Progress of Machine Vision Technologies in Intelligent Dairy Farming

Zhang, Yongan; Zheng, Qian; Zhang, Lina; Li, Jia; Li, Meian; Li, Yanqiu; Shi, Yu

doi:10.3390/app13127052

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…The NIRS prediction models for estimating the OMD 9 of the total mixed ration of dairy cows based on chemical parameters showed superior predictive capacity than empirical equations.…”

Section: Duplessis Et Al [103]mentioning

confidence: 98%

“…Deep learning (DL) is a branch of AI that can automatically extract features via learning algorithms, and it is applied to complicated computer vision tasks such as detection, classification, recognition, and tracing [8]. Machine vision technology that is mainly based on image and video processing can produce real-time responses and carry out judgments relative to various animal behaviors [9].…”

Section: Introductionmentioning

confidence: 99%

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A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

Liu,

Qi,

et al. 2023

Agriculture

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Milk production plays an essential role in the global economy. With the development of herds and farming systems, the collection of fine-scale data to enhance efficiency and decision-making on dairy farms still faces challenges. The behavior of animals reflects their physical state and health level. In recent years, the rapid development of the Internet of Things (IoT), artificial intelligence (AI), and computer vision (CV) has made great progress in the research of precision dairy farming. Combining data from image, sound, and movement sensors with algorithms, these methods are conducive to monitoring the behavior, health, and management practices of dairy cows. In this review, we summarize the latest research on contact sensors, vision analysis, and machine-learning technologies applicable to dairy cattle, and we focus on the individual recognition, behavior, and health monitoring of dairy cattle and precise feeding. The utilization of state-of-the-art technologies allows for monitoring behavior in near real-time conditions, detecting cow mastitis in a timely manner, and assessing body conditions and feed intake accurately, which enables the promotion of the health and management level of dairy cows. Although there are limitations in implementing machine vision algorithms in commercial settings, technologies exist today and continue to be developed in order to be hopefully used in future commercial pasture management, which ultimately results in better value for producers.

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“…The NIRS prediction models for estimating the OMD 9 of the total mixed ration of dairy cows based on chemical parameters showed superior predictive capacity than empirical equations.…”

Section: Duplessis Et Al [103]mentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

Liu,

Qi,

et al. 2023

Agriculture

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show abstract

“…This limitation of surveys can be overcome by novel data streams such as video surveillance data. With recent advancements in computer vision for agriculture automation (Tian et al, 2020), cameras have been more frequently suggested as a potential equipment for establishing an intelligent dairy farm system for cattle management (Niall et al, 2019;Zhang et al, 2023). As the udder is the key entry point for spore contamination, monitoring the cleanliness of teats and udders would be critical.…”

Section: Integration Of Novel Data Streams With State-of-art Machine ...mentioning

confidence: 99%

A machine learning approach reveals that spore levels in organic bulk tank milk are dependent on farm characteristics and meteorological factors

Qian,

Lee,

Weachock

et al. 2024

Preprint

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Bacterial spores in raw milk can lead to quality issues in milk and milk derived products. Since these spores originate from farm environments, it is important to understand contributions of farm-level factors to spore levels in raw milk. Identifying highly influential factors will guide interventions to control the transmission of spores from farm environments into bulk tank raw milk and therefore minimize spoilage in the finished products. The objective of this study was to investigate the impact of farm management practices and meteorological factors on levels of different spore types in organic raw milk by leveraging machine learning models. In this study, raw milk from certified organic dairy farms (n = 102) located across 11 states was collected 6 times over a year and tested for standard plate count, psychrotolerant spore count, mesophilic spore count, thermophilic spore count, and butyric acid bacteria. At each sampling date, a survey was collected from each farm to obtain structured data about farm management practices. Meteorological factors related to temperature, precipitation, solar radiation, and wind were obtained on the date of sampling as well as 1, 2, and 3 days prior to the date of sampling from an open-source website. The dataset was stratified separately based on the use of a parlor for milking, number of years since organic certification, and whether the lactating herd was exposed to pasture time into sub-datasets to address the potential confounders. Using the entire datasets and 6 sub-datasets respectively, we constructed random forest regression models to predict log10mesophilic spore count, log10thermophilic spore count, and log10butyric acid bacteria most probable number as well as a random forest classification model to classify the presence of psychrotolerant spores in each raw milk sample. The summary statistics showed that spore levels vary considerably between certified organic farms but were only slightly higher than spore levels previously reported from conventional dairy farms. The variable importance plots from the random forest models suggest that herd size, certification year, employee-related variables (e.g., number of people milking cows per week), clipping and flaming udders, stocking density, and principal component representing air temperatures are among the top variables influencing the spore levels in organic raw milk, despite the limitation in the model performance (the highest performance for regression and classification is R2of 0.36 for predicting TSC for farms with a parlor and accuracy of 0.73 for classifying positive PSC for farms without a parlor, respectively). The relatively small effects of top variables as demonstrated by the partial dependence plots suggest that an individualized approach that synergistically considers multiple farm and environmental factors is needed to enable a risk-based approach for managing spore levels. While at the current stage, these models were insufficiently accurate to be used as predictive tools, incorporating novel data streams such as video surveillance and daily farm observations with computer vision and natural language processing, respectively, has the potential to enhance the performance of the model as a real-time monitoring tool for spores as an indicator of milk microbiological quality.

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Designing AI-Based Non-invasive Method for Automatic Detection of Bovine Mastitis

Lakshitha,

Sajja

2024

Communications in Computer and Information Science

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Progress of Machine Vision Technologies in Intelligent Dairy Farming

Cited by 5 publications

References 54 publications

A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

A machine learning approach reveals that spore levels in organic bulk tank milk are dependent on farm characteristics and meteorological factors

Designing AI-Based Non-invasive Method for Automatic Detection of Bovine Mastitis

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