Cattle Feeding Experiment and Chopping Device Parameter Determination for Mechanized Harvesting of Forage Rape Crop

2021 ◽  
Vol 64 (2) ◽  
pp. 715-725
Author(s):  
Xingyu Wan ◽  
Qingxi Liao ◽  
Yajun Jiang ◽  
Yitao Liao

HighlightsForage rape crop could effectively alleviate the lack of green forage for livestock in winter.With the growth of forage rape crop, stem lignification was exacerbated and its palatability degenerated.The relationship between particle length and palatability was explored in a cattle feeding experiment.Optimal working parameters of the chopping device were obtained for harvesting the crop in different stages.Abstract. Forage rape crop, which uses the immature plant leaf and stem of a hybrid rape crop (Brassica napus L.) with low erucic acid and glucosinolate to feed livestock, is an innovative fresh-fed feed material with the advantages of high yield, low cost, rich nutrients, and vigorous growth in winter. In this work, a systematic study was carried out on the relationships among the characteristics of forage rape crop stems, chopping device parameters of the harvester, feeding performance, and chopped particle length (PL) in different growth stages. The results of the stem characteristics tests indicated that stem lignification occurred and increased with growth of the crop from the bolting stage to the silique stage, leading to degeneration of its palatability. The cattle feeding experiment showed that when the bolting rape crop was used, the average feed intake of the cattle fed the chopped rape crop increased by 33.35%, compared to feeding the whole crop without chopping, while the average feeding time decreased by 35.44%. Further experiments on the effects of PL after chopping on feeding performance in different growth stages showed that the optimal PL values in the bolting, flowering, and silique stages were 80, 60, and 30 mm, respectively. Finally, the corresponding cutterhead rotational speeds of the chopping device were calculated as 450, 510, and 1200 r min-1, respectively. This study provides a reference for the development and application of harvesting equipment for forage rape crop. Keywords: Agricultural mechanization, Cattle feeding, Forage palatability, Harvester, Parameter matching.

1997 ◽  
Vol 99 (1) ◽  
pp. 185-189
Author(s):  
Wen-Shaw Chen ◽  
Kuang-Liang Huang ◽  
Hsiao-Ching Yu

2013 ◽  
Vol 39 (5) ◽  
pp. 919 ◽  
Author(s):  
Bo MING ◽  
Jin-Cheng ZHU ◽  
Hong-Bin TAO ◽  
Li-Na XU ◽  
Bu-Qing GUO ◽  
...  

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.


2021 ◽  
Author(s):  
Xianhong Huang ◽  
Zhixin Wang ◽  
Jianliang Huang ◽  
Shaobing Peng ◽  
Dongliang Xiong

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