Chen Min, Li Haopu, Zhang Zhidong, Ren Ruixian, Wang Zhijiang, Feng Junnan, Cao Riliang, Hu Guangying, Liu Zhenyu
Department of Big Data and Intelligent Engineering, Shanxi Institute of Technology, Yangquan 045000, China.
College of Agricultural Engineering, Shanxi Agricultural University, Taigu, Jinzhong 030801, China.
Animals (Basel). 2025 Sep 5;15(17):2611. doi: 10.3390/ani15172611.
Addressing the carbon footprint in pig production is a fundamental technical basis for achieving carbon neutrality and peak carbon emissions. Only by systematically studying the carbon footprint can the goals of carbon neutrality and peak carbon emissions be effectively realized. This study aims to reduce the carbon footprint through optimized feeding strategies based on minimizing carbon emissions. To this end, this study conducted a full-lifecycle monitoring of the carbon footprint during pig growth from December 2024 to May 2025, optimizing feeding strategies using a real-time pig weight estimation model driven by deep learning to reduce resource consumption and the carbon footprint. We introduce EcoSegLite, a lightweight deep learning model designed for non-contact real-time pig weight estimation. By incorporating ShuffleNetV2, Linear Deformable Convolution (LDConv), and ACmix modules, it achieves high precision in resource-constrained environments with only 1.6 M parameters, attaining a 96.7% mAP50. Based on full-lifecycle weight monitoring of 63 pigs at the Pianguan farm from December 2024 to May 2025, the EcoSegLite model was integrated with a life cycle assessment (LCA) framework to optimize feeding management. This approach achieved a 7.8% reduction in feed intake, an 11.9% reduction in manure output, and a 5.1% reduction in carbon footprint. The resulting growth curves further validated the effectiveness of the optimized feeding strategy, while the reduction in feed and manure also potentially reduced water consumption and nitrogen runoff. This study offers a data-driven solution that enhances resource efficiency and reduces environmental impact, paving new pathways for precision agriculture and sustainable livestock production.
解决生猪生产中的碳足迹问题是实现碳中和和碳达峰的重要技术基础。只有系统地研究碳足迹，才能有效实现碳中和和碳达峰目标。本研究旨在通过基于碳排放最小化的优化饲养策略来减少碳足迹。为此，本研究在2024年12月至2025年5月生猪生长期间对碳足迹进行了全生命周期监测，利用深度学习驱动的实时生猪体重估计模型优化饲养策略，以减少资源消耗和碳足迹。我们引入了EcoSegLite，这是一种专为非接触式实时生猪体重估计设计的轻量级深度学习模型。通过整合ShuffleNetV2、线性可变形卷积（LDConv）和ACmix模块，它在资源受限环境中仅用160万个参数就实现了高精度，平均精度均值（mAP50）达到96.7%。基于2024年12月至2025年5月在偏关农场对63头生猪的全生命周期体重监测，将EcoSegLite模型与生命周期评估（LCA）框架相结合，以优化饲养管理。这种方法使采食量减少了7.8%，粪便产量减少了11.9%，碳足迹减少了5.1%。由此产生的生长曲线进一步验证了优化饲养策略的有效性，同时饲料和粪便的减少也可能降低了用水量和氮径流。本研究提供了一种数据驱动的解决方案，提高了资源效率，减少了环境影响，为精准农业和可持续畜牧生产开辟了新途径。
