Treffer: Real-Time Pig Weight Assessment and Carbon Footprint Monitoring Based on Computer Vision.

Simple Summary: Pig farming is essential for the food supply, but it also contributes to greenhouse gas emissions. Manual weighing is labor-intensive and can cause stress to animals, potentially spreading disease. We developed a camera-based, non-contact system that utilizes a lightweight deep learning model (EcoSegLite) to segment the pig's back in images and a machine learning model to estimate body weight in real-time. Using full-cycle monitoring of 63 pigs on a commercial farm, we integrated weight estimates with feeding adjustments and a life-cycle assessment of emissions. The system achieved accurate weight prediction (average error 3.2 kg) and enabled precision feeding that reduced feed use by 7.8%, manure output by 11.9%, and the carbon footprint per kilogram of live pig by 5.1%. Because the model is computationally light, it is suitable for deployment on farms and reduces handling, which benefits animal welfare. This approach offers a practical path to improve efficiency and lower emissions in sustainable pig production. 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. [ABSTRACT FROM AUTHOR]