Ashit Kumar Dutta^1,*, Yasser Albagory², Abdul Rahaman Wahab Sait³, Ismail Mohamed Keshta¹

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 899-915, 2022, DOI:10.32604/cmc.2022.026783

Abstract Recently, autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare, agriculture, industrial automation, etc. Among the interesting applications of autonomous systems, their applicability in agricultural sector becomes significant. Autonomous unmanned aerial vehicles (UAVs) can be used for suitable site-specific weed management (SSWM) to improve crop productivity. In spite of substantial advancements in UAV based data collection systems, automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops. The recently developed deep learning (DL) models have exhibited effective performance in several… More >

K. Naveen Durai^*, R. Subha, Anandakumar Haldorai

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 467-483, 2022, DOI:10.32604/iasc.2022.026020

Abstract In cloud computing, the processes of load balancing and task scheduling are major concerns as they are the primary mechanisms responsible for executing tasks by allocating and utilizing the resources of Virtual Machines (VMs) in a more optimal way. This problem of balancing loads and scheduling tasks in the cloud computing scenario can be categorized as an NP-hard problem. This problem of load balancing needs to be efficiently allocated tasks to VMs and sustain the trade-off among the complete set of VMs. It also needs to maintain equilibrium among VMs with the objective of maximizing throughput with a minimized time… More >

Mohd Anul Haq^*

Computer Systems Science and Engineering, Vol.42, No.2, pp. 837-849, 2022, DOI:10.32604/csse.2022.023016

Abstract The problem of weeds in crops is a natural problem for farmers. Machine Learning (ML), Deep Learning (DL), and Unmanned Aerial Vehicles (UAV) are among the advanced technologies that should be used in order to reduce the use of pesticides while also protecting the environment and ensuring the safety of crops. Deep Learning-based crop and weed identification systems have the potential to save money while also reducing environmental stress. The accuracy of ML/DL models has been proven to be restricted in the past due to a variety of factors, including the selection of an efficient wavelength, spatial resolution, and the… More >

JELAN MOFEED^1,2, MOHAMED DEYAB³, ABDELNASER MOHAMED³, MAHMOUD MOUSTAFA^4,5, SALLY NEGM^6,7, EMAD EL-BILAWY¹

BIOCELL, Vol.46, No.1, pp. 247-261, 2022, DOI:10.32604/biocell.2022.015966

Abstract

Microbial infections cause complicated health influences along with bad economic impacts. In the present investigation, three dominant seaweeds namely, Amphiroa anceps, Corallina officinalis and Sargassum filipendula were collected from different Egyptian sites at the Red Sea and Mediterranean Sea during autumn 2019. Organic extracts of the three algae were screened for their antibacterial activity against three pathogenic bacteria Salmonella typhiimurium, Staphylococcus aureus and Escherichia coli, in addition to in vitro antiviral activity against Rotavirus (RV), and Coxsackie virus B3 (CVB3) that cause severe diseases in human. Organic extract of A. anceps, C. officinalis and S. filipendula inhibit E. coli cells… More >

Anand Muni Mishra¹, Shilpi Harnal¹, Khalid Mohiuddin², Vinay Gautam¹, Osman A. Nasr², Nitin Goyal¹, Mamdooh Alwetaishi³, Aman Singh^4,*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1157-1173, 2022, DOI:10.32604/iasc.2022.020174

Abstract Automation of agricultural food production is growing in popularity in scientific communities and industry. The main goal of automation is to identify and detect weeds in the crop. Weed intervention for the duration of crop establishment is a serious difficulty for wheat in North India. The soil nutrient is important for crop production. Weeds usually compete for light, water and air of nutrients and space from the target crop. This research paper assesses the growth rate of weeds due to macronutrients (nitrogen, phosphorus and potassium) absorbed from various soils (fertile, clay and loamy) in the rabi crop field. The weed… More >

Kaihan Xie¹, Yamin Hu^1,*, Stephen Afonaa-Mensah², Chuan Yuan¹, Bin Cao¹, Shuang Wang^1,*, Qian Wang¹

Journal of Renewable Materials, Vol.10, No.2, pp. 541-560, 2022, DOI:10.32604/jrm.2022.017911

Abstract Use Enteromorpha clathrate as a carbon and nitrogen precursor could obtain the N-doped porous carbon materials by two-step pyrolysis. In this paper, the exogenous nitrogen (urea, melamine) and activating agents (KOH, ZnCl₂) were employed for the production of higher-performance carbon materials from seaweed biomass. SEM, BET, FTIR, XRD, XPS, Raman and ultimate analyses were carried out to investigate the morpho-structural and elemental peculiarities of the carbonaceous materials. Moreover, cyclic voltammetry (CV) , galvanostatic charge-discharge (GCD) and cyclic charge-discharge tests were carried out to examine the electrochemical properties of the samples. The results showed that electrochemical performance of the carbonaceous materials… More > Graphic Abstract

Yanlei Xu¹, Yuting Zhai¹, Bin Zhao¹, Yubin Jiao², ShuoLin Kong¹, Yang Zhou^1,*, Zongmei Gao³

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 669-682, 2021, DOI:10.32604/iasc.2021.015225

Abstract For improving the accuracy of weed recognition under complex field conditions, a weed recognition method using depthwise separable convolutional neural network based on deep transfer learning was proposed in this study. To improve the model classification accuracy, the Xception model was refined by using model transferring and fine-tuning. Specifically, the weight parameters trained by ImageNet data set were transferred to the Xception model. Then a global average pooling layer replaced the full connection layer of the Xception model. Finally, the XGBoost classifier was added to the top layer of the model to output results. The performance of the proposed model… More >

Eslam Mohammed Abdelkader^1,2,*, Osama Moselhi³, Mohamed Marzouk⁴, Tarek Zayed⁵

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 643-661, 2020, DOI:10.32604/iasc.2020.010100

Abstract Image segmentation is one of the fundamental stages in computer vision applications. Several meta-heuristics have been applied to solve the segmentation problems by extending the Otsu and entropy functions. However, no single-objective function can optimally handle the diversity of information in images besides the multimodality issues of gray-level images. This paper presents a self-adaptive multi-objective optimization-based method for the detection of crack images in reinforced concrete bridges. The proposed method combines the flexibility of information theory functions in addition to the invasive weed optimization algorithm for bi-level thresholding. The capabilities of the proposed method are demonstrated through comparisons with singleobjective… More >

Noor Ahmad^1,*, Rana Nadeem Abbas¹, Asif Tanveer¹, Zulfiqar Ahmad Saqib²

Phyton-International Journal of Experimental Botany, Vol.89, No.3, pp. 727-742, 2020, DOI:10.32604/phyton.2020.08536

Abstract Parthenium poses serious threat to modern crop production system and necessitate evaluating control practices for its effective management. Efficacy of different weed control practices for controlling parthenium was explored in conventional and deep tillage systems in the field conditions. Hand hoeing (20 and 35 days after emergence), S-Metolachlor (pre-emergence herbicide), sorghum straw mulch @ 5 tons ha^-1 and combination of hand hoeing and sorghum straw mulch (hand hoeing at 20 and straw mulch at 35 days after emergence) were used as weed control practice. Weedy check where no weed control measure was applied was also included in this experiment for… More >

Pereira JR¹, AE Duarte², JB Pitombeira³, MAP da Silva², NE de M Beltrão¹, LM Barros²

Phyton-International Journal of Experimental Botany, Vol.82, pp. 275-279, 2013, DOI:10.32604/phyton.2013.82.275

Abstract An experiment was conducted in dryland conditions of the Brazilian Northeast to determine the number of viable weed seeds (seedbank) in an upland cotton crop, and its distribution in the soil profile, before and after using various herbicide treatments. A randomized block design in a split-plot block scheme with 6 replications was used, where the main plots were constituted by a factorial (13 treatments and 2 sampling soil depths), and the subplots by 2 sampling dates. The seedbank was determined by germination of the recovered weed seeds obtained from different soil depths. The highest number of viable weed seeds in… More >