Yousef Alhwaiti^1,*, Muhammad Hameed Siddiqi¹, Irshad Ahmad²

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3011-3031, 2023, DOI:10.32604/csse.2023.042110

Abstract Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy. The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as they were implemented against controlled environments. Therefore, in this paper, a… More >

Jigna Patel¹, Anand Ruparelia¹, Sudeep Tanwar^1,*, Fayez Alqahtani², Amr Tolba³, Ravi Sharma⁴, Maria Simona Raboaca^5,6,*, Bogdan Constantin Neagu⁷

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1281-1301, 2023, DOI:10.32604/cmc.2023.038796

Abstract The overgrowth of weeds growing along with the primary crop in the fields reduces crop production. Conventional solutions like hand weeding are labor-intensive, costly, and time-consuming; farmers have used herbicides. The application of herbicide is effective but causes environmental and health concerns. Hence, Precision Agriculture (PA) suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants. Motivated by the gap above, we proposed a Deep Learning (DL) based model for detecting Eggplant (Brinjal) weed in this paper. The key objective of this study is to detect plant and non-plant (weed) parts from crop images.… More >

Tao Yin¹, Changgen Peng^2,*, Weijie Tan³, Dequan Xu⁴, Hanlin Tang⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 827-843, 2024, DOI:10.32604/cmes.2023.029039

Abstract In the assessment of car insurance claims, the claim rate for car insurance presents a highly skewed probability distribution, which is typically modeled using Tweedie distribution. The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset, when the data is provided by multiple parties, training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge. To address this issue, this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos. The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection… More >

B. Keerthi Samhitha^*, R. Subhashini

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3125-3142, 2023, DOI:10.32604/iasc.2023.040643

Abstract Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management decisions about recirculating the aquaculture system while decreasing labor. The classic detection approach involves placing sensors on the skin or body of the fish, which may interfere with typical behavior and welfare. The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy. This study develops an intelligent fish behavior classification using… More >

Bimal Kumar Ghimire¹, Chang Yeon Yu², Seung Hyun Kim¹, Ill Min Chung^1,*

Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1439-1467, 2023, DOI:10.32604/phyton.2023.023076

Abstract Miscanthus, is a promising bioenergy crop, considered superior to other bioenergy crops because of its higher water and nutrient use efficiency, cold tolerance, and higher production of biomass. Broadleaf weeds and grass weeds, cause major problems in the Miscanthus field. A field experiment was conducted in 2018 and 2019, to assess the effects of pre-emergence (alachlor and napropamide) and post-emergence herbicides (nicosulfuron, dicamba, bentazon, and glufosinate ammonium) on broadleaf and grass weeds in M. sinensis and M. sacchariflorus fields. The weed control efficiency and phytotoxicity of pre- and post-emergence herbicides were evaluated at 30 days after treatment (DAT) and compared… More >

Saud Yonbawi¹, Sultan Alahmari², T. Satyanarayana Murthy³, Padmakar Maddala⁴, E. Laxmi Lydia⁵, Seifedine Kadry^6,7,8,*, Jungeun Kim⁹

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1533-1547, 2023, DOI:10.32604/csse.2023.036296

Abstract Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield. Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns. Weed control has become one of the significant problems in the agricultural sector. In traditional weed control, the entire field is treated uniformly by spraying the soil, a single herbicide dose, weed, and crops in the same way. For more precise farming, robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the… More >

Yuzhi Wu¹, Cunzhen Geng^1,*, Chaochao Cui², Zhefeng Xin², Yanzhi Xia¹, Zhixin Xue^1,*

Journal of Renewable Materials, Vol.11, No.3, pp. 1199-1208, 2023, DOI:10.32604/jrm.2022.022976

Abstract The sulfate groups in agar structure played a good role in the formation of fiber. However, commercially available agar is usually extracted from red algae by alkali treatment to decrease the content of sulfate group for the purpose of high gel strength. In this paper, an alkali-free method of agar extraction from Gracilaria lemaneiformis was proposed for the wet-spinning purpose. This method is environmentally friendly, reduces the extraction steps, saves energy, and reduces the production cost of agar fiber. The improved agar preparation process not only has higher agar yield, but also has higher molecular weight and sulfate group content,… More > Graphic Abstract

Ge Chen^1,2, Yuling Kang¹, Fangbo Cao¹, Jiana Chen¹, Min Huang^1,*

Phyton-International Journal of Experimental Botany, Vol.92, No.1, pp. 71-77, 2023, DOI:10.32604/phyton.2022.022313

Abstract Weeds occurred during the fallow season can well perform the function of carbon (C) capture due to receiving little human disturbance. This study aimed to evaluate the C capture potential of fallow weeds in rice (Oryza sativa L.) cropping systems. A six-region, two-year on-farm investigation and a three-year tillage experiment were conducted to estimate C capture in fallow weeds in rice cropping systems. The on-farm investigation showed that the average mean C capture by fallow weeds across six regions and two years reached 112 g m^–2. The tillage experiment indicated that no-tillage practices increased C capture by fallow weeds by… More >

R. Punithavathi¹, A. Delphin Carolina Rani², K. R. Sughashini³, Chinnarao Kurangi⁴, M. Nirmala⁵, Hasmath Farhana Thariq Ahmed⁶, S. P. Balamurugan^7,*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2759-2774, 2023, DOI:10.32604/csse.2023.027647

Abstract Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique intends to properly discriminate the… More >

M. Manikandakumar^1,*, P. Karthikeyan²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 913-927, 2023, DOI:10.32604/csse.2023.025434

Abstract Weed is a plant that grows along with nearly all field crops, including rice, wheat, cotton, millets and sugar cane, affecting crop yield and quality. Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity. To address this issue, an efficient weed classification model is proposed with the Deep Convolutional Neural Network (CNN) that implements automatic feature extraction and performs complex feature learning for image classification. Throughout this work, weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds… More >