Qi Li

Mar 23, 2025 | mdpi.com | Yuqing Zhang |Lele Li |Qiao Yu |Qi Li

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Dec 27, 2024 | mdpi.com | Qi Li

Open AccessArticle by Xi Li 1,2, Zunyan Wang 1,2, Yiyong Chen 3,* and Qi Li 1,2,* 1College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China 2Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi’an 710127, China 3Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China *Authors to whom correspondence should be addressed. Int. J. Mol. Sci.

Nov 4, 2024 | nature.com | Dongxu Li |Baoshan Li |Qi Li |Yueming Wang |Mei Yang |Mingshuo Han

In farming scenarios, cattle identification has become a key issue for the development of precision farming. In precision livestock farming, single-feature recognition methods are prone to misjudgment in complex scenarios involving multiple cattle obscuring each other during drinking and feeding. This paper proposes a decision-level identification method based on the multi-feature fusion of cattle faces, muzzle patterns, and ear tags. The method utilizes the SOLO algorithm to segment images and employs the FaceNet and PP-OCRv4 networks to extract features for the cattle’s faces, muzzle patterns, and ear tags. These features are compared with the Ground truth, from which the Top 3 features are extracted. The corresponding cattle IDs of these features are then processed using One-Hot encoding to serve as the final input for the decision layer, and various ensemble strategies are used to optimize the model. The results show that using the multimodal decision fusion method makes the recognition accuracy reach 95.74%, 1.4% higher than the traditional optimal unimodal recognition accuracy. The verification rate reaches 94.72%, 10.65% higher than the traditional optimal unimodal recognition verification rate. The research results demonstrate that the multi-feature fusion recognition method has significant advantages in drinking and feeding farm environments, providing an efficient and reliable solution for precise identification and management of cattle in farms and significantly improving recognition accuracy and stability.

Sep 27, 2024 | mdpi.com | Wenfeng Hou |Dong Wang |Yanan Li |Qi Li

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Sep 24, 2024 | pubs.rsc.org | Qi Li |Christopher Jones

Syn-1,2-Diaminobenzocyclobutenes from [2+2] cycloaddition of 2-imidazolones with arynes Formal [2+2] cycloaddition of arynes with 2-imidazolones affords syn-1,2-diaminobenzocyclobutenes. The transformation can also be conducted as a one-pot, three-stage process direct from simple propargyl amines and isocyanates to afford the new stereochemically defined benzocyclobutene frameworks.