… More >

Qazi Mudassar Ilyas¹, Muneer Ahmad^2,*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 241-254, 2022, DOI:10.32604/iasc.2022.018042

Abstract The recent COVID-19 pandemic has had lasting and severe impacts on social gatherings and interaction among people. Local administrative bodies enforce standard operating procedures (SOPs) to combat the spread of COVID-19, with mandatory precautionary measures including use of face masks at social assembly points. In addition, the World Health Organization (WHO) strongly recommends people wear a face mask as a shield against the virus. The manual inspection of a large number of people for face mask enforcement is a challenge for law enforcement agencies. This work proposes an automatic face mask detection solution using an… More >

Jiancheng Zou¹, Na Zhu^1,*, Bailin Ge¹, Don Hong²

Journal of New Media, Vol.3, No.1, pp. 1-10, 2021, DOI:10.32604/jnm.2021.017763

Abstract We propose an improved a single-shot detector (SSD) algorithm to detect falls of the elderly. The VGG16 network part of the SSD network is replaced with the MobilenetV2 network. At the same time, we change the infrastructure of MobilenetV2 network, the three layers that were not downsampled at the end were removed, which can make the model structure simpler and faster to detect. The complete Intersection-over-Union (CIoU) loss function is introduced to get a good regression of the target borders that have different sizes and different proportions. We use Feature Pyramid Network (FPN) for upsampling, More >