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 >

Mazen Mushabab Alqahtani¹, Ashit Kumar Dutta², Sultan Almotairi³, M. Ilayaraja⁴, Amani Abdulrahman Albraikan⁵, Fahd N. Al-Wesabi^6,7,*, Mesfer Al Duhayyim⁸

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 217-233, 2023, DOI:10.32604/cmc.2023.025280 - 22 September 2022

Abstract Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique More >

Helong Yu, Xianhe Cheng, Ziqing Li, Qi Cai, Chunguang Bi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 711-738, 2022, DOI:10.32604/cmes.2022.020263 - 27 June 2022

Abstract To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks, a lightweight ResNet (LW-ResNet) model for apple disease recognition is proposed. Based on the deep residual network (ResNet18), the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features. By improving the identity mapping structure to reduce information loss. By introducing the efficient channel attention module (ECANet) to suppress noise from a complex background. The experimental… More >

Swati Singh¹, Isha Gupta², Sheifali Gupta², Deepika Koundal^3,*, Sultan Aljahdali⁴, Shubham Mahajan⁵, Amit Kant Pandit⁵

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1849-1866, 2022, DOI:10.32604/cmc.2022.021875 - 03 November 2021

Abstract In Agriculture Sciences, detection of diseases is one of the most challenging tasks. The mis-interpretations of plant diseases often lead to wrong pesticide selection, resulting in damage of crops. Hence, the automatic recognition of the diseases at earlier stages is important as well as economical for better quality and quantity of fruits. Computer aided detection (CAD) has proven as a supportive tool for disease detection and classification, thus allowing the identification of diseases and reducing the rate of degradation of fruit quality. In this research work, a model based on convolutional neural network with 19… More >

Fahd N. Al-Wesabi^1,2,*, Amani Abdulrahman Albraikan³, Anwer Mustafa Hilal⁴, Majdy M. Eltahir¹, Manar Ahmed Hamza⁴, Abu Sarwar Zamani⁴

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6223-6238, 2022, DOI:10.32604/cmc.2022.021299 - 11 October 2021

Abstract Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze the requirements of individual fields and crops. The recent developments of computer vision and artificial intelligence (AI) techniques find a way for effective detection of plants, diseases, weeds, pests, etc. On the other hand, the detection of plant diseases, particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss. Besides, earlier and precise apple leaf disease detection can minimize the spread of the disease. Earlier works make use of traditional image processing techniques which cannot assure… More >

A. A. Mazlan¹, M. T. H. Sultan^1,2,3,*, S. N. A. Safri², N. Saba², A. U. M. Shah², M. Jawaid²

Journal of Renewable Materials, Vol.8, No.7, pp. 833-843, 2020, DOI:10.32604/jrm.2020.08724 - 01 June 2020

Abstract The present paper deals with the effect of loading different pineapple leaf fibre (PALF) length (short, mixed and long fibres) and their reinforcement for the fabrication of vinyl ester (VE) composites. Performance of PALF/VE composites was investigated through three-point bending flexural testing and viscoelastic (dynamic) mechanical properties through dynamic mechanical analysis (DMA). DMA results revealed that the long PALF/VE composites displayed better mechanical, damping factor and dynamic properties as compared to the short and mixed PALF/VE composites. The flexural strength and modulus of long PALF/VE composites were 113.5 MPa and 14.3 GPa, respectively. The storage More >

Galia Moreno, Karla Ramirez, Marianelly Esquivel, Guillermo Jimenez*

Journal of Renewable Materials, Vol.7, No.1, pp. 9-20, 2019, DOI:10.32604/jrm.2019.00017

Abstract Poly(lactic acid) (PLA) composite films reinforced with microcrystalline cellulose (MCC) extracted from pineapple leaf fibers (PALF) were prepared by a solution casting procedure. In an attempt to improve the interaction between PLA and cellulose, two approaches were adopted; first, poly(ethylene glycol) (PEG) was used as a surfactant, and second, the cellulosic fibers were pre-treated using tert-butanol (TBA). Lignocellulosic and cellulosic substrates were characterized using Fourier transform infrared (FTIR), wide-angle X-ray scattering (WAXS), and thermogravimetrical analysis (TGA). MCC from PALF showed good thermal stability, left few residues after decomposing, and exhibited high crystallinity index. Mechanical, thermal More >

Xiaojian Zhou^1,2, Hui Wang¹, Jun Zhang², Zhifeng Zheng¹, Guanben Du^1,2,*

Journal of Renewable Materials, Vol.6, No.1, pp. 68-74, 2018, DOI:10.7569/JRM.2017.634150

Abstract Pineapple leaf nanofibers (PLNFs) extracted from pineapple leaf fiber were used for reinforcing biobased polyurethane foam (BPU). The dispersion performance of PLNF in the foaming mixture system, nanocomposite foaming behavior, cell morphology, cell size, density, compressive strength and dimensional stability were investigated. The viscosity of the mixtures increased with increasing the PLNF content. The addition of a tiny amount of PLNF did not influence the exothermic temperature of the foam system, but reduced the expansion and gel time of the nanocomposite foams. This reduced time was found to increase the production efficiency. Scanning electron More >

M. Asim¹, M. Jawaid^1,2*, M. Nasir³, N. Saba¹

Journal of Renewable Materials, Vol.6, No.4, pp. 383-393, 2018, DOI:10.7569/JRM.2017.634162

Abstract This study deals with the analysis of dynamic mechanical, thermal and flammability properties of treated and untreated pineapple leaf fiber (PALF) and kenaf fiber (KF) phenolic composites. Results indicated that storage modulus was decreased for all composites with increases in temperature and pattern of slopes for all composites, having almost the same values of E' at glass transition temperature (Tg). The peak of the loss modulus of pure phenolic composites was shown to be much less. After the addition of kenaf/PALF, peaks were higher and shifted towards a high temperature. The Tan delta peak height More >

Galia Moreno, Karla Ramirez, Marianelly Esquivel, Guillermo Jimenez*

Journal of Renewable Materials, Vol.6, No.4, pp. 362-369, 2018, DOI:10.7569/JRM.2017.634167

Abstract Cellulose, microcrystalline cellulose and nanocellulose were prepared from three agricultural waste resources: pineapple leaf (PALF), banana rachis (BR), and sugarcane bagasse (SCB). Each waste resource was first converted into microcrystalline cellulose which was subsequently converted into cellulose nanoparticles by using mild (30% w/v) and strong (60% w/v) sulfuric acid concentrations for extraction. Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA) were used to characterize each waste resource and extracted cellulosic materials. Furthermore, nanocelluloses were studied by zeta potential, size analysis, and transmission electron microscopy (TEM). Cellulose nanowhiskers were successfully obtained and More >