Naeem Ullah¹, Javed Ali Khan^2,*, Sultan Almakdi³, Mohammed S. Alshehri³, Mimonah Al Qathrady⁴, Eman Abdullah Aldakheel^5,*, Doaa Sami Khafaga⁵

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3969-3992, 2023, DOI:10.32604/cmc.2023.041819

Abstract Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification. The proposed DTomatoDNet model… More >

Khalid Rehman Hakeem^1,2,3,*, Hesham F. Alharby¹, M. Irfan Qureshi⁴

Phyton-International Journal of Experimental Botany, Vol.92, No.12, pp. 3323-3344, 2023, DOI:10.32604/phyton.2023.030212

Abstract

Abiotic stresses, including drought, have been found to affect the growth and medicinal quality of numerous herbs. The proposed study aims to study the effects of different drought regimes on the metabolic profile, growth, ecophysiology, cellular antioxidants, and antioxidant potential of Nigella sativa (Black cumin) leaf. Forty-day-old seedlings of N. sativa were exposed to three regimes of drought (control, moderate and high) for a week. UPLC-MS/MS metabolic profile of the leaf reveals the presence of more than a hundred metabolites belonging to anthocyanins, chalcones, dihydro flavonoids, flavonoids, flavanols, flavones, flavonoid carbonoside, isoflavones, etc. Drought was found to alter the contents… More >

Xiaodong Shi^1,*, Yue Qi¹, Liangzhu Lin¹, Jia Wang¹, Xiaobo Qin², Bei Niu^3,*

Phyton-International Journal of Experimental Botany, Vol.92, No.11, pp. 3149-3162, 2023, DOI:10.32604/phyton.2023.027324

Abstract Tartary buckwheat (Fagopyrum tataricum) is a dual-purpose medicinal and food crop grown for its high contents of functional compounds and abundant nutrients. Although studies have shown the differences of total flavonoid content in Tartary buckwheat at different ploidy levels, the composition of flavonoid and its regulatory mechanisms are largely unknown. In this study, the leaf metabolome and transcriptome of diploid and tetraploid accessions of Tartary buckwheat were analyzed to gain insight into the impact of polyploidization on comparative secondary metabolite composition and molecular regulatory mechanism. Based on a widely targeted metabolomics analysis, a total of 792 metabolites were identified, including… More >

Javed Rashid^1,2, Imran Khan¹, Irshad Ahmed Abbasi³, Muhammad Rizwan Saeed⁴, Mubbashar Saddique^5,*, Mohamed Abbas^6,7

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3897-3920, 2023, DOI:10.32604/cmc.2023.040356

Abstract Many plant species have a startling degree of morphological similarity, making it difficult to split and categorize them reliably. Unknown plant species can be challenging to classify and segment using deep learning. While using deep learning architectures has helped improve classification accuracy, the resulting models often need to be more flexible and require a large dataset to train. For the sake of taxonomy, this research proposes a hybrid method for categorizing guava, potato, and java plum leaves. Two new approaches are used to form the hybrid model suggested here. The guava, potato, and java plum plant species have been successfully… More >

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

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 features fusion-based model is proposed,… 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

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 because DNN is also considered… 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

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, including Phytophthora infestans, Fusarium graminearum,… 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

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. First, after comparing various attention… 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

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 solvent control. 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

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), or olive pomace (8.5 kg/tree/year)… More >