Yao Yuanzhou, Yu Haoyang, Mu Jiong, Li Jun, Pu Haibo
College of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan 625000, China.
Sichuan Key Laboratory of Agricultural Information Engineering, Ya'an, Sichuan 625000, China.
Entropy (Basel). 2020 Jun 29;22(7):719. doi: 10.3390/e22070719.
The gender ratio of free-range chickens is considered as a major animal welfare problem in commercial broiler farming. Free-range chicken producers need to identify chicken gender to estimate the economic value of their flock. However, it is challenging for farmers to estimate the gender ratio of chickens efficiently and accurately, since the environmental background is complicated and the chicken number is dynamic. Moreover, manual estimation is likely double counts or missed count and thus is inaccurate and time consuming. Hence, automated methods that can lead to results efficiently and accurately replace the identification abilities of a chicken gender expert, working in a farm environment, are beneficial to the industry. The contributions in this paper include: (1) Building the world's first chicken gender classification database annotated manually, which comprises 800 chicken flock images captured on a farm and 1000 single chicken images separated from the flock images by an object detection network, labelled with gender information. (2) Training a rooster and hen classifier using a deep neural network and cross entropy in information theory to achieve an average accuracy of 96.85%. The evaluation of the algorithm performance indicates that the proposed automated method is practical for the gender classification of chickens on the farm environment and provides a feasible way of thinking for the estimation of the gender ratio.
在商业肉鸡养殖中，散养鸡的性别比例被视为一个主要的动物福利问题。散养鸡生产者需要识别鸡的性别，以评估其鸡群的经济价值。然而，由于环境背景复杂且鸡的数量动态变化，农民要高效、准确地估计鸡的性别比例具有挑战性。此外，人工估计可能会出现重复计数或漏计的情况，因此不准确且耗时。因此，能够在农场环境中高效、准确地得出结果，取代鸡性别专家识别能力的自动化方法，对该行业是有益的。本文的贡献包括：（1）构建世界上第一个手动标注的鸡性别分类数据库，该数据库包含在农场拍摄的800张鸡群图像以及通过目标检测网络从鸡群图像中分离出的1000张单只鸡图像，并标注了性别信息。（2）使用深度神经网络和信息论中的交叉熵训练公鸡和母鸡分类器，以达到96.85%的平均准确率。算法性能评估表明，所提出的自动化方法在农场环境中对鸡的性别分类是实用的，并为性别比例估计提供了一种可行的思路。
