Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms

• URL: http://arxiv.org/abs/2307.01383v1
• Date: Mon, 3 Jul 2023 22:27:37 GMT
• Title: Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms
• Authors: Ye Bi, Leticia M.Campos, Jin Wang, Haipeng Yu, Mark D.Hanigan, Gota Morota
• Abstract summary: The aim of this study was to predict cow body weight from repeatedly measured video data. Three approaches were investigated, including single thresholding, adaptive thresholding, and Mask R-CNN. Our results suggest that deep learning-based segmentation improves the prediction performance of cow body weight from longitudinal depth video data.
• Score: 3.6809995018369936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monitoring cow body weight is crucial to support farm management decisions due to its direct relationship with the growth, nutritional status, and health of dairy cows. Cow body weight is a repeated trait, however, the majority of previous body weight prediction research only used data collected at a single point in time. Furthermore, the utility of deep learning-based segmentation for body weight prediction using videos remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data, to compare the performance of the thresholding and Mask R-CNN deep learning approaches, to evaluate the predictive ability of body weight regression models, and to promote open science in the animal science community by releasing the source code for video-based body weight prediction. A total of 40,405 depth images and depth map files were obtained from 10 lactating Holstein cows and 2 non-lactating Jersey cows. Three approaches were investigated to segment the cow's body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric features, such as dorsal length, abdominal width, height, and volume, were estimated from the segmented images. On average, the Mask-RCNN approach combined with a linear mixed model resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.98 and 2.03%, respectively, in the forecasting cross-validation. The Mask-RCNN approach was also the best in the leave-three-cows-out cross-validation. The prediction coefficients of determination and mean absolute percentage error of the Mask-RCNN coupled with the linear mixed model were 0.90 and 4.70%, respectively. Our results suggest that deep learning-based segmentation improves the prediction performance of cow body weight from longitudinal depth video data.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Comparison of gait phase detection using traditional machine learning and deep learning techniques [3.11526333124308]
  This study proposes a few Machine Learning (ML) based models on lower-limb EMG data for human walking. The results show up to 75% average accuracy for traditional ML models and 79% for Deep Learning (DL) model.
  arXiv  Detail & Related papers  (2024-03-07T10:05:09Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Prawn Morphometrics and Weight Estimation from Images using Deep Learning for Landmark Localization [2.778518997767646]
  We developed a novel approach to automate weight estimation and morphometric analysis using the black tiger prawn (Penaeus monodon) as a model crustacean. For morphometric analyses, we utilized the detected landmarks to derive five important prawn traits. Our experimental results demonstrate that the novel DL approach outperforms existing DL methods in terms of accuracy, robustness, and efficiency.
  arXiv  Detail & Related papers  (2023-07-15T07:05:06Z)
• Masked Autoencoding for Scalable and Generalizable Decision Making [93.84855114717062]
  MaskDP is a simple and scalable self-supervised pretraining method for reinforcement learning and behavioral cloning. We find that a MaskDP model gains the capability of zero-shot transfer to new BC tasks, such as single and multiple goal reaching.
  arXiv  Detail & Related papers  (2022-11-23T07:04:41Z)
• Segmentation Enhanced Lameness Detection in Dairy Cows from RGB and Depth Video [8.906235809404189]
  Early lameness detection helps farmers address illnesses early and avoid negative effects caused by the degeneration of cows' condition. We collected a dataset of short clips of cows exiting a milking station and annotated the degree of lameness of the cows. We proposed a lameness detection method that leverages pre-trained neural networks to extract discriminative features from videos and assign a binary score to each cow indicating its condition: "healthy" or "lame"
  arXiv  Detail & Related papers  (2022-06-09T12:16:31Z)
• T-LEAP: occlusion-robust pose estimation of walking cows using temporal information [0.0]
  Lameness, a prevalent health disorder in dairy cows, is commonly detected by analyzing the gait of cows. A cow's gait can be tracked in videos using pose estimation models because models learn to automatically localize anatomical landmarks in images and videos. Most animal pose estimation models are static, that is, videos are processed frame by frame and do not use any temporal information.
  arXiv  Detail & Related papers  (2021-04-16T10:50:56Z)
• Breast mass detection in digital mammography based on anchor-free architecture [0.4568777157687961]
  We propose a one-stage object detection architecture, called Breast Mass Detection Network (BMassDNet) BMassDNet is based on anchor-free and feature pyramid which makes the detection of breast masses of different sizes well adapted. We show that the proposed BMassDNet can obtain competitive detection performance over the current top ranked methods.
  arXiv  Detail & Related papers  (2020-09-02T07:11:16Z)
• Deep Learning for Content-based Personalized Viewport Prediction of 360-Degree VR Videos [72.08072170033054]
  In this paper, a deep learning network is introduced to leverage position data as well as video frame content to predict future head movement. For optimizing data input into this neural network, data sample rate, reduced data, and long-period prediction length are also explored for this model.
  arXiv  Detail & Related papers  (2020-03-01T07:31:50Z)
• Assessing Graph-based Deep Learning Models for Predicting Flash Point [52.931492216239995]
  Graph-based deep learning (GBDL) models were implemented in predicting flash point for the first time. Average R2 and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3% lower and 2.0 K higher than previous comparable studies.
  arXiv  Detail & Related papers  (2020-02-26T06:10:12Z)
• Anatomy-aware 3D Human Pose Estimation with Bone-based Pose Decomposition [92.99291528676021]
  Instead of directly regressing the 3D joint locations, we decompose the task into bone direction prediction and bone length prediction. Our motivation is the fact that the bone lengths of a human skeleton remain consistent across time. Our full model outperforms the previous best results on Human3.6M and MPI-INF-3DHP datasets.
  arXiv  Detail & Related papers  (2020-02-24T15:49:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.