Weighing finishing pigs in motion: A walk-over scale for accurate weight estimation

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2025-02-06 DOI:10.1016/j.compag.2025.110019

François Decarie , Charles Grant , Gabriel Dallago

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引用次数: 0

Abstract

Accurate and efficient weight estimation of pigs is crucial for optimizing production, ensuring animal welfare, and making informed decisions in swine farming. Despite technological advancements, obtaining precise individual pig weights remains challenging due to the dynamic nature of pig movement and the stress induced by traditional weighing methods, highlighting the need for innovative, non-invasive solutions. This study presents an automated walk-over scale system that leverages high-frequency load cell data, feature engineering, and machine learning techniques to estimate pig weights in motion, addressing the limitations of traditional weighing methods. The system’s effectiveness was validated in a real-world setting with 50 pigs across 944 walk-throughs, achieving a Root Mean Square Error (RMSE) of 2.87 kg and a Mean Absolute Percentage Error (MAPE) of 2.65% on a 20% pig-wise holdout validation set, demonstrating its potential as a practical solution for non-invasive, accurate weight monitoring in commercial pig farming operations.

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来源期刊

Computers and Electronics in Agriculture 工程技术-计算机：跨学科应用

CiteScore

15.30

自引率

14.50%

发文量

800

审稿时长

62 days

期刊介绍： Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.