Aiming at breaking down the bottleneck problems of different scale of poultry farms, the low profitability of poultry farming, and backward information management in China, a safe and efficient information management system for poultry farming was designed. This system consists of (1) a management system application layer, (2) a data service layer, and (3) an information sensing layer. The information sensing layer obtains and uploads production and farming information through the wireless sensor network built in the poultry house. The use of a cloud database as an information storage carrier in the data service layer eliminates the complex status of deploying local server clusters, and it improves the flexibility and scalability of the system. The management system application layer contains many sub-function modules including poultry disease detection functions to realize the visual management of farming information and health farming; each module operates independently and cooperates with each other to form a set of information management system for poultry farming with wide functional coverage, high service efficiency, safety, and convenience. The system prototype has been tested for the performance of wireless sensor network and cloud database, and the results show that the prototype is capable of acquiring and managing poultry farming information.

Poultry pose estimation is a prerequisite for evaluating abnormal behavior and disease prediction in poultry. Accurate pose-estimation enables poultry producers to better manage their poultry. Because chickens are group-fed, how to achieve automatic poultry pose recognition has become a problematic point for accurate monitoring in large-scale farms. To this end, based on computer vision technology, this paper uses a deep neural network (DNN) technique to estimate the posture of a single broiler chicken. This method compared the pose detection results with the Single Shot MultiBox Detector (SSD) algorithm, You Only Look Once (YOLOV3) algorithm, RetinaNet algorithm, and Faster_R-CNN algorithm. Preliminary tests show that the method proposed in this paper achieves a 0.0128 standard deviation of precision and 0.9218 ± 0.0048 of confidence (95%) and a 0.0266 standard deviation of recall and 0.8996 ± 0.0099 of confidence (95%). By successfully estimating the pose of broiler chickens, it is possible to facilitate the detection of abnormal behavior of poultry. Furthermore, the method can be further improved to increase the overall success rate of verification.

The sex detection of chicks is an important work in poultry breeding. Separating chicks of different sexes early can effectively improve production efficiency and commercial benefits. In this paper, based on the difference in calls among one-day-old chicks of different sexes, a sex detection method based on chick calls is designed. Deep learning methods were used to classify the calls of chicks and detect their sex. This experiment studies three different varieties of chicks. The short-time zero-crossing rate was used to automatically detect the endpoints of chick calls in audio. Three kinds of audio features were compared: Spectrogram, Cepstrogram and MFCC+Logfbank. The features were used as the input in neural networks, and there were five kinds of neural networks: CNN, GRU, CRNN, TwoStream and ResNet-50. After the cross-comparison experiment of different varieties of chicks, audio features and neural networks, the ResNet-50 neural network trained with the MFCC+Logfbank audio features of three yellow chick calls had the highest test accuracy of 83% when testing Three-yellow chicks’ calls. The GRU neural network trained with the Spectrogram audio features of native chick calls had the highest test accuracy of 76.8% when testing Native chicks’ calls. The ResNet-50 neural network trained with Spectrogram audio features of flaxen-yellow chick calls had the highest test accuracy of 66.56%when testing flaxen-yellow chick calls. Multiple calls of each chick were detected, and the majority voting method was used to detect the sex of the chicks. The ResNet-50 neural network trained with the Spectrogram of three yellow chick calls had the highest sex detection accuracy of 95% when detecting the three yellow chicks’ sex. The GRU neural network trained with the Spectrogram and cepstrogram of native chick calls and the CRNN network trained with the Spectrogram of native chick calls had the highest sex detection accuracy of 90% when detecting the native chicks’ sex. The Twostream neural network trained with MFCC+Logfbank of flaxen-yellow chick calls and the ResNet-50 network trained with the Spectrogram of flaxen-yellow chick calls had the highest sex detection accuracy of 80% when detecting the flaxen-yellow chicks’ sex. The results of the cross-comparison experiment show that there is a large diversity between the sex differences in chick calls of different breeds. The method is more applicable to chick sex detection in three yellow chicks and less so in native chicks and flaxen-yellow chicks. Additionally, when detecting the sex of chicks of a similar breed to the training chicks, the method obtained better results, while detecting the sex of chicks of other breeds, the detection accuracy was significantly reduced. This paper provides further perspectives on the sex detection method of chicks based on their calls and help and guidance for future research.

Poultry managers can better understand the state of poultry through poultry behavior analysis. As one of the key steps in behavior analysis, the accurate estimation of poultry posture is the focus of this research. This study mainly analyzes a top-down pose estimation method of multiple chickens. Therefore, we propose the “multi-chicken pose” (MCP), a pose estimation system for multiple chickens through deep learning. Firstly, we find the position of each chicken from the image via the chicken detector; then, an estimate of the pose of each chicken is made using a pose estimation network, which is based on transfer learning. On this basis, the pixel error (PE), root mean square error (RMSE), and image quantity distribution of key points are analyzed according to the improved chicken keypoint similarity (CKS). The experimental results show that the algorithm scores in different evaluation metrics are a mean average precision (mAP) of 0.652, a mean average recall (mAR) of 0.742, a percentage of correct keypoints (PCKs) of 0.789, and an RMSE of 17.30 pixels. To the best of our knowledge, this is the first time that transfer learning has been used for the pose estimation of multiple chickens as objects. The method can provide a new path for future poultry behavior analysis

At present, raising caged chickens is a common farming method in China. However, monitoring the status of caged chickens is still done by human labor, which is time-consuming and laborious. This paper proposed a posture detection method for caged chickens based on computer vision, which can automatically identify the standing and lying posture of chickens in a cage. For this aim, an image correction method was used to rotate the image and make the feeding trough horizontal in the image. The variance method and the speeded-up robust features method were proposed to identify the feeding trough and indirectly obtain the key area through the feeding trough position. In this paper, a depth camera was used to generate three-dimensional information so that it could extract the chickens from the image of the key area. After some constraint conditions, the chickens’ postures were screened. The experimental results show that the algorithm can achieve 97.80% precision and 80.18% recall (IoU > 0.5) for white chickens and can achieve 79.52% precision and 81.07% recall (IoU > 0.5) for jute chickens (yellow and black feathers). It runs at ten frames per second on an i5-8500 CPU. Overall, the results indicated that this study provides a non-invasive method for the analysis of posture in caged chickens, which may be helpful for future research on poultry.

Accurate poultry detection is crucial for studying poultry behavior using computer vision and video surveillance. However, in free-range farming environments, detecting chickens can often be challenging due to their small size and mutual occlusion. The current detection algorithms exhibit a low level of accuracy, with a high probability of false and missed detections. To address this, we proposed a multi-object chicken detection method named Super-resolution Chicken Detection, which utilizes super-resolution fusion optimization. The algorithm employes the residual-residual dense block to extract image features and used a generative adversarial network to compensate for the loss of details during deep convolution, producing high-resolution images for detection. The proposed algorithm was validated with the B1 dataset and the MC1 multi-object dataset, demonstrating that the reconstructed images possessed richer pixel features compared to original images, specifically it improved detection accuracy and reduced the number of missed detections. The structural similarity of the reconstructed images was 99.9%, and the peak signal-to-noise ratio was above 30. The algorithm improved the Average Precision_50:95 of all You Only Look Once Version X (YOLOX) models, with the largest improvement for the B1 dataset with YOLOX-Large (+6.3%) and for the MC1 dataset with YOLOX-Small (+4.1%). This was the first time a super-resolution reconstruction technique was applied to multi-object poultry detection. Our method will provide a fresh approach for future poultry researchers to improve the accuracy of object detection using computer vision and video surveillance.