Full
Wang Pengjun, E-mail: wangpengjun@caas.cn
Outline
To address difficulties and inefficiencies in monitoring duck weight during the routine inspection of cage-raised meat ducks, this study proposes a precise body weight prediction method utilizing a tree-based machine learning algorithm, aiming to enhance the stability and accuracy of weight prediction in complex scenarios of large-scale meat duck production. Unilateral body size and age data were manually collected from meat ducks at Shuyang Xinfeng Future Farm to develop an extreme gradient boosting model predicting weight. The model integrated morphometric features and chronological age as predictors to evaluate their respective impacts on body weight estimation. The results indicate a distinct growth trajectory: for ducks 1-13 days, data collection is hindered by their small body size, and weight growth is relatively slow, making this period less critical for weight prediction modeling. Conversely, ducks aged 14 to 35 days undergo a rapid growth phase, representing a critical period in the production cycle during which age and unilateral body size serve as pivotal predictors. The Extreme Gradient Boosting Tree model effectively predicted the weight of meat ducks, achieving an optimal R2 of 0.95, a minimum mean squared error (MSE) of 0.563 kg2, and a minimum mean absolute error (MAE) of 0.044 kg. Furthermore, by evaluating the importance of unilateral body size data of meat ducks, it is concluded that body slant length, as the main parameter of meat duck development, contributes the most to the model among body size data, accounting for 20.7% (second only to age) while tibia length exhibited the weakest correlation, contributing only 4.8%. This study not only deepens the understanding of machine learning application in meat duck weight prediction but also provides a valuable method for selection and management in non-contact meat duck weight analysis scenarios. Additionally, it offers technical support for improving model accuracy and promoting the development of intelligent cage-raised meat duck farming, and provides an effective automated solution for weight monitoring via inspection robots.
48
13
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
