weight prediction
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Author(s):  
Wenzhi Wang ◽  
Yuan Zhang ◽  
Jie He ◽  
Zhanqi Chen ◽  
Dan Li ◽  
...  

In order to solve the labor-intensive and time-consuming problem in the process of measuring yak body size and weight in yak breeding industry in Qinghai Province, a non-contact method for measuring yak body size and weight was proposed in this experiment, and key technologies based on semantic segmentation, binocular ranging and neural network algorithm were studied to boost the development of yak breeding industry in Qinghai Province. Main conclusions: (1) Study yak foreground image extraction, and implement yak foreground image extraction model based on U-net algorithm; select 2263 yak images for experiment, and verify that the accuracy of the model in yak image extraction is over 97%. (2) Develop an algorithm for estimating yak body size based on binocular vision, and use the extraction algorithm of yak body size related measurement points combined with depth image to estimate yak body size. The final test shows that the average estimation error of body height and body oblique length is 2.6%, and the average estimation error of chest depth is 5.94%. (3) Study the yak weight prediction model; select the body height, body oblique length and chest depth obtained by binocular vision to estimate the yak weight; use two algorithms to establish the yak weight prediction model, and verify that the average estimation error of the model for yak weight is 10.78% and 13.01% respectively.


2021 ◽  
Author(s):  
Yunhan Yang ◽  
Bing Xue ◽  
Linley Jesson ◽  
Matthew Wylie ◽  
Mengjie Zhang ◽  
...  

2021 ◽  
Author(s):  
Mathew Wheto ◽  
Nkiruka Goodness Chima ◽  
Henry T Ojoawo ◽  
Matthew A Adeleke ◽  
Sunday O Peters ◽  
...  

Abstract This study aimed to assess the relationship among carcass traits of meat line FUNAAB Alpha chicken genotype, to identify the components that defined bled weight in them using multivariate principal component regression. A total of 14 different carcass traits from sixty-eight birds were recorded and subjected to one-way analysis of variance to vet for sex effect. Phenotypic relationships among the carcass traits were also established to pave way for the principal component analysis. The results reveal significant effects between the traits measured. The male significantly (P<0.05) had greater mean values for the traits measured. Correlations among the considered carcass traits were found to be positive and significant ranging from r = 0.406 (LrWt) - 0.981 (EdWt) for the female chicken; r = 0.330 (Head Wt) - 0.978 (BdWt) for the male chicken. The extracted components PC1 to PC7 contributed 95.66% with PC1 accounting for 68.68% of the variability in the original parameters. Communality estimates varied from 0.466 (thigh weight) to 0.983 (liver weight). In the principal component regression models, Eviscerated weight accounted for 95% of the variation observed in bled weight. The use of PC1 as a single predictor, explained 96.4% of the variability, whilst combining PC1 and PC4 showed improvements in the variance explained (R2 = 96.7%) with a lower Mallow's cp (5.31). Using the principal components scores from the chicken morphometric traits was more appropriate than using the original traits in bled weight prediction.


Author(s):  
Alexey Ruchay ◽  
Konstantin Dorofeev ◽  
Vsevolod Kalschikov ◽  
Vladimir Kolpakov ◽  
Kinispay Dzhulamanov ◽  
...  

2021 ◽  
Vol 11 (15) ◽  
pp. 7151
Author(s):  
Yijun Hu ◽  
Jingfang Shen ◽  
Yonghao Qi

Rice has long served as the staple food in Asia, and the cultivation of high-yield rice crops draws increasing attention from academic researchers. The prediction of rice growth condition by image features realizes nondestructive prediction and it has great implications for smart agriculture. We found a special image parameter called the fractal dimension that can improve the effect of the prediction model. As an important geometric feature, the fractal dimension could be calculated from the image, but it is rarely used in the field of rice growth prediction. In this paper, we attempt to combine the fractal dimension with traditional rice image features to improve the effect of the model. The thresholding method is used to transform the cropped rice image into binary image, and the box-counting method is used to calculate the fractal dimension of the image. The correlation coefficients are calculated to select the characteristics with a strong correlation with biomass. The prediction models of dry weight, fresh weight and plant height of rice are established by using random forest, support vector regression and linear regression. By evaluating the prediction effect of the model, it can be concluded that the fractal dimension can improve the prediction effect of the model. Among the models obtained by the three methods, the multiple linear regression model has the best comprehensive effect, with the dry weight prediction model R2 reaching 0.8697, the fresh weight prediction model R2 reaching 0.8631 and the plant height prediction model R2 reaching 0.9196. The model established in this paper has a fine effect and has a certain guiding significance in rice research.


2021 ◽  
Vol 299 ◽  
pp. 110501
Author(s):  
Majid Masoumi ◽  
Marcel Marcoux ◽  
Laurence Maignel ◽  
Candido Pomar

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