Zhunruo Feng¹, Ruomeng Shi², Yuhan Jiang³, Yiming Han¹, Zeyang Ma¹, Yuheng Ren^4,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3559-3575, 2025, DOI:10.32604/cmc.2025.065152 - 03 July 2025

Abstract In this study, we propose Space-to-Depth and You Only Look Once Version 7 (SPD-YOLOv7), an accurate and efficient method for detecting pests in maize crops, addressing challenges such as small pest sizes, blurred images, low resolution, and significant species variation across different growth stages. To improve the model’s ability to generalize and its robustness, we incorporate target background analysis, data augmentation, and processing techniques like Gaussian noise and brightness adjustment. In target detection, increasing the depth of the neural network can lead to the loss of small target information. To overcome this, we introduce the… More >

Farooq Ali^1,*, Huma Qayyum¹, Kashif Saleem², Iftikhar Ahmad³, Muhammad Javed Iqbal⁴

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2373-2388, 2025, DOI:10.32604/cmc.2025.060745 - 17 February 2025

Abstract Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging… More >

Ji-Yuan Ding¹, Wang-Su Jeon², Sang-Yong Rhee^2,*, Chang-Man Zou^1,3

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3989-4014, 2024, DOI:10.32604/cmc.2024.057473 - 19 December 2024

Abstract In complex agricultural environments, cucumber disease identification is confronted with challenges like symptom diversity, environmental interference, and poor detection accuracy. This paper presents the DM-YOLO model, which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases. Traditional detection models have a tough time identifying small-scale and overlapping symptoms, especially when critical features are obscured by lighting variations, occlusion, and background noise. The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way. First, the MultiCat module employs a multi-scale feature processing strategy with… More >

Xiaozhong Yu^1,2,*, Jinhua Zheng^1,2

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 >

Lin Yuan¹, Qimeng Yu¹, Yao Zhang^2,*, 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 >

Lin Yuan¹, Jingcheng Zhang^2,*, 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 >

U. Muthaiah^1,*, 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 >

Lihan Cui¹, Lijie Yan¹, Xiaohu Zhao¹, Lin Yuan², Jing Jin³, Jingcheng Zhang^1,*

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 >

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 >