|
|
|
|
|
Wenxiao Cao, Guoming Li, Hongfei Song, Boyu Quan and Zilu Liu
Water control of grain has always been a crucial link in storage and transportation. The resistance method is considered an effective technique for quickly detecting moisture in grains, making it particularly valuable in practical applications at drying ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Wenbo Peng and Jinjie Huang
Current object detection methods typically focus on addressing the distribution discrepancies between source and target domains. However, solely concentrating on this aspect may lead to overlooking the inherent limitations of the samples themselves. This...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yifan Shang, Wanneng Yu, Guangmiao Zeng, Huihui Li and Yuegao Wu
Image recognition is vital for intelligent ships? autonomous navigation. However, traditional methods often fail to accurately identify maritime objects? spatial positions, especially under electromagnetic silence. We introduce the StereoYOLO method, an ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Oscar Leonardo García-Navarrete, Oscar Santamaria, Pablo Martín-Ramos, Miguel Ángel Valenzuela-Mahecha and Luis Manuel Navas-Gracia
Corn (Zea mays L.) is one of the most important cereals worldwide. To maintain crop productivity, it is important to eliminate weeds that compete for nutrients and other resources. The eradication of these causes environmental problems through the use of...
ver más
|
|
|
|
|
|
|
|
|
|
|
Wenji Yang and Xiaoying Qiu
The damage caused by pests to crops results in reduced crop yield and compromised quality. Accurate and timely pest detection plays a crucial role in helping farmers to defend against and control pests. In this paper, a novel crop pest detection model na...
ver más
|
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
|
Qing Dong, Lina Sun, Tianxin Han, Minqi Cai and Ce Gao
Timely and effective pest detection is essential for agricultural production, facing challenges such as complex backgrounds and a vast number of parameters. Seeking solutions has become a pressing matter. This paper, based on the YOLOv5 algorithm, develo...
ver más
|
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
|
Zhe Yin, Mingkang Peng, Zhaodong Guo, Yue Zhao, Yaoyu Li, Wuping Zhang, Fuzhong Li and Xiaohong Guo
With the advancement of machine vision technology, pig face recognition has garnered significant attention as a key component in the establishment of precision breeding models. In order to explore non-contact individual pig recognition, this study propos...
ver más
|
|
|
|
|
|
|
|
|
|
|
Hailiang Gong, Xi Wang and Weidong Zhuang
This study focuses on real-time detection of maize crop rows using deep learning technology to meet the needs of autonomous navigation for weed removal during the maize seedling stage. Crop row recognition is affected by natural factors such as soil expo...
ver más
|
|
|
|
|
|