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Pig Weight Estimation According to RGB Image Analysis 基于RGB图像分析的猪体重估计
Pub Date : 2023-05-30 DOI: 10.18690/agricsci.20.1.6
Andras Kárpinszky, Gergely Dobsinszki
In pig farming, knowing the exact weight of each animal is critical for the owner. Such information can help determine the amount and type of feed that needs to be fed to a specific fattening pig. Weighing pigs has always been problematic, because it is highly time consuming, and herding the pigs on the scale is extremely cumbersome. Moreover, it causes stress to the animals. The aim of our study was to build an RGB-based system that could estimate the daily weight of pigs and individual animal weight. The study was set up in a 100-day rotation in a commercial pig farm where we monitored 32 pigs. We developed a system to identify the features of the pigs, more particularly the head, shoulder, belly, and rump part. Three different modelswere tested, and their main differences were linked to image processing and training data. Using these models, we received higher than 97% accuracy between the predicted and the manually recorded weight of the animals. This system allows owners to manage and monitor their pigs using our web interface, allowing them to make crucial decisions during the farming process.
在养猪业中,知道每头猪的确切体重对养猪户来说至关重要。这些信息可以帮助确定需要喂给特定育肥猪的饲料的数量和类型。给猪称重一直是个问题,因为这非常耗时,而且把猪放在秤上非常麻烦。此外,它会给动物带来压力。本研究的目的是建立一个基于rgb的系统,可以估计猪的日重和个体动物的体重。这项研究是在一个商业养猪场进行的,为期100天,我们对32头猪进行了监测。我们开发了一个系统来识别猪的特征,特别是头、肩、腹部和臀部。测试了三种不同的模型,它们的主要差异与图像处理和训练数据有关。使用这些模型,我们在预测和人工记录的动物体重之间获得了高于97%的准确性。该系统允许饲主使用我们的网络界面管理和监控他们的猪,使他们能够在养殖过程中做出关键决策。
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引用次数: 0
Composition of Proteins and Phenolics in the Leaves of Different Mulberry Species (Morus alba L., M. alba × rubra, M. australis Poir., M. nigra L.) 不同桑树(Morus alba L., M. alba × rubra, M. australis Poir.)叶片中蛋白质和酚类物质的组成。, M. nigra L.)
Pub Date : 2023-05-30 DOI: 10.18690/agricsci.20.1.3
Špela Jelen, Andreja Urbanek Krajnc
The leaves of the mulberry (Morus sp.) have a variety of medicinal, culinary, industrial and agricultural applications. In our study, we compared the protein and phenolic contents of different mulberry species (Morus alba L., M. alba × rubra, M. australis Poir., M. nigra L.) from the mulberry germplasm collection to determine species-specific differences. The possibility of using mulberries as animal feed and for pharmacological purposes was reviewed. Total phenols of all genotypes were analysed using the Folin-Ciocalteu method, while total protein content was determined using the Lowry's method. The individual phenols were analysed by high-performance liquid chromatography with UV/VIS detection. The total protein content ranged from 162.03 mg BSA/g DW (M. australis) to 239.42 mg BSA/g DW (M. alba). Significantly higher contents of total proteins were determined in the leaves of M. alba. The highest mean concentrations of total phenols (21.51 mg GAE /g DW), chlorogenic acid (18.05 mg/g DW), 4-caffeoylquinic acid (4.36 mg/g DW), 5-p-coumaroylquinic acid (2.01 mg/g DW), quercetin glycoside (0.74 mg/g DW) and kaempferol acetyl hexoside (4.42 mg/g DW) were determined in M. alba × rubra and M. nigra. In contrast, white mulberry (M.alba) genotypes contained on average the most rutin (2.63 mg/g DW) and quercetin-malonyl-hexoside (1.59 mg/g DW). It can be concluded that the leaves of the white mulberry are best suited as animal feed due to their high protein content, while the black mulberry and the hybrid M. alba × rubra have pharmacological potential due to their high phenolic content.
桑树(Morus sp.)的叶子有多种药用、烹饪、工业和农业用途。在本研究中,我们比较了不同桑种(Morus alba L., M. alba × rubra, M. australis Poir.)的蛋白质和酚类含量。, M. nigra L.)从桑树种质收集中确定种特异性差异。综述了桑葚作为动物饲料和药理用途的可能性。采用Folin-Ciocalteu法分析各基因型的总酚含量,采用Lowry法测定总蛋白含量。采用紫外/可见高效液相色谱法对各酚类物质进行分析。总蛋白含量从162.03 mg BSA/g DW(南稻)到239.42 mg BSA/g DW(白稻)不等。白桦叶片中总蛋白含量显著高于其他叶片。总酚(21.51 mg GAE /g DW)、绿原酸(18.05 mg/g DW)、4-咖啡酰奎宁酸(4.36 mg/g DW)、5-对香豆素酰奎宁酸(2.01 mg/g DW)、槲皮苷(0.74 mg/g DW)和山奈酚乙酰己糖苷(4.42 mg/g DW)的平均浓度最高。相比之下,白桑(m.a alba)基因型平均含有最多的芦丁(2.63 mg/g DW)和槲皮素-丙二酰己糖(1.59 mg/g DW)。综上所述,白桑叶蛋白质含量高,最适合作为动物饲料,而黑桑叶及其杂交品种白桑叶酚类物质含量高,具有药理潜力。
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中国农业科学
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