An Improved Single Shot Multibox Detector Method Applied in Body Condition Score for Dairy Cows

Huang, Xiaoping; Hu, Zhengwei; Wang, Xiaorun; Yang, Xin; Zhang, Jian; Dao-ling, Shi

doi:10.3390/ani9070470

Cited by 42 publications

(15 citation statements)

References 25 publications

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“…Body condition is a significant welfare and herd management indicator. Body condition is in high correlation with the health and metabolic status of the dairy cow and also with milk composition during lactation [ 105 ]. Body condition assessment is an indirect appraisal of the level of body reserves, and deviations reveal aggregate variation in energy balance [ 106 , 107 ].…”

Section: Welfare Of Dairy Cows and Precision Livestock Farmingmentioning

confidence: 99%

Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

Silva

Araújo

Guedes

et al. 2021

Animals

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Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.

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Section: Welfare Of Dairy Cows and Precision Livestock Farmingmentioning

confidence: 99%

Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

Silva

Araújo

Guedes

et al. 2021

Animals

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“…So when there are some limitations and speed constraints, the more important metrics should be taken into account and compared to help to choose the right method [79]. However, there are studies that improve the accuracy of detection and speed of processing to make these suitable for real-time applications [50,52,103].…”

Section: Discussionmentioning

confidence: 99%

“…Kvam and Kongsro [49] proposed a method for estimating the IMF on ultrasound images. A noninvasive in vivo method, constructed using deep CNNs, by (Huang et al [50] and Yukun et al [51] provided a lowcost method based on machine vision and deep learning for evaluation of body condition scores. Zhang et al [52] proposed a real-time sow behavior detection algorithm based on deep learning.…”

Section: Precision Livestock Farmingmentioning

confidence: 99%

“…Compared to original SSD and YOLO-v3, it has a smaller model size with 23.1 MB and faster detection speed with 115 fps. [50] "Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score" Individual identification and BCS assessment An automatic system for identifying individuals and assessing body condition score (BCS) using a deep learning framework…”

Section: Image Samplesmentioning

confidence: 99%

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Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

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Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

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“…Not only that, BCS is also important in farm management [ 9 ]. It was found that BCS at calving and its changes throughout lactation affect the health and fertility of high producing dairy cows [ 10 ]. Cow BCS monitoring is important because it is a measure for the fat reserves of a cow, also suitable in the assessment of feeding management.…”

Section: Introductionmentioning

confidence: 99%

Relation of Automated Body Condition Scoring System and Inline Biomarkers (Milk Yield, β-Hydroxybutyrate, Lactate Dehydrogenase and Progesterone in Milk) with Cow’s Pregnancy Success

Antanaitis

Juozaitienė

Malašauskienė

et al. 2021

Sensors

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The aim of the current study was to evaluate the relation of automatically determined body condition score (BCS) and inline biomarkers such as β-hydroxybutyrate (BHB), milk yield (MY), lactate dehydrogenase (LDH), and progesterone (mP4) with the pregnancy success of cows. The cows (n = 281) had 2.1 ± 0.1. lactations on average, were 151.6 ± 0.06 days postpartum, and were once tested with “Easy scan” ultrasound (IMV imaging, Scotland) at 30–35 d post-insemination. According to their reproductive status, cows were grouped into two groups: non-pregnant (n = 194 or 69.0% of cows) and pregnant (n = 87 or 31.0% of cows). Data concerning their BCS, mP4, MY, BHB, and LDH were collected each day from the day of insemination for 7 days. The BCS was collected with body condition score camera (DeLaval Inc., Tumba, Sweden); mP4, MY, BHB, and LDH were collected with the fully automated real-time analyzer Herd Navigator™ (Lattec I/S, Hillerød, Denmark) in combination with a DeLaval milking robot (DeLaval Inc., Tumba, Sweden). Of all the biomarkers, three differences between groups were significant. The body condition score (BCS) of the pregnant cows was higher (+0.49 score), the milk yield (MY) was lower (-4.36 kg), and milk progesterone in pregnant cows was (+6.11 ng/mL) higher compared to the group of non-pregnant cows (p < 0.001). The pregnancy status of the cows was associated with their BCS assessment (p < 0.001). We estimated that cows with BCS > 3.2 were 22 times more likely to have reproductive success than cows with BCS ≤ 3.2.

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An Improved Single Shot Multibox Detector Method Applied in Body Condition Score for Dairy Cows

Cited by 42 publications

References 25 publications

Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Relation of Automated Body Condition Scoring System and Inline Biomarkers (Milk Yield, β-Hydroxybutyrate, Lactate Dehydrogenase and Progesterone in Milk) with Cow’s Pregnancy Success

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