BCS ("Body Condition Score") is a method used to estimate body fat reserves and accumulated energy balance of cows. BCS heavily influences milk production, reproduction, and health of cows. Therefore, it is important to monitor BCS to achieve better animal response, but this is a time-consuming and subjective task performed visually by expert scorers. Several studies have tried to automate BCS of dairy cows by applying image analysis and machine learning techniques. This work analyzes these studies and proposes a system based on Convolutional Neural Networks (CNNs) to improve overall automatic BCS estimation, whose use might be extended beyond dairy production. The developed system has achieved good estimation results in comparison with other systems in the area. Overall accuracy of BCS estimations within 0.25 units of difference from true values was 78%, while overall accuracy within 0.50 units was 94%. Similarly, weighted precision and recall, which took into account imbalance BCS distribution in the built dataset, show similar values considering those error ranges. 1. uses images as the only information source (without external data such as weight, age, or lactation stage of the cow), 2. uses low-cost hardware resources, 3. gets real-time estimations,
BCS (Body Condition Score) is a method to estimate body fat reserves and accumulated energy balance of cows, placing estimations (or BCS values) in a scale of 1 to 5. Periodically rating BCS of dairy cows is very important since BCS values are associated with milk production, reproduction, and health of cows. However, in practice, obtaining BCS values is a time-consuming and subjective task performed visually by expert scorers. There have been several efforts to automate BCS of dairy cows by using image analysis and machine learning techniques. In a previous work, an automatic system to estimate BCS values was proposed, which is based on Convolutional Neural Networks (CNNs). In this paper we significantly extend the techniques exploited by that system via using transfer learning and ensemble modeling techniques to further improve BCS estimation accuracy. The improved system has achieved good estimations results in comparison with the base system. Overall accuracy of BCS estimations within 0.25 units of difference from true values has increased 4% (up to 82%), while overall accuracy within 0.50 units has increased 3% (up to 97%).
Abstract-The accuracy of a standard market receiver GPS (Global Positioning System) is near 10-15 meters the 95% of the times. To reach a sub-metric level of accuracy some techniques must be used [1]. This article describes some of these procedures to improve the positioning accuracy by using a low-cost GPS in a differential relative positioning way. The proposed techniques are some variations of Kalman, fuzzy logic and information selection.
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