Nameer Baht^1,*, Enrique Domínguez^1,2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4441-4455, 2025, DOI:10.32604/cmc.2025.069528 - 23 October 2025

Abstract Coffee is an important agricultural commodity, and its production is threatened by various diseases. It is also a source of concern for coffee-exporting countries, which is causing them to rethink their strategies for the future. Maintaining crop production requires early diagnosis. Notably, Coffee Leaf Miner (CLM) Machine learning (ML) offers promising tools for automated disease detection. Early detection of CLM is crucial for minimising yield losses. However, this study explores the effectiveness of using Convolutional Neural Networks (CNNs) with transfer learning algorithms ResNet50, DenseNet121, MobileNet, Inception, and hybrid VGG19 for classifying coffee leaf images as… More >

Geoffry Mutiso^*, John Ndia

Journal on Artificial Intelligence, Vol.7, pp. 275-288, 2025, DOI:10.32604/jai.2025.069768 - 05 September 2025

Abstract Tomato leaf diseases significantly reduce crop yield; therefore, early and accurate disease detection is required. Traditional detection methods are laborious and error-prone, particularly in large-scale farms, whereas existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse environmental and disease conditions. This study presents a unified U-Net-Vision Mamba Model with Hierarchical Bottleneck Attention Mechanism (U-net-Vim-HBAM), which integrates U-Net’s high-resolution segmentation, Vision Mamba’s efficient contextual processing, and a Hierarchical Bottleneck Attention Mechanism to address the challenges of disease detection accuracy, computational complexity, and efficiency in existing models. The model was trained on More >

Hao Sun^1,2, Di Cai¹, Dae-Ki Kang^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1675-1692, 2025, DOI:10.32604/cmc.2025.067675 - 29 August 2025

Abstract Accurately identifying crop pests and diseases ensures agricultural productivity and safety. Although current YOLO-based detection models offer real-time capabilities, their conventional convolutional layers involve high computational redundancy and a fixed receptive field, making it challenging to capture local details and global semantics in complex scenarios simultaneously. This leads to significant issues like missed detections of small targets and heightened sensitivity to background interference. To address these challenges, this paper proposes a lightweight adaptive detection network—StarSpark-AdaptiveNet (SSANet), which optimizes features through a dual-module collaborative mechanism. Specifically, the StarNet module utilizes Depthwise separable convolutions (DW-Conv) and dynamic… More >

MD Tausif Mallick¹, Saptarshi Banerjee², Nityananda Thakur³, Himadri Nath Saha^4,*, Amlan Chakrabarti¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 121-180, 2025, DOI:10.32604/cmc.2025.065250 - 29 August 2025

Abstract Addressing plant diseases and pests is not just crucial; it’s a matter of utmost importance for enhancing crop production and preventing economic losses. Recent advancements in artificial intelligence, machine learning, and deep learning have revolutionised the precision and efficiency of this process, surpassing the limitations of manual identification. This study comprehensively reviews modern computer-based techniques, including recent advances in artificial intelligence, for detecting diseases and pests through images. This paper uniquely categorises methodologies into hyperspectral imaging, non-visualisation techniques, visualisation approaches, modified deep learning architectures, and transformer models, helping researchers gain detailed, insightful understandings. The exhaustive… More >

Sameera V Mohd Sagheer^1,*, Meghana K H², P M Ameer³, Muneer Parayangat⁴, Mohamed Abbas⁴

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4259-4297, 2025, DOI:10.32604/cmc.2025.063726 - 30 July 2025

Abstract Integrating multiple medical imaging techniques, including Magnetic Resonance Imaging (MRI), Computed Tomography, Positron Emission Tomography (PET), and ultrasound, provides a comprehensive view of the patient health status. Each of these methods contributes unique diagnostic insights, enhancing the overall assessment of patient condition. Nevertheless, the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution, data collection methods, and noise levels. While traditional models like Convolutional Neural Networks (CNNs) excel in single-modality tasks, they struggle to handle multi-modal complexities, lacking the capacity to model global relationships. This research presents a novel approach for… More >

Maheen Anwar¹, Saima Farhan¹, Yasin Ul Haq², Waqar Azeem³, Muhammad Ilyas⁴, Razvan Cristian Voicu^5,*, Muhammad Hassan Tanveer⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3477-3502, 2025, DOI:10.32604/cmc.2025.065141 - 03 July 2025

Abstract Glaucoma, a chronic eye disease affecting millions worldwide, poses a substantial threat to eyesight and can result in permanent vision loss if left untreated. Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective. To address these challenges, this research proposes a computer-aided diagnosis (CAD) approach using Artificial Intelligence (AI) techniques for binary and multiclass classification of glaucoma stages. An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network (ConvNet) models–ResNet-50, VGG-16, and InceptionV3 is utilized in this paper. This fusion technique enhances… More >

Urfa Gul^#, M. Junaid Gul^#, Gyu Sang Choi, Chang-Hyeon Park^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3811-3834, 2025, DOI:10.32604/cmc.2025.063789 - 03 July 2025

Abstract Spiking Neural Network (SNN) inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection, offering enhanced performance and efficiency in contrast to Artificial Neural Networks (ANN). Unlike conventional ANNs, which process static images without fully capturing the inherent temporal dynamics, our approach represents the first implementation of SNNs tailored explicitly for agricultural disease classification, integrating an encoding method to convert static RGB plant images into temporally encoded spike trains. Additionally, while Bernoulli trials and standard deep learning architectures like Convolutional Neural Networks (CNNs) and Fully Connected Neural Networks… More >

Walaa N. Ismail^1,2,#,*, Fathimathul Rajeena P. P.^3,#, Mona A. S. Ali^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3709-3741, 2025, DOI:10.32604/cmes.2025.065564 - 30 June 2025

Abstract Early detection of Alzheimer’s disease (AD) is crucial, particularly in resource-constrained medical settings. This study introduces an optimized deep learning framework that conceptualizes neural networks as computational “sensors” for neurodegenerative diagnosis, incorporating feature selection, selective layer unfreezing, pruning, and algorithmic optimization. An enhanced lightweight hybrid DenseNet201 model is proposed, integrating layer pruning strategies for feature selection and bioinspired optimization techniques, including Genetic Algorithm (GA) and Harris Hawks Optimization (HHO), for hyperparameter tuning. Layer pruning helps identify and eliminate less significant features, while model parameter optimization further enhances performance by fine-tuning critical hyperparameters, improving convergence speed,… More >

Rahim Khan¹, Ihsan Rabbi¹, Umar Farooq¹, Jawad Khan^2,*, Fahad Alturise^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 827-842, 2025, DOI:10.32604/cmc.2025.063303 - 09 June 2025

Abstract Leaf disease identification is one of the most promising applications of convolutional neural networks (CNNs). This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health. In this study, a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves. The researchers utilized a dataset of 3422 images, divided into four classes: healthy, fig rust, fig mosaic, and anthracnose. These diseases can significantly reduce the yield and quality of fig tree fruit. The objective of this research is to develop a… More >

Hussam Qushtom, Ahmad Hasasneh^*, Sari Masri

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1379-1395, 2025, DOI:10.32604/cmc.2025.061995 - 09 June 2025

Abstract This study presents an enhanced convolutional neural network (CNN) model integrated with Explainable Artificial Intelligence (XAI) techniques for accurate prediction and interpretation of wheat crop diseases. The aim is to streamline the detection process while offering transparent insights into the model’s decision-making to support effective disease management. To evaluate the model, a dataset was collected from wheat fields in Kotli, Azad Kashmir, Pakistan, and tested across multiple data splits. The proposed model demonstrates improved stability, faster convergence, and higher classification accuracy. The results show significant improvements in prediction accuracy and stability compared to prior works,… More >