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  • Open Access

    REVIEW

    A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

    Xiaozhong Yu1,2,*, Jinhua Zheng1,2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 197-225, 2024, DOI:10.32604/cmc.2023.043943 - 30 January 2024

    Abstract In rice production, the prevention and management of pests and diseases have always received special attention. Traditional methods require human experts, which is costly and time-consuming. Due to the complexity of the structure of rice diseases and pests, quickly and reliably recognizing and locating them is difficult. Recently, deep learning technology has been employed to detect and identify rice diseases and pests. This paper introduces common publicly available datasets; summarizes the applications on rice diseases and pests from the aspects of image recognition, object detection, image segmentation, attention mechanism, and few-shot learning methods according to More >

  • Open Access

    ARTICLE

    Monitoring Thosea sinensis Walker in Tea Plantations Based on UAV Multi-Spectral Image

    Lin Yuan1, Qimeng Yu1, Yao Zhang2,*, Xiaochang Wang3, Ouguan Xu1, Wenjing Li1

    Phyton-International Journal of Experimental Botany, Vol.92, No.3, pp. 747-761, 2023, DOI:10.32604/phyton.2023.025502 - 29 November 2022

    Abstract Thosea sinensis Walker (TSW) rapidly spreads and severely damages the tea plants. Therefore, finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community. Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests. In this work, based on the unmanned aerial vehicle (UAV) platform, five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations. By combining the minimum redundancy maximum relevance (mRMR) with the selected spectral features,… More >

  • Open Access

    ARTICLE

    Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands

    Lin Yuan1, Jingcheng Zhang2,*, Quan Deng2, Yingying Dong3, Haolin Wang2, Xiankun Du2

    Phyton-International Journal of Experimental Botany, Vol.92, No.2, pp. 611-628, 2023, DOI:10.32604/phyton.2022.023662 - 12 October 2022

    Abstract Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests. In most previous studies, the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice. If a detection system can be established with a few bands and a relatively simple logic, it would be of great significance for application. This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat, i.e., powdery mildew, yellow rust… More >

  • Open Access

    ARTICLE

    Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

    U. Muthaiah1,*, S. Chitra2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3447-3458, 2023, DOI:10.32604/iasc.2023.028869 - 17 August 2022

    Abstract Image processing, agricultural production, and field monitoring are essential studies in the research field. Plant diseases have an impact on agricultural production and quality. Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops. Manually identifying the agricultural pests is usually evident in plants; also, it takes more time and is an expensive technique. A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations. An atmosphere generates vast amounts of data as it is monitored closely; the evaluation of this big… More >

  • Open Access

    ARTICLE

    Detection and Discrimination of Tea Plant Stresses Based on Hyperspectral Imaging Technique at a Canopy Level

    Lihan Cui1, Lijie Yan1, Xiaohu Zhao1, Lin Yuan2, Jing Jin3, Jingcheng Zhang1,*

    Phyton-International Journal of Experimental Botany, Vol.90, No.2, pp. 621-634, 2021, DOI:10.32604/phyton.2021.015511 - 07 February 2021

    Abstract Tea plant stresses threaten the quality of tea seriously. The technology corresponding to the fast detection and differentiation of stresses is of great significance for plant protection in tea plantation. In recent years, hyperspectral imaging technology has shown great potential in detecting and differentiating plant diseases, pests and some other stresses at the leaf level. However, the lack of studies at canopy level hampers the detection of tea plant stresses at a larger scale. In this study, based on the canopy-level hyperspectral imaging data, the methods for identifying and differentiating the three commonly occurred tea… More >

  • Open Access

    ARTICLE

    Production system and value chain in oregano (Origanum sp.) cultivation in the province of Córdoba (Argentina)

    Argüello JA1, SB Núñez1, V Davidenco1, DA Suárez2, L Seisdedos1, MC Baigorria1, N La Porta1, G Ruiz1, V Yossen1

    Phyton-International Journal of Experimental Botany, Vol.81, pp. 23-34, 2012, DOI:10.32604/phyton.2012.81.023

    Abstract The aim of the present review was to analyze and identify the problems associated with the Production System and Chain Value of Oregano in the area of Traslasierra Valley, province of Córdoba. Traditional ecotypes, such as Criollo, Chileno II and Compacto, are cultivated in the region, as well as new ecotypes such as “Serrano Cordobés”, “Flor Rosa” and “Rosa Fuerte”. The Traslasierra Valley of Córdoba is a very suitable area for the production and for increasing the production of oregano. However, the agricultural management of the different oregano ecotypes should be optimized. Ecophysiological studies conducted… More >

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