Journal Article10.1016/j.compag.2022.106848
A lightweight deep learning model for cattle face recognition
Zheng Li,Xue Lei,Shuang Yan Liu +2 more
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TL;DR: Li et al. as discussed by the authors proposed a lightweight neural network that can be deployed in embedded systems that requires a small amount of weight representations and low-cost operators, which achieved high recognition accuracy for cattle face recognition and significantly reduced the computational cost.
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About: This article is published in Computers and Electronics in Agriculture. The article was published on 01 Apr 2022. The article focuses on the topics: Computer science & Computer science.
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Citations
When Mobilenetv2 Meets Transformer: A Balanced Sheep Face Recognition Model
TL;DR: Deploying MobileViTFace on the Jetson Nano-based edge computing platform, real-time and accurate recognition results are obtained, which has implications for practical production.
Deep learning-based multi-cattle tracking in crowded livestock farming using video
Shujie Han,Alvaro Fuentes,Sook Yoon,Yongchae Jeong,Hyong Suk Kim,Dong Sun Park +5 more
TL;DR: This paper proposes a deep learning-based framework for multi-cattle tracking in crowded livestock farming using video, addressing scale variations, random motion, and occlusion with improved techniques, achieving 84.49% accuracy in data association.
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Sheep face image dataset and DT-YOLOv5s for sheep breed recognition
TL;DR: Zhang et al. as discussed by the authors proposed a sheep breed recognition model DT-YOLOv5s based on knowledge distillation for the dataset, which transferred the knowledge of sheep face features learned by the teacher network, which has a large number of parameters but high recognition accuracy, to the lightweight student network through a 15-dimensional high-dimensional semantic feature vector, achieving the purpose of lightweight and accurate recognition of the overall network model.
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Classification of seed corn ears based on custom lightweight convolutional neural network and improved training strategies
Xiang Ma,Yonglei Li,Lipengcheng Wan,Ze-fang Xu,Jiannong Song,Jinqiu Huang +5 more
TL;DR: In this paper , the authors proposed a deep learning model (CornNet) based on custom lightweight CNN and improved training strategies for corn ears classification to address this issue, which achieved a good balance between performance and computational cost.
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State-of-the-art AI-enabled mobile device for real-time water stress detection of field crops
Narendra Chandel,Subir Kumar Chakraborty,Abhilash K. Chandel,Kumkum Dubey,Subeesh A,Dilip Jat,Yogesh Anand Rajwade +6 more
TL;DR: Researchers developed an AI-enabled mobile device for real-time water stress detection in field crops, achieving 92.9% and 97.9% accuracy in wheat and maize, respectively, with results displayed in 200 ms, enabling timely decision-making for crop breeders and growers.
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