Vision-Based Module for Herding with a Sheepdog Robot

Castillo, Virginia Riego del; Sánchez-González, Lídia; Campazas-Vega, Adrián; Strisciuglio, Nicola

doi:10.3390/s22145321

Cited by 7 publications

(4 citation statements)

References 80 publications

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“…They have been widely applied in agricultural farming scenarios. Considering the application context and accuracy requirements, this study is based on the latest iteration of the YOLO algorithm, the YOLOv8 model [20]. Specific optimizations were made to certain network structures to achieve relative light weightiness of the model and fast detection of complex meat duck targets, while ensuring accuracy.…”

Section: Yolov8 Network Structurementioning

confidence: 99%

Regulation of Meat Duck Activeness through Photoperiod Based on Deep Learning

Duan,

Han,

Zhao

et al. 2023

Animals

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The regulation of duck physiology and behavior through the photoperiod holds significant importance for enhancing poultry farming efficiency. To clarify the impact of the photoperiod on group-raised duck activeness and quantify duck activeness, this study proposes a method that employs a multi-object tracking model to calculate group-raised duck activeness. Then, duck farming experiments were designed with varying photoperiods as gradients to assess this impact. The constructed multi-object tracking model for group-raised ducks was based on YOLOv8. The C2f-Faster-EMA module, which combines C2f-Faster with the EMA attention mechanism, was used to improve the object recognition performance of YOLOv8. Furthermore, an analysis of the tracking performance of Bot-SORT, ByteTrack, and DeepSORT algorithms on small-sized duck targets was conducted. Building upon this foundation, the duck instances in the images were segmented to calculate the distance traveled by individual ducks, while the centroid of the duck mask was used in place of the mask regression box’s center point. The single-frame average displacement of group-raised ducks was utilized as an intuitive indicator of their activeness. Farming experiments were conducted with varying photoperiods (24L:0D, 16L:8D, and 12L:12D), and the constructed model was used to calculate the activeness of group-raised ducks. The results demonstrated that the YOLOv8x-C2f-Faster-EMA model achieved an object recognition accuracy (mAP@50-95) of 97.9%. The improved YOLOv8 + Bot-SORT model achieved a multi-object tracking accuracy of 85.1%. When the photoperiod was set to 12L:12D, duck activeness was slightly lower than that of the commercial farming’s 24L:0D lighting scheme, but duck performance was better. The methods and conclusions presented in this study can provide theoretical support for the welfare assessment of meat duck farming and photoperiod regulation strategies in farming.

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Section: Yolov8 Network Structurementioning

confidence: 99%

Regulation of Meat Duck Activeness through Photoperiod Based on Deep Learning

Duan,

Han,

Zhao

et al. 2023

Animals

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“…In Riego Del Castillo et al [59] a camera-based system is tested to automatically detect predators in pasture-based livestock farming and distinguish them from other species, such as dog. After adopting algorithms for the objects detection by using from sv1 to v5 versions of YOLO in combination with the Single-Shot MultiBox Detector (SSD), the YOLOv5 archives prove to be the most accurate for the requirements of pasture-based systems, achieving a mean average precision of 99.49%.…”

Section: Animals Identification and Locationmentioning

confidence: 99%

“…After adopting algorithms for the objects detection by using from sv1 to v5 versions of YOLO in combination with the Single-Shot MultiBox Detector (SSD), the YOLOv5 archives prove to be the most accurate for the requirements of pasture-based systems, achieving a mean average precision of 99.49%. This system could be accounted for an additional tool for the protection of the herd [59]. [57].…”

Section: Animals Identification and Locationmentioning

confidence: 99%

Advanced Technologies in Sheep Extensive Farming on a Climate Change Context

Giovanna Ciliberti,

Caroprese,

Albenzio

2023

Agricultural Sciences

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Climate change represents a serious issue that negatively impacts the animals’ performance. Sheep production from Mediterranean region is mainly characterized by extensive farming system that during summer are exposed to high temperature. The explored new technologies to monitoring animal welfare and environment could mitigate the impact of climate change supporting the sustainability of animal production and ensuring food security. The present chapter will summarize the more recent advanced technologies based on passive sensors, wearable sensors, and the combination of different technologies with the latest machine learning protocol tested for sheep farming aimed at monitoring animal welfare. A focus on the precision technologies solution to detect heat stress will be presented.

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“…Existing technologies use a combination of low-cost time-delay cameras together with a machine learning method to monitor the position information of sheep with high accuracy and sensitivity; however, it requires that there is no background confused with the color of animals in the camera's field of view, and the camera also cannot face to the sun [2]. Del Castillo et al [3] designed a system that could automatically detect dangerous animals such as Iberian wolves and distinguish them from dogs and other animals in real time, with an average accuracy of 99.49%. In order to further promote the development of precision livestock technology, behavior recognition, weight estimation, and other research based on deep learning have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Mid-Infrared Sheep Segmentation in Highland Pastures Using Multi-Level Region Fusion OTSU Algorithm

et al. 2023

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In highland pastures, grazing is a common method for managing sheep due to the abundance of grassland resources. However, it is easy for sheep to encounter situations such as stray, deviation and attacks from natural enemies; therefore, the remote monitoring of sheep in the highland pastures is an urgent problem to be solved. This paper proposes a mid-infrared sheep segmentation method based on the multi-level region fusion maximum between-class variance algorithm, i.e., OTSU algorithm, for sheep surveillance. First, a mean adjustment OTSU algorithm is designed to better distinguish the interference areas in the background. Second, the Butterworth high-pass filter is combined with the mean adjustment OTSU segmentation algorithm to remove the high-brightness interference areas in the background with slow gray intensity changes. Finally, after filtering out the large area background and small stray point, the two processed results above are fused with the AND logical operation to obtain a final segmentation result. Our algorithm is evaluated using three objective evaluation indicators: the root mean square error (RMSE), structural similarity index metric (SSIM), and peak signal to noise ratio (PSNR). The RMSE, SSIM, PSNR of highland wetland image are 0.43187, 0.99526, and 29.16353. The RMSE, SSIM, PSNR of sandy land image are 0.87472, 0.98388, and 23.87430. The RMSE, SSIM, PSNR of grassland image are 0.65307, 0.99437, and 30.33159. The results show that our algorithm can meet the requirements for the mid-infrared sheep segmentation in highland pastures.

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Vision-Based Module for Herding with a Sheepdog Robot

Cited by 7 publications

References 80 publications

Regulation of Meat Duck Activeness through Photoperiod Based on Deep Learning

Regulation of Meat Duck Activeness through Photoperiod Based on Deep Learning

Advanced Technologies in Sheep Extensive Farming on a Climate Change Context

Mid-Infrared Sheep Segmentation in Highland Pastures Using Multi-Level Region Fusion OTSU Algorithm

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