Data mining as a hatchery process evaluation tool

ABSTRACT The hatchery is one of the most important segments of the poultry chain, and generates an abundance of data, which, when analyzed, allow for identifying critical points of the process . The aim of this study was to evaluate the applicability of the data mining technique to databases of egg incubation of broiler breeders and laying hen breeders. The study uses a database recording egg incubation from broiler breeders housed in pens with shavings used for litters in natural mating, as well as laying hen breeders housed in cages using an artificial insemination mating system. The data mining technique (DM) was applied to analyses in a classification task, using the type of breeder and house system for delineating classes. The database was analyzed in three different ways: original database, attribute selection, and expert analysis. Models were selected on the basis of model precision and class accuracy. The data mining technique allowed for the classification of hatchery fertile eggs from different genetic groups, as well as hatching rates and the percentage of fertile eggs (the attributes with the greatest classification power). Broiler breeders showed higher fertility (> 95 %), but higher embryonic mortality between the third and seventh day post-hatching (> 0.5 %) when compared to laying hen breeders’ eggs. In conclusion, applying data mining to the hatchery process, selection of attributes and strategies based on the experience of experts can improve model performance.

摘要 孵化场是家禽产业链中至关重要的环节之一，会产生海量数据，通过对这些数据进行分析，可识别出该生产流程的关键控制点。本研究旨在评估数据挖掘（Data Mining, DM）技术在肉用种鸡与蛋用种鸡的鸡蛋孵化数据库中的应用可行性。本研究所用数据库涵盖两类种鸡的鸡蛋孵化数据：一类为采用自然交配方式、饲养于铺设有刨花垫料栏舍中的肉用种鸡，另一类为采用人工授精配种系统、饲养于笼养环境中的蛋用种鸡。本研究将数据挖掘技术应用于分类分析任务，以种鸡类型与饲养舍饲系统作为类别划分依据。本次研究采用三种不同分析方案处理该数据库：原始数据集直接分析、属性筛选分析以及专家辅助分析。模型筛选以模型精确率与类别准确率为核心评判标准。数据挖掘技术可实现不同遗传品系种鸡所产受精蛋的分类任务，其中孵化率与受精蛋占比为分类能力最强的两项属性。相较于蛋用种鸡的种蛋，肉用种鸡的受精率更高（＞95%），但在孵化后第3天至第7天期间的胚胎死亡率也更高（＞0.5%）。综上，将数据挖掘技术应用于孵化场生产流程，并结合专家经验开展属性筛选与策略优化，可有效提升模型性能。