Identifying Images of Dead Chickens with a Chicken Removal System Integrated with a Deep Learning Algorithm

Abstract

The chicken industry, in which broiler chickens are bred, is the largest poultry industry in Taiwan. In a traditional poultry house, breeders must usually observe the health of the broilers in person on the basis of their breeding experience at regular times every day. When a breeder finds unhealthy broilers, they are removed manually from the poultry house to prevent viruses from spreading in the poultry house. Therefore, in this study, we designed and constructed a novel small removal system for dead chickens for Taiwanese poultry houses. In the mechanical design, this system mainly contains walking, removal, and storage parts. It comprises robotic arms with a fixed end and sweep-in devices for sweeping dead chickens, a conveyor belt for transporting chickens, a storage cache for storing chickens, and a tracked vehicle. The designed system has dimensions of approximately 1.038 × 0.36 × 0.5 m3, and two dead chickens can be removed in a single operation. The walking speed of the chicken removal system is 3.3 cm/s. In order to enhance the automation and artificial intelligence in the poultry industry, the identification system was used in a novel small removal system. The conditions of the chickens in a poultry house can be monitored remotely by using a camera, and dead chickens can be identified through deep learning based on the YOLO v4 algorithm. The precision of the designed system reached 95.24% in this study, and dead chickens were successfully moved to the storage cache. Finally, the designed system can reduce the contact between humans and poultry to effectively improve the overall biological safety.

Welfare of broilers on farm

“…Another system developed by Liu et al. ( 2021a ) has in addition a small removal system so that the caretaker does not need to touch the animal. There has also been research on the responses of birds to robots in the building, e.g.…”

Research on Tennis Motion Evaluation Method and System Based on Deep Learning

“…In order to meet the real-time performance of the video, the constant time median filtering is used here to denoise the image, and the size of the wave template is set as 3 × 3. Similarly, the average frame rate of the median filtering denoising test after brightness adjustment is about 23.8 frames per second, which has little influence on the speed [7].…”

A Real-Time Zanthoxylum Target Detection Method for an Intelligent Picking Robot under a Complex Background, Based on an Improved YOLOv5s Architecture

“…Zanthoxylum fruit target detection is similar to the majority of target detection programs in many aspects, such as UAVS automatic navigation, fire detection and face recognition. Therefore, traditional detection models, such as R-CNN [18][19][20], Faster R-CNN [21], YOLO [22][23][24][25] and SSD [26], have been applied to the detection of Zanthoxylum. Among these models, R-CNN, SSP-NET and Faster R-CNN have two detection stages, with high accuracy but much slower computing speed than YOLO and SSD models with primary structures.…”