[1F4-OS-17b] 農業とAI(2)

[1F4-OS-17b-05] Suppression of false alarm using crowdsourcing in calving detection system

〇Yusuke Okimoto¹, Susumu Saito^1,2, Teppei Nakano^1,2, Makoto Akabane^1,2, Tetsunori Kobayashi¹, Tetsuji Ogawa¹ (1. Waseda University, 2. Intelligent Framework Lab)

Keywords:Precision Livestock Farming, Crowdsourcing, Computer Vision

In order for cattle farmers to detect calving sign beforehand and assist it to reduce risks of fatal accidents, recent work proposed a pattern recognition approach based on video information from cameras. To reduce false alarms given by the pattern recognizer, crowdsourcing can be used for double-checking the result of the automatic event detection. However, calving sign detection from videos is not a common task for crowd workers, where most of them are not experts of cattle farming; it is therefore not clear about how microtasks can be designed for the workers and thus their answers contribute to better prediction accuracy. In the present study, a calving detection system of beef cattle is designed aiming for real-world deployment. Exposure of the amnion and allanto from the buttocks of cattle is considered as a sign of calving and identified by the crowdworkers in microtasks designed. As a result of simulation evaluation of detecting two birth prognostic events, precision obtained when using only the pattern recognizer were 0.049 and 0.22, whereas in the case of using crowdsourcing it improved to 0.91 and 0.83, respectively. This result demonstrated that verification of the pattern recognition result by crowdworkers successfully reduced detection errors.