Accurate assessment of body weights using morphometric measurements in Landlly pigs

Panda, Snehasmita; Gaur, Gyanendra Kumar; Chauhan, Anuj; Kar, Junaid; Mehrotra, Arnav

doi:10.1007/s11250-021-02803-2

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…Numerous other studies, which usually model weight rather than the cube root of weight, have also reported heart girth to be the highly predictive of weight (Groesbeck et al, 2002 ; Iwasawa et al, 2004 ; Machebe and Ezekwe, 2010 ; Mutua et al, 2010 . ; Paras et al, 2015 ; Panda et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…In addition to the studies described here, numerous other studies have been undertaken on the relationship between pig weights and body measurements (Al Ard Khanji et al, 2018 ; Groesbeck et al, 2002 ; Kumari et al, 2020 ; Iwasawa et al, 2004 ; Machebe et al, 2010 ; Murillo and Valdez, 2004 ; Panda et al, 2021 ; Paras and Cu-Cordoves, 2015 ; Sungirai et al, 2014 ). However, these were conducted outside of East Africa ánd are thus less relevant due to differences in management strategies, particularly feeding regimes, as well as breed types (Ugandan pigs are typically crosses, including of the local “Ugandan” breed).…”

Section: Resultsmentioning

confidence: 99%

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A farmer-friendly tool for estimation of weights of pigs kept by smallholder farmers in Uganda

Marshall

Poole

Oyieng

et al. 2023

Trop Anim Health Prod

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Pig keeping is important to the livelihoods of many rural Ugandans. Pigs are typically sold based on live weight or a carcass weight derived from this; however this weight is commonly estimated due to the lack of access to scales. Here, we explore the development of a weigh band for more accurate weight determination and potentially increased farmer bargaining power on sale price. Pig weights and varied body measurements (heart girth, height, and length) were collected on 764 pigs of different ages, sex, and breed types, from 157 smallholder pig keeping households in Central and Western Uganda. Mixed-effects linear regression analyses, with household as a random effect and the varied body measurements as a fixed effect, were performed to determine the best single predictor for cube root of weight (transformation of weight for normality), for 749 pigs ranging between 0 and 125 kg. The most predictive single body measurement was heart girth, where weight in kg = (0.4011 + heart girth in cm × 0.0381)3. This model was found to be most suitable for pigs between 5 and 110 kg, notably more accurate than farmers’ estimates, but still with somewhat broad confidence intervals (for example, ±11.5 kg for pigs with a predicted weight of 51.3 kg). We intend to pilot test a weigh band based on this model before deciding on whether it is suitable for wider scaling.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A farmer-friendly tool for estimation of weights of pigs kept by smallholder farmers in Uganda

Marshall

Poole

Oyieng

et al. 2023

Trop Anim Health Prod

View full text Add to dashboard Cite

show abstract

“…Several studies have demonstrated a correlation between pig body measurement, body weight, and other growth parameters. A relationship model can be used to estimate the body weight of pigs in an effective manner [ 31 , 32 ]. Body size parameters were extracted based on critical points along the back contour of the pigs, and a relationship model between body size and body weight was established.…”

Section: Methodsmentioning

confidence: 99%

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Liu,

Hua,

Xue

et al. 2023

Sensors

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The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig’s back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding.

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“…The morphometric measurements observed are hip height and width, rump length, ankle circumference, body length, shoulder height, chest width, and depth, as well as head index measurements such as the length and width. There are few studies on body morphometrics and head index of pigs (Panda et al, 2021;Prheeti et al, 2023;Baruzzi et al, 2023), however, to our knowledge such type of data is missing for Bali pig breed. This study aims to determine the morphometric characteristics and head index of Bali pigs using a Principal Component Analysis (PCA) approach to identify the phenotypic characteristics of Bali pigs.…”

Section: Identification Of Bali Pigs Using Body Morphometric and Head...mentioning

confidence: 97%

Identification of Bali Pigs Using Body Morphometric and Head Index by Applying Principal Component Analysis (PCA) Approach

Heryani¹,

Setiasih²,

Susari³

et al. 2022

JAHP

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Bali pig is an important livestock for meeting the meat needs of the community, especially in the Province of Bali, Indonesia. The characteristics of this animal include a slightly rough black fur, a long snout, curved back, and their stomach does not touch the ground when standing. This study aims to determine the morphometric characteristics and head index of Bali pigs using a Principal Component Analysis (PCA) approach to easily identify the phenotypic characteristics. The samples include 50 Bali pigs that are intensively reared in Musi Village, Gerogak District, Buleleng Regency, Bali Province, Indonesia. The measurement data were analyzed using PCA to reduce and interpret the measured variables. The results showed that the average hip height and width, rump length, body length, shoulder height, chest width and depth, head width and length were 55.68cm, 17.53cm, 10.62cm, 67.40cm, 54.92cm, 17.81cm, 41.24cm, 10.63cm, and 26.26cm, respectively. Furthermore, the result of measuring the index of the female and male heads was 40.76% and 39.41%. The results showed that Principal Component 1 (KU1) and 2 (KU2) had a variance of 5.834 and 1.834, respectively, while the percentage of variance was 58.338% and 18.340%, with a total value of 76.678%. The hip width, shoulder height, and chest width were significantly different (p<0.05) between male and female Bali pigs. In addition, the main characteristics of size for this specie include hip height, rump length, body length, shoulder height, chest width, and head width and length, while that of the shape include chest and hip-width

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Accurate assessment of body weights using morphometric measurements in Landlly pigs

Cited by 13 publications

References 7 publications

A farmer-friendly tool for estimation of weights of pigs kept by smallholder farmers in Uganda

A farmer-friendly tool for estimation of weights of pigs kept by smallholder farmers in Uganda

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Identification of Bali Pigs Using Body Morphometric and Head Index by Applying Principal Component Analysis (PCA) Approach

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