344   Artículos

 
en línea
Huizhong Xiong, Xiaotong Gao, Ningyi Zhang, Haoxiong He, Weidong Tang, Yingqiu Yang, Yuqian Chen, Yang Jiao, Yihong Song and Shuo Yan    
A novel deep learning model, DiffuCNN, is introduced in this paper, specifically designed for counting tobacco lesions in complex agricultural settings. By integrating advanced image processing techniques with deep learning methodologies, the model signi... ver más
Revista: Agriculture    Formato: Electrónico

 
en línea
Huiru Zhou, Qiang Lai, Qiong Huang, Dingzhou Cai, Dong Huang and Boming Wu    
The severity of rice blast and its impacts on rice yield are closely related to the inoculum quantity of Magnaporthe oryzae, and automatic detection of the pathogen spores in microscopic images can provide a rapid and effective way to quantify pathogen i... ver más
Revista: Agriculture    Formato: Electrónico

 
en línea
Ping Dong, Kuo Li, Ming Wang, Feitao Li, Wei Guo and Haiping Si    
In addition to the conventional situation of detecting a single disease on a single leaf in corn leaves, there is a complex phenomenon of multiple diseases overlapping on a single leaf (compound diseases). Current research on corn leaf disease detection ... ver más
Revista: Agriculture    Formato: Electrónico

 
en línea
Yuanyuan Shao, Shengheng Ji, Guantao Xuan, Yanyun Ren, Wenjie Feng, Huijie Jia, Qiuyun Wang and Shuguo He    
The objective is to develop a portable device capable of promptly identifying root rot in the field. This study employs hyperspectral imaging technology to detect root rot by analyzing spectral variations in chili pepper leaves during times of health, in... ver más
Revista: Agronomy    Formato: Electrónico

 
en línea
Rujia Li, Yadong Li, Weibo Qin, Arzlan Abbas, Shuang Li, Rongbiao Ji, Yehui Wu, Yiting He and Jianping Yang    
This research tackles the intricate challenges of detecting densely distributed maize leaf diseases and the constraints inherent in YOLO-based detection algorithms. It introduces the GhostNet_Triplet_YOLOv8s algorithm, enhancing YOLO v8s by integrating t... ver más
Revista: Agriculture    Formato: Electrónico

 
en línea
Xuejun Yue, Haifeng Li, Qingkui Song, Fanguo Zeng, Jianyu Zheng, Ziyu Ding, Gaobi Kang, Yulin Cai, Yongda Lin, Xiaowan Xu and Chaoran Yu    
Existing disease detection models for deep learning-based monitoring and prevention of pepper diseases face challenges in accurately identifying and preventing diseases due to inter-crop occlusion and various complex backgrounds. To address this issue, w... ver más
Revista: Agronomy    Formato: Electrónico

 
en línea
Alwyn Tan, Sangeeta Rao and Mo Salman    
Effective animal disease reporting is critical for early disease detection and control, but it is often hindered by various human behavioral barriers. This review outlines a comprehensive approach to understanding and addressing these barriers in animal ... ver más
Revista: Agriculture    Formato: Electrónico

 
en línea
Ruicheng Gao, Zhancai Dong, Yuqi Wang, Zhuowen Cui, Muyang Ye, Bowen Dong, Yuchun Lu, Xuaner Wang, Yihong Song and Shuo Yan    
In this study, a deep-learning-based intelligent detection model was designed and implemented to rapidly detect cotton pests and diseases. The model integrates cutting-edge Transformer technology and knowledge graphs, effectively enhancing pest and disea... ver más
Revista: Agriculture    Formato: Electrónico

 
en línea
Artur Chudzik and Andrzej W. Przybyszewski    
Neurodegenerative diseases (NDs), including Parkinson?s and Alzheimer?s disease, pose a significant challenge to global health, and early detection tools are crucial for effective intervention. The adaptation of online screening forms and machine learnin... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Abdul Rahaman Wahab Sait and Ali Mohammad Alorsan Bani Awad    
Coronary artery disease (CAD) is the most prevalent form of cardiovascular disease that may result in myocardial infarction. Annually, it leads to millions of fatalities and causes billions of dollars in global economic losses. Limited resources and comp... ver más
Revista: Applied Sciences    Formato: Electrónico

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