Mehwish Zafar¹, Javeria Amin², Muhammad Sharif¹, Muhammad Almas Anjum³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2779-2793, 2023, DOI:10.32604/cmc.2023.035860 - 08 October 2023

Abstract Worldwide cotton is the most profitable cash crop. Each year the production of this crop suffers because of several diseases. At an early stage, computerized methods are used for disease detection that may reduce the loss in the production of cotton. Although several methods are proposed for the detection of cotton diseases, however, still there are limitations because of low-quality images, size, shape, variations in orientation, and complex background. Due to these factors, there is a need for novel methods for features extraction/selection for the accurate cotton disease classification. Therefore in this research, an optimized… More >

Fawad Ali Shah¹, Habib Akbar¹, Abid Ali^2,3, Parveen Amna⁴, Maha Aljohani⁵, Eman A. Aldhahri⁶, Harun Jamil^7,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1385-1413, 2023, DOI:10.32604/csse.2023.036144 - 28 July 2023

Abstract The detection of rice leaf disease is significant because, as an agricultural and rice exporter country, Pakistan needs to advance in production and lower the risk of diseases. In this rapid globalization era, information technology has increased. A sensing system is mandatory to detect rice diseases using Artificial Intelligence (AI). It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases. Deep Neural Network (DNN) is a novel technique that will help detect disease present on a rice leave… More >

Mona Jamjoom¹, Ahmed Elhadad², Hussein Abulkasim^3,*, Safia Abbas⁴

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 367-382, 2023, DOI:10.32604/cmc.2023.037310 - 08 June 2023

Abstract Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, More >

Huaxin Zhou^1,2, Ziying Fang³, Yilin Wang⁴, Mengjun Tong^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 175-194, 2023, DOI:10.32604/cmc.2023.037342 - 08 June 2023

Abstract Plant diseases have become a challenging threat in the agricultural field. Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early. However, deep learning entails extensive data for training, and it may be challenging to collect plant datasets. Even though plant datasets can be collected, they may be uneven in quantity. As a result, the problem of classification model overfitting arises. This study targets this issue and proposes an auxiliary classifier GAN (small-ACGAN) model based on a small number of datasets to extend the available data.… More >

Huiling Fu¹, Mijun Peng², Qiuwen Tang², Haojun Liang², Yanli Liang², Jiali Fang^2,*, Xuesong Wang^2,*

Journal of Renewable Materials, Vol.11, No.7, pp. 3091-3114, 2023, DOI:10.32604/jrm.2023.026689 - 05 June 2023

Abstract To ensure the export quality of Eucommia ulmoides leaf extract (ELE) and facilitate E. ulmoides leaf inclusion in the directory of traditional Chinese health foods, an overall safety assessment of ELE was performed, including genotoxicity and long-term toxicity, according to the national food safety standards of China. No variations in the reverse mutation number of the nominal bacterial strains were observed under ELE treatment in comparison with the solvent control. Additionally, the micronucleus rates of in vivo mammalian erythrocytes and in vitro mammalian cells under ELE treatment were equivalent to or significantly lower than those of the… More > Graphic Abstract

Bassam F. Alowaiesh^1,*, M. M. Gad², Mohamed Saleh M. Ali³

Phyton-International Journal of Experimental Botany, Vol.92, No.6, pp. 1813-1829, 2023, DOI:10.32604/phyton.2023.026950 - 11 April 2023

Abstract Olive productivity should be improved through stimulating nutrition, particularly under poor fertility soils. Consequently, the objective of this study was to assess the efficacy of applying organic and bio-fertilizers on the physiological growth, yield and fruit quality of olive trees under newly reclaimed poor-fertility sandy soil in an arid environment. During a field experiment carried out at El-Qantara, North Sinai, Egypt over two consecutive seasons (2019–2020 and 2020–2021), olive Kalamata trees were evaluated under three organic fertilizer treatments alone or in combination with three bio-fertilizers treatments. Organic fertilizer was applied as goat manure (16.8 kg/tree/year),… More >

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 - 04 April 2023

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… More >

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 >