תפריט נגישות
ניגודיות עדינהניגודיות גבוהה
מונוכרום
הדגשת קישורים
חסימת אנימציה
פונט קריא
סגור
איפוס הגדרות נגישות
להורדת מודול נגישות חינם תפריט נגישותניגודיות עדינהניגודיות גבוההמונוכרוםהדגשת קישוריםחסימת אנימציהפונט קריאסגוראיפוס הגדרות נגישותלהורדת מודול נגישות חינםY. Edan
Individual feed intake of dairy cows is an important, currently unavailable, variable in commercial dairies. Earlier systems developed were either costly or unreliable enough for commercial farms. This research developed a low-cost individual feed intake system using RGB-D cameras and deep learning algorithm. Depth and colour images are produced from an RGB-D camera, and are used to build a CNNs (Convolutional Neural Networks) regression model for weight intake prediction. To provide training data, an automatic data acquisition system was designed to collect a wide range of food weights, in different configurations and conditions (indoor, outdoor, direct-sun). The system included a scale and a micro-controller set in the Volcani research dairy facility, an open cowshed with Holstein cows, eating Total Mix Ration. With this setup, 28,761 data were collected over seven days. Additional data were created by data augmentation methods. The model was evaluated on a test-dataset acquired in the same dairy farm. The model was tested for different combinations of training data (direct-sun/outdoor) to evaluate the importance of the data diversity. Per meal, mean absolute and square errors were 0.127 kg, and 0.034 kg2, respectively, the consumed amount of feed measured in range of 0-8 kg. The sensitivity analysis shows that the amount and diversity of data is important for model training. Better results were achieved for the model that was trained with high diversity data. The results suggest that cameras and CNNs are feasible for individual feed intake measurement on the dairy farm. © Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019. All rights reserved.
Y. Edan
Individual feed intake of dairy cows is an important, currently unavailable, variable in commercial dairies. Earlier systems developed were either costly or unreliable enough for commercial farms. This research developed a low-cost individual feed intake system using RGB-D cameras and deep learning algorithm. Depth and colour images are produced from an RGB-D camera, and are used to build a CNNs (Convolutional Neural Networks) regression model for weight intake prediction. To provide training data, an automatic data acquisition system was designed to collect a wide range of food weights, in different configurations and conditions (indoor, outdoor, direct-sun). The system included a scale and a micro-controller set in the Volcani research dairy facility, an open cowshed with Holstein cows, eating Total Mix Ration. With this setup, 28,761 data were collected over seven days. Additional data were created by data augmentation methods. The model was evaluated on a test-dataset acquired in the same dairy farm. The model was tested for different combinations of training data (direct-sun/outdoor) to evaluate the importance of the data diversity. Per meal, mean absolute and square errors were 0.127 kg, and 0.034 kg2, respectively, the consumed amount of feed measured in range of 0-8 kg. The sensitivity analysis shows that the amount and diversity of data is important for model training. Better results were achieved for the model that was trained with high diversity data. The results suggest that cameras and CNNs are feasible for individual feed intake measurement on the dairy farm. © Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019. All rights reserved.
