Pig body condition scoring is an important tool for pig farmers to ensure the health and nutritional status of pigs. An efficient and accurate body condition scoring method is very important to ensure the health of pigs. This thesis proposes an automatic and objective pig body condition scoring method based on deep learning and EfficientNet-B0. Traditional methods of visual examination and manual palpation are subjective, time-consuming, and can vary from observer to observer, leading to inconsistent and unreliable results. Deep learning model based on artificial neural network shows great potential in automating pig state scoring. The proposed method was trained and evaluated on a large pig image dataset, and compared with traditional manual methods and object detection deep learning algorithms. The results show that this method can improve the accuracy and efficiency of pig body condition grading, with a higher average accuracy of 85.66% for body condition classification, which has certain practical production significance and provides a foundation for further related research.

Zihao Liu, Xingpeng Chen, Bingyu Pan, Shuai Li, Lulu Yang, Hongjun Ma, Longshen Liu, Jun Zhou. Study on Pig Body Condition Scoring Based on Deep Learning Model EfficientNet-B0. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 6: 156-162. https://doi.org/10.25236/AJCIS.2023.060625.

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