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. 2024 May 29;14(11):1614.
doi: 10.3390/ani14111614.

Improving Efficiency: Automatic Intelligent Weighing System as a Replacement for Manual Pig Weighing

Affiliations

Improving Efficiency: Automatic Intelligent Weighing System as a Replacement for Manual Pig Weighing

Gaifeng Hou et al. Animals (Basel). .

Abstract

To verify the accuracy of AIWS, we weighed 106 pen growing-finishing pigs' weights using both the manual and AIWS methods, respectively. Accuracy was evaluated based on the values of MAE, MAPE, and RMSE. In the growth experiment, manual weighing was conducted every two weeks and AIWS predicted weight data was recorded daily, followed by fitting the growth curves. The results showed that MAE, MAPE, and RMSE values for 60 to 120 kg pigs were 3.48 kg, 3.71%, and 4.43 kg, respectively. The correlation coefficient r between the AIWS and manual method was 0.9410, and R2 was 0.8854. The two were extremely significant correlations (p < 0.001). In growth curve fitting, the AIWS method has lower AIC and BIC values than the manual method. The Logistic model by AIWS was the best-fit model. The age and body weight at the inflection point of the best-fit model were 164.46 d and 93.45 kg, respectively. The maximum growth rate was 831.66 g/d. In summary, AIWS can accurately predict pigs' body weights in actual production and has a better fitting effect on the growth curves of growing-finishing pigs. This study suggested that it was feasible for AIWS to replace manual weighing to measure the weight of 50 to 120 kg live pigs in large-scale farming.

Keywords: 3D camera; automatic weight measurement; growth curve; large-scale farming; live pigs.

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Conflict of interest statement

The authors declare no conflicts of in Author Xingfu Zhang was employed by the company Beijing Focused Loong Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Schematic diagram of data collection. The distance between the depth camera and the ground is 2010 ± 20 mm.
Figure 2
Figure 2
Correlation analysis of two weight measurement methods.
Figure 3
Figure 3
The growth curves of two models for 50–110 kg pigs in different weighing methods: (a) Logistic model, (b) Gompertz model. AIWS = automatic intelligent weighing system.
Figure 4
Figure 4
The absolute growth rate curve of the first derivative. (a) Growth rate of Logistic model in manual weighing, (b) Growth rate of Logistic model in AIWS weighing, (c) Growth rate of Gompertz model in manual weighing, (d) Growth rate of Gompertz model in AIWS weighing.

References

    1. Chen H.M., Liang Y., Huang H., Huan Q., Gu W., Liang H. Live pig-weight learning and prediction method based on a multilayer RBF network. Agriculture. 2023;13:253. doi: 10.3390/agriculture13020253. - DOI
    1. Pezzuolo A., Guarino M., Sartori L., González L.A., Marinello F. On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera. Comput. Electron. Agric. 2018;148:29–36. doi: 10.1016/j.compag.2018.03.003. - DOI
    1. Schofield C.P. Evaluation of image analysis as a means of estimating the weight of pigs. J. Agric. Eng. Res. 1990;37:287–296. doi: 10.1016/0021-8634(90)80048-Y. - DOI
    1. Wang Y., Yang W., Winte P., Walker L.T. Non-contact sensing of hog weights by machine vision. Appl. Eng. Agric. 2006;22:577–582. doi: 10.13031/2013.21225. - DOI
    1. Wang Z., Shadpour S., Chan E., Rotondo V., Wood K.M., Tulpan D. ASAS-NANP Symposium: Applications of machine learning for livestock body weight prediction from digital images. J. Anim. Sci. 2021;99:1–15. doi: 10.1093/jas/skab022. - DOI - PMC - PubMed

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