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  • Open Access

    ARTICLE

    Development of Molecular Marker Linked with Cercospora Leaf Spot (CLS) Disease Resistance in Vigna radiata, Cloning, and Expression for Evaluating Antifungal Activity against Cercospora canescens

    Maria Babar1, Siddra Ijaz1,*, Imran Ul Haq2, Muhammad Sarwar Khan1

    Phyton-International Journal of Experimental Botany, Vol.92, No.4, pp. 1289-1300, 2023, DOI:10.32604/phyton.2023.026469 - 06 January 2023

    Abstract We developed a molecular marker for MAS of mungbean resistant varieties against CLS from the consensus sequence (MB-CLsRG) of identified RGAs (MB-ClsRCaG1 and MB-ClsRCaG2). The MB-CLsRG sequence-specific primer pair was used to screen Cercospora leaf spot (CLS) resistant varieties of mungbean in genomic analysis that showed congruency with phenotypic screening. Validation of molecular marker linkage with CLS resistance was performed using rtPCR in transcriptomic analysis. The sequenced PCR products showed 100% homology with MB-CLsRG sequence and putative disease resistance proteins that confirmed the linkage of molecular marker with CLS resistance in mungbean. The antifungal potential of… More >

  • Open Access

    ARTICLE

    Smart Nutrient Deficiency Prediction System for Groundnut Leaf

    Janani Malaisamy*, Jebakumar Rethnaraj

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1845-1862, 2023, DOI:10.32604/iasc.2023.034280 - 05 January 2023

    Abstract Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative yield. Distributing fertiliser in optimum amounts will protect the environment’s condition and human health risks. Early identification also prevents the disease’s occurrence in groundnut crops. A convolutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitrogen nutrient deficiency through image features. Since chlorophyll and nitrogen are proportionate to one… More >

  • Open Access

    ARTICLE

    Global and Comparative Proteome Analysis of Nitrogen-Stress Responsive Proteins in the Root, Stem and Leaf of Brassica napus

    Liang Chai1,2, Cheng Cui1, Benchuan Zheng1, Jinfang Zhang1, Jun Jiang1, Haojie Li1,2,*, Liangcai Jiang1,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.3, pp. 645-663, 2023, DOI:10.32604/phyton.2023.024717 - 29 November 2022

    Abstract Nitrogen (N) is one of the basic nutrients and signals for plant development and deficiency of it would always limit the productions of crops in the field. Quantitative research on expression of N-stress responsive proteins on a proteome level remains elusive. In order to gain a deep insight into the proteins responding to nitrogen stress in rapeseed (Brassica napus L.), comparative proteomic analysis was performed to investigate changes of protein expression profiles from the root, stem and leaf under different N concentrations, respectively. More than 200 differential abundance proteins (DAPs) were detected and categorized into groups More >

  • Open Access

    ARTICLE

    Comparative Analysis of the Photosynthetic Characteristics and Active Compounds of Semiliquidambar cathayensis Chang Heteromorphic Leaves

    Xiaoming Tian*, Guangfeng Xiang, Hao Lv, Jing Peng, Lu Zhu

    Phyton-International Journal of Experimental Botany, Vol.92, No.3, pp. 837-850, 2023, DOI:10.32604/phyton.2023.024408 - 29 November 2022

    Abstract In the present study, the variation patterns of leaf shape in different populations of individual Semiliquidambar cathayensis plants were analyzed to investigate the relationship among leaf shape variation, photosynthetic properties, and active compounds to understand the genetic characteristics of S. cathayensis and screen elite germplasms. The leaf shape of 18 offspring from three natural S. cathayensis populations was analyzed to investigate the level of diversity and variation patterns of leaf shape. Furthermore, photosynthetic pigment content, physiological parameters of photosynthesis, and the active compounds in leaves of different shapes were determined. Statistical analysis showed that the leaf shape variation in  S.More >

  • Open Access

    ARTICLE

    Leaf Wettability Difference Among Tea Leaf Ages and Analysis Based on Microscopic Surface Features

    Qingmin Pan1, Yongzong Lu1, Liang Xue2, Yongguang Hu1,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.2, pp. 411-421, 2023, DOI:10.32604/phyton.2022.023437 - 12 October 2022

    Abstract The wettability of leaf surface, commonly represented by contact angle (CA), affects various physiological and physical processes. The present study aims to better understand the wettability of tea leaves and elucidate its influence on the energy barrier of the droplet condensation process. The CA values of different leaf ages (young, mature and old) of five famous tea cultivars (Maolu, longjing 43, Huangjinya, Zhongcha 108 and Anji Baicha) were measured via the sessile drop method, and the micro-morphology of two cultivars leaves (Maolu, Zhongcha 108) was investigated by a 3D super depth-of-field digital microscope. Specifically, two radically distinctive types of CA trends… More >

  • Open Access

    ARTICLE

    Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

    Javed Rashid1,2, Imran Khan1, Ghulam Ali3, Shafiq ur Rehman4, Fahad Alturise5, Tamim Alkhalifah5,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1235-1257, 2023, DOI:10.32604/cmc.2023.032005 - 22 September 2022

    Abstract The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments, soil conditions and higher human consumption. It is cultivated in vast areas of Asian and Non-Asian countries, including Pakistan. The guava plant is vulnerable to diseases, specifically the leaves and fruit, which result in massive crop and profitability losses. The existing plant leaf disease detection techniques can detect only one disease from a leaf. However, a single leaf may contain symptoms of multiple diseases. This study has proposed a hybrid deep learning-based framework for the real-time detection… More >

  • Open Access

    ARTICLE

    Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

    Shafaq Abbas1, Muhammad Attique Khan1, Majed Alhaisoni2, Usman Tariq3, Ammar Armghan4, Fayadh Alenezi4, Arnab Majumdar5, Orawit Thinnukool6,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1139-1159, 2023, DOI:10.32604/cmc.2023.028824 - 22 September 2022

    Abstract Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples.… More >

  • Open Access

    ARTICLE

    Lightweight Multi-scale Convolutional Neural Network for Rice Leaf Disease Recognition

    Chang Zhang1, Ruiwen Ni1, Ye Mu1,2,3,4, Yu Sun1,2,3,4,*, Thobela Louis Tyasi5

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 983-994, 2023, DOI:10.32604/cmc.2023.027269 - 22 September 2022

    Abstract In the field of agricultural information, the identification and prediction of rice leaf disease have always been the focus of research, and deep learning (DL) technology is currently a hot research topic in the field of pattern recognition. The research and development of high-efficiency, high-quality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective. This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network (CNN) model and proposes a recognition model that combines More >

  • Open Access

    ARTICLE

    Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

    Mazen Mushabab Alqahtani1, Ashit Kumar Dutta2, Sultan Almotairi3, M. Ilayaraja4, Amani Abdulrahman Albraikan5, Fahd N. Al-Wesabi6,7,*, Mesfer Al Duhayyim8

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 217-233, 2023, DOI:10.32604/cmc.2023.025280 - 22 September 2022

    Abstract Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

    V. Nirmala1,*, B. Gomathy2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2585-2601, 2023, DOI:10.32604/csse.2023.027512 - 01 August 2022

    Abstract In agricultural engineering, the main challenge is on methodologies used for disease detection. The manual methods depend on the experience of the personal. Due to large variation in environmental condition, disease diagnosis and classification becomes a challenging task. Apart from the disease, the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background. In Cucurbita gourd family, the disease severity examination of leaf samples through computer vision, and deep learning methodologies have gained popularity in recent years. In this paper, a hybrid method More >

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