Deep learning-based sow posture classifier using colour and depth images

Le résumé fourni par la source

• This study explored different models and image types to classify sow postures. • Models that used depth images performed better than other image types. • The Resnet-18 model was successful in determining sow positions (98.7 % accuracy). • Transition postures were accurately determined (recall above 97.7 %). Assessing sow posture is essential for understanding their physiological condition and helping farmers improve herd productivity. Deep learning-based techniques have proven effective for image interpretation, offering a better alternative to traditional image processing methods. However, distinguishing transitional postures such as sitting and kneeling is challenging with only conventional top-view RGB images. This study aimed to develop and compare different deep learning-based sow posture classifiers using different architectures and image types. Using Kinect v.2 cameras, RGB and depth images were collected from 9 sows housed individually in farrowing crates. A total of 26,362 images were manually labelled by posture: “standing”, “kneeling”, “sitting”, “ventral recumbency” and “lateral recumbency”. Different deep learning algorithms were developed to detect sow postures from three types of images: colour (RGB), depth (depth image transformed into greyscale), and fused (colour-depth composite images). Results indicated that the ResNet-18 model presented the best results and that including depth information improved the performance of all models tested. Depth and fused models achieved higher accuracies than the models using only RGB images. The best model used only depth images as input and presented an accuracy of 98.3 %. The mean precision and recall values were 97.04 % and 97.32 %, respectively (F1-score = 97.2 %). The study shows improved posture classification using depth images. Future research can improve model accuracy and speed by expanding the database, exploring fused methods and computational models, considering different breeds of sows, and incorporating more postures. These models can be integrated into computer vision systems to automatically characterise sow behavior.

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