Zhenyang He, Mengjun Tong^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4301-4317, 2025, DOI:10.32604/cmc.2025.060550 - 06 March 2025

Abstract Tomato plant diseases often first manifest on the leaves, making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry. However, conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery. This paper proposes a lightweight model for detecting tomato leaf diseases, named LT-YOLO, based on the YOLOv8n architecture. First, we enhance the C2f module into a RepViT Block (RVB) with decoupled token and channel mixers to reduce the cost of feature extraction. Next, we incorporate a novel Efficient… More >

Xianfeng Zhou^1,2,*, Ruiju Sun¹, Zhaojie Zhang¹, Yuanyuan Song¹, Lijiao Jin¹, Lin Yuan³

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 503-519, 2025, DOI:10.32604/phyton.2025.060827 - 06 March 2025

Abstract An accurate and robust estimation of leaf chlorophyll content (LCC) is very important to better know the process of material and energy exchange between plants and the environment. Compared with traditional remote sensing methods, abundant research has made progress in agronomic parameter retrieval using different CNN frameworks. Nevertheless, limited reports have paid attention to the problems, i.e., limited measured data, hyperspectral redundancy, and model convergence issues, when concerning CNN models for parameter estimation. Therefore, the present study tried to analyze the effects of synthetic data size expansion employing a Gaussian process regression (GPR) model for… More >

Sally Negm¹, Badwy Mohdly², Motrih Al-Mutiry³, Wael Shehata⁴, Karima Ahmed⁵, Mohamed Abou-Zeid^2,*, Rana Elessawy⁶, Ashgan Abdel-Azim⁵, Amr Abdel-Fattah², Amani Omar Abuzaid⁷, Enas A. Almanzalawi⁷, Tahani M. Alqahtani⁷, Shouaa A. Alrobaish⁸, Diaa Abd El Moneim⁹, Ahmed M. Abbas^10,11, Mohammed O. Alshaharni¹⁰, Huda Alghamdi¹⁰, Shaimaa G. Salama¹², Kairy Amer⁵

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 347-377, 2025, DOI:10.32604/phyton.2025.057448 - 06 March 2025

Abstract Barley (Hordeum vulgare L.) is a significant global crop that thrives in various climatic and drought-stress conditions. Furthermore, increased drought intervals and more significant weather variability resulting from climate change can affect the severity of plant diseases. Therefore, two primary objectives of integrated disease management regarding climate change are identifying cultivars resistant to foliar diseases and understanding disease progression under abiotic stress. In the current study, we assessed the quantitative foliar disease resistance of 17 commercial barley cultivars under both normal and water stress conditions over two growing seasons (from 2020/21 to 2021/22). The findings demonstrated… More >

Abduladheem Fadhil Khudhur^*, Ayça Kurnaz Türkben, Sefer Kurnaz

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3805-3824, 2024, DOI:10.32604/cmc.2024.056941 - 19 December 2024

Abstract The fast growth in Internet-of-Vehicles (IoV) applications is rendering energy efficiency management of vehicular networks a highly important challenge. Most of the existing models are failing to handle the demand for energy conservation in large-scale heterogeneous environments. Based on Large Energy-Aware Fog (LEAF) computing, this paper proposes a new model to overcome energy-inefficient vehicular networks by simulating large-scale network scenarios. The main inspiration for this work is the ever-growing demand for energy efficiency in IoV-most particularly with the volume of generated data and connected devices. The proposed LEAF model enables researchers to perform simulations of… More >

Mohd Asif Hajam¹, Tasleem Arif¹, Akib Mohi Ud Din Khanday², Mudasir Ahmad Wani^3,*, Muhammad Asim^3,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2077-2131, 2024, DOI:10.32604/cmc.2024.057136 - 18 November 2024

Abstract The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and cost-effectiveness compared to modern drugs. Throughout the extensive history of medicinal plant usage, various plant parts, including flowers, leaves, and roots, have been acknowledged for their healing properties and employed in plant identification. Leaf images, however, stand out as the preferred and easily accessible source of information. Manual plant identification by plant taxonomists is intricate, time-consuming, and prone to errors, relying heavily on human perception. Artificial intelligence (AI) techniques offer a solution by automating plant recognition processes. This study thoroughly examines cutting-edge… More >

Abdelghafour Doghbage^1,*, Safia Belhadj², Hassen Boukerker³, Jean Philippe Mevy⁴, Thierry Gauquelin⁴, Alain Tonetto⁵, Benbader Habib^1,6, Arezki Derridj⁷, Zahra Robã Bouabdelli¹, Walid Soufan⁸, Fathi Abdellatif Belhouadjeb¹

Phyton-International Journal of Experimental Botany, Vol.93, No.9, pp. 2383-2413, 2024, DOI:10.32604/phyton.2024.055528 - 30 September 2024

Abstract The Atlas pistachio tree is a typically Mediterranean species, which represents an important forest heritage in the arid and semi-arid regions of Algeria. It is deeply rooted in the local population’s culture, making it essential to better understand this species for its conservation and valorization. Through our work on 7 provenances of Pistacia atlantica distributed across different bioclimates in Algeria and based on 28 quantitative and qualitative leaf, trichome, and stomatal traits, it was revealed that the Atlas pistachio tree exhibits significant ecotypic variability linked to its habitat and a high adaptability to extreme conditions in… More >

Robertas Damasevicius^1,*, Faheem Mahmood², Yaseen Zaman³, Sobia Dastgeer², Sajid Khan²

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1349-1366, 2024, DOI:10.32604/csse.2024.050359 - 13 September 2024

Abstract Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species. Consequently, the identification of leaf diseases is essential in this field of agriculture. Diseases brought on by bacteria, viruses, and fungi are a significant factor in reduced crop yields. Numerous machine learning models have been applied in the identification of plant diseases, however, with the recent developments in deep learning, this field of study seems to hold huge potential for improved accuracy. This study presents an effective method that uses image processing and deep… More >

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 - 18 July 2024

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 >

Alexis Salazar-Navarro¹, Victor Ruiz-Valdiviezo², Jose Joya-Dávila³, Daniel Gonzalez-Mendoza^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.5, pp. 923-949, 2024, DOI:10.32604/phyton.2024.049612 - 28 May 2024

Abstract Coffee Leaf Rust (CLR) is caused by Hemileia vastatrix in Coffea spp. It is one of the most dangerous phytopathogens for coffee plantations in terms of coffee productivity and coffee cup quality. In this review, we resume the problem of CLR in Mexico and the pathogenesis of H. vastatrix. The review abord plant-pathogen interactions which lead a compatible or incompatible interactions and result in CLR disease or resistance, respectively. The review abord Coffea spp. defense response pathways involved in H. vastatrix pathogenicity. Additionally, current measures to control H. vastatrix proliferation and germination were aborded focused on phytosanitary actions, and biological More >

Jingcheng Zhang¹, Xingjian Zhou¹, Dong Shen¹, Qimeng Yu¹, Lin Yuan^2,*, Yingying Dong³

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 745-762, 2024, DOI:10.32604/phyton.2024.049734 - 29 April 2024

Abstract As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv. oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result of the disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remote sensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutions offer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapid dispersal under suitable conditions, making it difficult to track the disease at… More >