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  • Open Access

    ARTICLE

    Analysis of the Hydraulic Performances of a New Liquid Emitter Based on a Leaf Vein Concept

    Tianyu Xu, Zhouming Su, Yanru Su, Zonglei Li, Quanjie Chen, Shuteng Zhi, Ennan Zheng*, Kaili Meng

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.8, pp. 2147-2160, 2023, DOI:10.32604/fdmp.2023.025556

    Abstract The leaf-vein drip irrigation emitter is a new type of drip emitter based on a bionic structure able to support shunting, sharp turns, and increased dissipation. In the present work, the results of twenty-five tests executed in the framework of an orthogonal design strategy are presented in order to clarify the influence of the geometrical parameters of the flow channel on the hydraulic characteristics of such emitter. The corresponding flow index and head loss coefficient are determined through numerical simulations and model testing. The results show that the flow index of the flow channel is 0.4970∼0.5461, which corresponds to good… More >

  • Open Access

    ARTICLE

    Identification of Rice Leaf Disease Using Improved ShuffleNet V2

    Yang Zhou, Chunjiao Fu, Yuting Zhai, Jian Li, Ziqi Jin, Yanlei Xu*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4501-4517, 2023, DOI:10.32604/cmc.2023.038446

    Abstract Accurate identification of rice diseases is crucial for controlling diseases and improving rice yield. To improve the classification accuracy of rice diseases, this paper proposed a classification and identification method based on an improved ShuffleNet V2 (GE-ShuffleNet) model. Firstly, the Ghost module is used to replace the convolution in the two basic unit modules of ShuffleNet V2, and the unimportant convolution is deleted from the two basic unit modules of ShuffleNet V2. The Hardswish activation function is applied to replace the ReLU activation function to improve the identification accuracy of the model. Secondly, an effective channel attention (ECA) module is… More >

  • Open Access

    ARTICLE

    HD-Zip Transcription Factor is Responsible for No-Lobed Leaf in Watermelon (Citrullus lanatus L.)

    Shixiang Duan1,#, Yaomiao Guo1,#, Yinping Wang1, Muhammad Jawad Umer2, Dongming Liu1, Sen Yang1, Huanhuan Niu1, Shouru Sun1, Luming Yang1, Junling Dou1,*, Huayu Zhu1,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1311-1328, 2023, DOI:10.32604/phyton.2023.026928

    Abstract Leaf is a vital organ of plants that plays an essential role in photosynthesis and respiration. As an important agronomic trait in leaf development, leaf shape is classified into lobed, entire (no-lobed), and serrated in most crops. In this study, two-lobed leaf watermelon inbred lines WT2 and WCZ, and a no-lobed leaf watermelon inbred line WT20 were used to create two F2 populations. Segregation analysis suggested that lobed leaves were dominant over the no-lobed leaves, and it was controlled by a signal gene. A locus on watermelon chromosome 4 controlling watermelon lobed/no-lobed leaves was identified through BSA-seq strategy combined with… More >

  • Open Access

    ARTICLE

    Anatomical and Molecular Identification of Ornamental Plant Ficus L. Species

    Abtisam Binnoubah1, Rim Hamdy2, Osama G. Ragab3, Ahmed M. El-Taher4, Ahmed Abou El-Yazied5, Fatmah A. Safhi6,*, Hala A. Elzilal7, Ashwaq T. Althobaiti8, Salha M. ALshamrani9, Diaa Abd El Moneim10, Ahmed El-Banhawy11

    Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1329-1347, 2023, DOI:10.32604/phyton.2023.026888

    Abstract This present study includes twelve species that represent the Ficus genus, namely; aspera, carica, tinctoria subsp. gibbosa, hirta, hispida, neriifolia, palmata, pumila, racemosa, septica, sur, and sycomorus, belonging to the Moraceae family. The species samples were collected from various locations in Egypt. The study focused on the anatomical and molecular characteristics of mature foliage leaves. Since the identification and classification of taxa are highly dependent on the anatomical features of leaves, the anatomical characteristics were recorded in the form of a comparison between the examined plants in the data matrix. This study aims to contribute to the identification of the… More >

  • Open Access

    ARTICLE

    Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework

    Samra Rehman1, Muhammad Attique Khan1, Majed Alhaisoni2, Ammar Armghan3, Fayadh Alenezi3, Abdullah Alqahtani4, Khean Vesal5, Yunyoung Nam5,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1179-1194, 2023, DOI:10.32604/cmc.2023.035324

    Abstract Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye observation. The authors present computer vision techniques for detecting and classifying fruit leaf diseases. Examples of computer vision techniques are preprocessing original images for visualization of infected regions, feature extraction from raw or segmented images, feature fusion, feature selection, and classification. The following are the major challenges identified by researchers in the literature: (i) low-contrast infected regions extract irrelevant and redundant information, which misleads classification accuracy; (ii) irrelevant and redundant information may increase computational time and reduce the designed model’s accuracy. This paper proposed… More >

  • Open Access

    ARTICLE

    A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

    Samra Rehman1, Muhammad Attique Khan1, Majed Alhaisoni2, Ammar Armghan3, Usman Tariq4, Fayadh Alenezi3, Ye Jin Kim5, Byoungchol Chang6,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 697-714, 2023, DOI:10.32604/cmc.2023.035183

    Abstract Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the… More >

  • 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

    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 MB-CLsRG gene encoding protein… 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

    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 another, the Smart Nutrient Deficiency… 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

    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 according to annotations, including… 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

    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. cathayensisMore >

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