Mahmood A. Mahmood^1,2,*, Khalaf Alsalem¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3431-3448, 2024, DOI:10.32604/cmc.2024.047604 - 26 March 2024

Abstract Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses. Early detection of these diseases is essential for effective management. We propose a novel transformed wavelet, feature-fused, pre-trained deep learning model for detecting olive leaf diseases. The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images. The model has four main phases: preprocessing using data augmentation, three-level wavelet transformation, learning using pre-trained deep learning models, and a fused deep learning model. In the preprocessing phase, the image dataset is… More >

Chirag Chandrashekar¹, K. P. Vijayakumar^1,*, K. Pradeep¹, A. Balasundaram^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.047697 - 27 February 2024

Abstract The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. More >

B. BRAILSON MANSINGH¹, K. L. NARASIMHAMU², K. C. VARAPRASAD³, J. S. BINOJ^4,*, A. RADHAKRISHNAN⁵, ALAMRY ALI⁶

Journal of Polymer Materials, Vol.40, No.1-2, pp. 71-82, 2023, DOI:10.32381/JPM.2023.40.1-2.6

Abstract Several agro-wastes are rich in natural fibers and finds scope to be used as reinforcement in composite industry. These natural fibers have some advantages over man-made fibers, including low cost, light weight, renewable nature, high specific strength and modulus, and availability in various forms worldwide. In this paper, the effect of surface modification of leaf sheath coconut fiber (LSF) (an agro-waste) reinforced in phenol formaldehyde matrix composites with silicon carbide (SiC) nano particles as filler material were investigated for its mechanical characteristics. The investigation portrays that coconut LSF (CLSF) modified with potassium permanganate reinforced polymer More >

Shaik Thaseentaj, S. Sudhakar Ilango^*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3593-3618, 2023, DOI:10.32604/cmc.2023.042496 - 26 December 2023

Abstract The South Indian mango industry is confronting severe threats due to various leaf diseases, which significantly impact the yield and quality of the crop. The management and prevention of these diseases depend mainly on their early identification and accurate classification. The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks (CNNs) as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees. Our study collected a rich dataset of leaf images representing different disease classes, including Anthracnose, Powdery… More >

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 - 26 December 2023

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… 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 - 28 December 2023

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

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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 - 24 October 2023

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… 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 - 08 October 2023

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 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 - 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 >