Enhanced LiteHRNet based sheep weight estimation using RGB-D images

Sheep farming is a strategic sector of livestock husbandry, and its production has large market demand in many countries. The live weight of sheep provides important information about the health state and the time point for marketing. Manual weighing sheep is time-consuming for farmers even with the help of a ground scale. With the development of Artificial Intelligence (AI) and smart sensors, non-contact sheep weighing methods have gradually been used to estimate weight. However, the performance of prior studies tends to degenerate with varying postures and light conditions in practical natural environments. In this study, we propose a sheep live weight estimation approach based on LiteHRNet (a Lightweight High-Resolution Network) using RGB-D images. Class Activation Mapping (CAM) guided the design of efficient network heads embracing visual explanation and applicability in practical natural environments. Experiments are conducted on our challenging dataset (of 726 sheep RGB-D images, weight range between 19.5 to 94 kg). Comparative experiment results reveal that the lightweight Convolutional Neural Network (CNN) model trained on RGB-D images can reach an acceptable weight estimation result, Mean Average Percentage Error (MAPE) is 14.605% (95% confidence interval: [13.821%, 15.390%], t test) with only 1.06M parameters. Our works can be viewed as preliminary work that confirms the ability to use lightweight CNNs for sheep weight estimation on RGB-D data. The results of this study are potential to develop an embedded device to automatically estimate sheep live weight and would contribute to the development of precision livestock farming.

Automated summary

This study proposes a sheep live weight estimation approach based on LiteHRNet using RGB-D images. Experimental results show that the lightweight CNN model trained on RGB-D images can achieve acceptable weight estimation results, with a Mean Average Percentage Error (MAPE) of 14.605% and only 1.06M parameters. The results of this study have the potential to develop an embedded device for automatic sheep live weight estimation and contribute to the development of precision livestock farming.