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 >

Zhunruo Feng¹, Ruomeng Shi², Yuhan Jiang³, Yiming Han¹, Zeyang Ma¹, Yuheng Ren^4,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3559-3575, 2025, DOI:10.32604/cmc.2025.065152 - 03 July 2025

Abstract In this study, we propose Space-to-Depth and You Only Look Once Version 7 (SPD-YOLOv7), an accurate and efficient method for detecting pests in maize crops, addressing challenges such as small pest sizes, blurred images, low resolution, and significant species variation across different growth stages. To improve the model’s ability to generalize and its robustness, we incorporate target background analysis, data augmentation, and processing techniques like Gaussian noise and brightness adjustment. In target detection, increasing the depth of the neural network can lead to the loss of small target information. To overcome this, we introduce the… More >

CARLOS ALFONSO FLORES-GUTIéRREZ¹, ERANDIS DHENI TORRES-SáNCHEZ¹, EMMANUEL REYES-URIBE¹, JUAN HERIBERTO TORRES-JASSO², JOEL SALAZAR-FLORES^1,*

BIOCELL, Vol.49, No.5, pp. 767-787, 2025, DOI:10.32604/biocell.2025.062225 - 27 May 2025

Abstract Pesticides are chemical substances used to eliminate various pests. Currently, more than two million tons of pesticides are used annually in developing and developed countries. One of the chronic diseases associated with pesticide poisoning is diabetes. This review aimed to elucidate the mechanisms of action involved in the development of diabetes after pesticide poisoning. Relevant information was collected between January and May 2024, using databases such as PubMed, Google Academic, and Elsevier. Pesticides reduce the secretion of glucagon-like peptide-1 (GLP-1) in the intestine, thereby decreasing the release of insulin. Moreover, pesticides are metabolized to acetic More >

Muhammad Qasim^1,2, Syed M. Adnan Shah¹, Qamas Gul Khan Safi¹, Danish Mahmood², Adeel Iqbal^3,*, Ali Nauman³, Sung Won Kim^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4429-4445, 2025, DOI:10.32604/cmc.2025.065060 - 19 May 2025

Abstract Grains are the most important food consumed globally, yet their yield can be severely impacted by pest infestations. Addressing this issue, scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods. Traditional approaches often rely on preprocessed datasets, but there is a growing need for solutions that utilize real-time images of pests in their natural habitat. Our study introduces a novel two-step approach to tackle this challenge. Initially, raw images with complex backgrounds are captured. In the subsequent step, feature extraction is performed using both hand-crafted algorithms (Haralick, LBP, and Color… More >

Zijun Gao^*, Zheyi Li, Chunqi Zhang, Ying Wang, Jingwen Su

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4353-4371, 2025, DOI:10.32604/cmc.2025.061743 - 19 May 2025

Abstract Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks; however, their application in the actual agricultural production process is still challenging owing to the problems of inter-species similarity, multi-scale, and background complexity of pests. To address these problems, this study proposes an FD-YOLO pest target detection model. The FD-YOLO model uses a Fully Connected Feature Pyramid Network (FC-FPN) instead of a PANet in the neck, which can adaptively fuse multi-scale information so that the model can retain small-scale target features in the deep layer, enhance large-scale target features in the… More >

Pamela Lippi¹, Aleš Eichmeier², Sergio Puccioni³, Giovan Battista Mattii¹, Eleonora Cataldo^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.4, pp. 1047-1072, 2025, DOI:10.32604/phyton.2025.064272 - 30 April 2025

Abstract The search for environmentally friendly approaches in viticulture is increasing, driven by the need to minimize the ecological footprint of conventional methods while ensuring high grape quality and stable yields. Among the various alternatives explored, essential oils (EOs) have drawn attention due to their natural origin and bioactive properties, including antimicrobial, antifungal, and insect-repellent effects. They are characterized by numerous utilisations, from managing diseases and pests in vineyards to post-harvest applications to preserve and prolong storage duration. This innovative review examines, for the first time, the topic of EOs on viticulture, embracing their multiple uses… More >

Farooq Ali^1,*, Huma Qayyum¹, Kashif Saleem², Iftikhar Ahmad³, Muhammad Javed Iqbal⁴

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2373-2388, 2025, DOI:10.32604/cmc.2025.060745 - 17 February 2025

Abstract Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging… More >

Pratyush Kumar Das¹, Satyabrata Nanda^2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.12, pp. 3217-3235, 2024, DOI:10.32604/phyton.2024.057956 - 31 December 2024

Abstract Insect pests cause severe crop damage, resulting in substantial economic losses and threats to global food security. Conventional insecticides are low-cost chemical agents that kill the target insects and some non-specific beneficial organisms. Due to their toxic and non-biodegradable nature, these conventional insecticides persist in the environment, thus causing pollution and accumulating in the food chain. The development of novel insecticidal products based on double-stranded (dsRNA)-based RNA interference (RNAi) technology is a sustainable tool to effectively control insect pests. The dsRNA-based insecticides are known for their specificity, non-toxicity, and biodegradability. The current review introduces the… More >

Ji-Yuan Ding¹, Wang-Su Jeon², Sang-Yong Rhee^2,*, Chang-Man Zou^1,3

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3989-4014, 2024, DOI:10.32604/cmc.2024.057473 - 19 December 2024

Abstract In complex agricultural environments, cucumber disease identification is confronted with challenges like symptom diversity, environmental interference, and poor detection accuracy. This paper presents the DM-YOLO model, which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases. Traditional detection models have a tough time identifying small-scale and overlapping symptoms, especially when critical features are obscured by lighting variations, occlusion, and background noise. The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way. First, the MultiCat module employs a multi-scale feature processing strategy with… More >

Zeyu Deng¹, Yuan Liang^1,*, Gengdong Cheng¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012504

Abstract Compared to single-material optimization, topology optimization of multi-material structures offers a larger design space. It also requires efficient material selection methods to provide guidance for designers. The predominant methods are based on interpolation schemes, which introduce order-dependence issues during the optimization process. This means the sequence in which materials are arranged can significantly impact the optimization outcomes and may lead to notable issues with material gradation. This paper identifies the mathematical essence of multi-material topology optimization as a nonlinear multi-valued integer programming problem. In this paper, we propose a novel stochastic discrete steepest descent multi-valued More >