Multi-modal machine learning for the early detection of metabolic disorder in dairy cows using a cloud computing framework

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Rafael E.P. Ferreira , Maria Angels de Luis Balaguer , Tiago Bresolin , Ranveer Chandra , Guilherme J.M. Rosa , Heather M. White , João R.R. Dórea
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引用次数: 0

Abstract

In precision livestock farming (PLF), wearable sensors, computer vision, and genomic tests generate large amounts of data, which can be challenging to integrate and analyze jointly due to their diverse nature. However, incorporating both genomic and phenotypic data together can be beneficial for developing predictive models in animal biology. The development of automated and modular data pipelines using scalable solutions such as cloud computing can be an effective strategy to integrate and analyze animal-level information in real-time. The objectives of this study were (1) to propose a cloud computing-based framework to automate the processing and integration of phenotypic and genotypic data, and (2) to assess different data fusion strategies (early and late fusion, and cooperative learning) for the early detection of subclinical ketosis (SCK) in dairy cows, integrating wearable sensors, imaging systems, and genotypic data in livestock farms. We developed a modular pipeline for image analysis, which includes body segmentation, frame quality assessment, animal identification, and body condition score (BCS), which were crucial for producing the features used in SCK detection. The body segmentation module achieved a Dice similarity coefficient of 0.990, the frame quality assessment module reached 99.1 % accuracy, the animal identification module attained 93.2 % accuracy, and the BCS module achieved accuracies of 81.1 % and 96.2 % when allowing up to 0.25 and 0.50 prediction error, respectively. For SCK detection, early fusion and cooperative learning achieved the lowest mean absolute errors in predicting plasma beta-hydroxybutyrate as a continuous variable (as low as 0.242). Late fusion, combined with an ordinary least squares regression, achieved the highest F1 scores for binary SCK prediction (up to 0.750). These results suggest that data fusion techniques can be effectively used to integrate genotypic and phenotypic data from multiple sensors. Additionally, SCK detection can be performed on dairy farms using the proposed cloud computing-based framework, which was implemented with modular, independent services that can be customized and reused for a variety of tasks.
利用云计算框架进行多模态机器学习以早期检测奶牛的代谢紊乱症
在精准畜牧业(PLF)中,可穿戴传感器、计算机视觉和基因组测试会产生大量数据,由于这些数据的性质各不相同,要对其进行整合和联合分析可能具有挑战性。然而,将基因组数据和表型数据结合在一起有利于开发动物生物学预测模型。利用云计算等可扩展的解决方案开发自动化和模块化数据管道,是实时整合和分析动物级信息的有效策略。本研究的目标是:(1) 提出一个基于云计算的框架,自动处理和整合表型和基因型数据;(2) 评估不同的数据融合策略(早期融合、后期融合和合作学习),整合畜牧场的可穿戴传感器、成像系统和基因型数据,用于奶牛亚临床酮症(SCK)的早期检测。我们开发了一个模块化图像分析流水线,其中包括体表分割、帧质量评估、动物识别和体况评分(BCS),这些对于生成用于 SCK 检测的特征至关重要。身体分割模块的 Dice 相似系数达到了 0.990,帧质量评估模块的准确率达到了 99.1%,动物识别模块的准确率达到了 93.2%,而 BCS 模块在允许最大 0.25 和 0.50 预测误差的情况下,准确率分别达到了 81.1% 和 96.2%。在 SCK 检测方面,早期融合和合作学习在预测血浆 beta-hydroxybutyrate 这一连续变量时取得了最低的平均绝对误差(低至 0.242)。后期融合与普通最小二乘回归相结合,在二元 SCK 预测中取得了最高的 F1 分数(高达 0.750)。这些结果表明,数据融合技术可有效用于整合来自多个传感器的基因型和表型数据。此外,奶牛场还可以利用所提出的基于云计算的框架进行 SCK 检测,该框架采用模块化、独立的服务实现,可针对各种任务进行定制和重复使用。
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来源期刊
Computers and Electronics in Agriculture
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.
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