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 - 31 March 2023

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… More >

Shixiang Duan^1,#, Yaomiao Guo^1,#, Yinping Wang¹, Muhammad Jawad Umer², Dongming Liu¹, Sen Yang¹, Huanhuan Niu¹, Shouru Sun¹, Luming Yang¹, Junling Dou^1,*, Huayu Zhu^1,*

Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1311-1328, 2023, DOI:10.32604/phyton.2023.026928 - 09 March 2023

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… More >

Abtisam Binnoubah¹, Rim Hamdy², Osama G. Ragab³, Ahmed M. El-Taher⁴, Ahmed Abou El-Yazied⁵, Fatmah A. Safhi^6,*, 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 - 09 March 2023

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 studied species… More >

Samra Rehman¹, Muhammad Attique Khan¹, Majed Alhaisoni², Ammar Armghan³, Fayadh Alenezi³, Abdullah Alqahtani⁴, Khean Vesal⁵, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1179-1194, 2023, DOI:10.32604/cmc.2023.035324 - 06 February 2023

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… More >

Samra Rehman¹, Muhammad Attique Khan¹, Majed Alhaisoni², Ammar Armghan³, Usman Tariq⁴, Fayadh Alenezi³, Ye Jin Kim⁵, Byoungchol Chang^6,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 697-714, 2023, DOI:10.32604/cmc.2023.035183 - 06 February 2023

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… More >

Maria Babar¹, Siddra Ijaz^1,*, 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 >

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 >

Liang Chai^1,2, Cheng Cui¹, Benchuan Zheng¹, Jinfang Zhang¹, Jun Jiang¹, Haojie Li^1,2,*, Liangcai Jiang^1,*

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 >

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 >

Qingmin Pan¹, Yongzong Lu¹, Liang Xue², Yongguang Hu^1,*

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 >