Electronic aids for use in fastock weighing

Smith, R. A.; Turner, M.J.B.

doi:10.1016/0021-8634(74)90069-9

Cited by 10 publications

(3 citation statements)

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“…With WoW, animals must pass through a crate that is specially designed crate to estimate body mass using continuous averaging techniques; the electronic circuit averages the fluctuating signal while the animal crosses the platform and, once the animal leaves the platform, it registers the average LW value. The weight-averaging circuit involves integration of the incoming electrical signal from the weigher for a period of about 2.5 s (Smith and Turner, 1974). An example of the block diagram of a weighing circuit for a WoW platform used with dairy cows at the exit race of a milking parlor was reported by Filby et al (1979).…”

Section: Measuring Liveweight Changes In Lactating Dairy Ewes With An...mentioning

confidence: 99%

Measuring liveweight changes in lactating dairy ewes with an automated walk-over-weighing system

García

Alhamada

Nascimento

et al. 2021

Journal of Dairy Science

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Monitoring liveweight (LW) is an important part of sound management practices at the individual and flock level (e.g., controlling for nutritional status based on body condition, reproduction, and health-related issues), but it is time consuming and stressful. To our knowledge, no literature has reported on the evaluation of automated weighing systems in dairy sheep as an alternative to conventional static scales. The objective of this research was to evaluate the practical feasibility of using an automated walk-over-weighing (WoW) prototype to measure daily LW changes in dairy ewes without human intervention. We used adult Lacaune dairy ewes in 2 complementary trials conducted indoors. Trial 1 aimed at evaluating the repeatability, precision, and accuracy of LW measures recorded using WoW scales compared with a static scale (the gold standard). Forty-two adult ewes (LW ± standard deviation = 71.3 ± 10.4 kg) were randomly drafted from the main flock and used in a 1-day session. The trial included 3 passages. In each passage, ewes were weighed first on a static scale; once a static position was achieved and LW recorded, they continued the circuit and immediately traversed the WoW scale for an automated LW record. Trial 2 aimed to demonstrate the feasibility of using the WoW device under real-world conditions in a dairy sheep-farming system. The WoW scale was installed in the exit race of the milking parlor and evaluated over 7 wk with adult ewes in mid lactation (n = 93; LW 78.5 ± 8.1 kg). Once the ewes were acclimated to the WoW system, 1 group of ewes (n = 48) continued to receive the same feeding regimen (controls), and the other group (n = 45) underwent a nutritional challenge [challenged; 2 wk of undernutrition and then back to control regimen (refeeding) for 1 wk]. We evaluated the ability of the WoW to detect small changes in LW. We collected LW data (2 weighings per ewe per day) from the WoW after each of the 2 milking sessions (morning and evening). We also obtained LW values by weighing the ewes using a static scale once a week. The automated WoW system showed substantial agreement with the gold standard when assessed using Lin's concordance correlation coefficient and Bland and Altman's method, largely due to high repeatability. The WoW system was adequate for detecting small daily variations in LW during undernutrition and refeeding periods. Misbehaviors resulted in spurious WoW values in trial 2, requiring us to use filtration methods to exclude outlier weights and allow meaningful assessment of small LW changes. The WoW system evaluated here is an alternative to the static scales conventionally used on dairy sheep farms. If sound filtration of raw data is applied, WoW could contribute to the close (daily) monitoring of individual LW without operator intervention (i.e., voluntary weighing) and taking animal welfare into account (i.e., no stress related to the weighing session on static scales).

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Section: Measuring Liveweight Changes In Lactating Dairy Ewes With An...mentioning

confidence: 99%

Measuring liveweight changes in lactating dairy ewes with an automated walk-over-weighing system

García

Alhamada

Nascimento

et al. 2021

Journal of Dairy Science

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“…Initially, studies using semi-automatic scales were carried out (SMITH & TURNER, 1974;TURNER & SMITH, 1975) to reduce the time of pigs' weighing as well as faster identification of animals outside a specified body weight.…”

Section: Electronic and Automatic Weighing Systemsmentioning

confidence: 99%

“…In general, the electronic and automatic weighing systems involve direct contact with the animal with electronic boards. They can be used in the form of semi-automatic scales (SMITH & TURNER, 1974), significantly reducing the time of weighing; in the form of automatic feeders with automatic scale (SLADER & GREGORY, 1988;RAMAEKERS et al .1995;, and can be successfully used for individual monitoring of pigs in a herd, reducing the time spent on the process.…”

Section: Evaluation Of a Depth Sensor For Growing And Finishing Pigs`mentioning

confidence: 99%

Automatic assess of growing-finishing pigs\' weight through depth image analysis

Condotta¹

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Automatic assess of growing-finishing pigs' weight through depth image analysis A method of continuously monitoring weight would aid producers by ensuring all pigs are gaining weight and increasing the precision of marketing pigs thus saving money. Electronically monitoring weight without moving the pigs to the scale would eliminate a stress-generating source. Therefore, the development of methods for monitoring the physical conditions of animals from a distance appears as a necessity for obtaining data with higher quality. In pigs' production, animals' weighing is a practice that represents an important role in the control of the factors that affect the performance of the herd and it is an important factor on the production's monitoring. Therefore, this research aimed to extract weight data of pigs through depth images. First, a validation of 5 Kinect® depth sensors was completed to understand the accuracy of the depth sensors. In addition, equations were generated to correct the dimensions' data (length, area and volume) provided by these sensors for any distance between the sensor and the animals. Depth images and weights of finishing pigs (gilts and barrows) of three commercial lines (Landrace, Duroc and Yorkshire based) were acquired. Then, the images were analyzed with the MATLAB software (2016a). The pigs on the images were selected by depth differences and their volumes were calculated and then adjusted using the correction equation developed. Also, pigs' dimensions were acquired for updating existing data. Curves of weight versus corrected volumes and corrected dimensions versus weight were adjusted. Equations for weight predictions through volume were adjusted for gilts and barrows and for each of the three commercial lines used. A reduced equation for all the data, without considering differences between sexes and genetic lines was also adjusted and compared with the individual equations using the Efroymson's algorithm. The result showed that there was no significant difference between the reduced equation and the individual equations for barrows and gilts (p<0.05), and the global equation was also no different than individual equations for each of the three sire lines (p<0.05). The global equation can predict weights from a depth sensor with an R² of 0,9905. Therefore, the results of this study show that the depth sensor would be a reasonable approach to continuously monitor weights.

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