Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the farmers can track specific data for individual cows such as body condition score, genetic abnormalities, etc. Specifically, a four-digit identification number is used, so that a farm can accommodate up to 9999 cows. In our proposed system, we develop an individual cow tracker to provide effective management with real-time upgrading enforcement. For this purpose, head detection is first carried out to determine the cow’s position in its related camera view. The head detection process incorporates an object detector called You Only Look Once (YOLO) and is then followed by ear tag detection. The steps involved in ear tag recognition are (1) finding the four-digit area, (2) digit segmentation using an image processing technique, and (3) ear tag recognition using a convolutional neural network (CNN) classifier. Finally, a location searching system for an individual cow is established by entering the ID numbers through the application’s user interface. The proposed searching system was confirmed by performing real-time experiments at a feeding station on a farm at Hokkaido prefecture, Japan. In combination with our decision-making process, the proposed system achieved an accuracy of 100% for head detection, and 92.5% for ear tag digit recognition. The results of using our system are very promising in terms of effectiveness.
Outbreaks of bacterial cold-water disease (BCWD), caused by Flavobacterium psychrophilum, are widespread in Japan, especially among ayu Plecoglossus altivelis. There are few investigations of F. psychrophilum in river water, and its seasonal distribution has not been clarified. We aimed to identify the spatiotemporal dynamics of F. psychrophilum and ayu to provide information that is useful for establishing a countermeasure for BCWD. Quantitative analysis of environmental DNA (eDNA) was used to clarify the year-round dynamics of ayu and F. psychrophilum. We sampled river water from the Nagara and Ibi rivers in Japan, and conducted monthly water sampling and eDNA quantification. Changes in the eDNA concentration of ayu were consistent with the known life histories of the fish. There was a strong negative correlation between the eDNA concentration of F. psychrophilum and water temperature, suggesting a strong dependence of F. psychrophilum dynamics in the river on water temperature. Furthermore, relatively high eDNA concentrations were recorded for both organisms in early summer and fall, suggesting that ayu is infected with F. psychrophilum during these seasons when experiencing up- and downmigration, respectively.
Accurately predicting when calving will occur can provide great value in managing a dairy farm since it provides personnel with the ability to determine whether assistance is necessary. Not providing such assistance when necessary could prolong the calving process, negatively affecting the health of both mother cow and calf. Such prolongation could lead to multiple illnesses. Calving is one of the most critical situations for cows during the production cycle. A precise video-monitoring system for cows can provide early detection of difficulties or health problems, and facilitates timely and appropriate human intervention. In this paper, we propose an integrated approach for predicting when calving will occur by combining behavioral activities extracted from recorded video sequences with a Hidden Markov Model. Specifically, two sub-systems comprise our proposed system: (i) Behaviors extraction such as lying, standing, number of changing positions between lying down and standing up, and other significant activities, such as holding up the tail, and turning the head to the side; and, (ii) using an integrated Hidden Markov Model to predict when calving will occur. The experiments using our proposed system were conducted at a large dairy farm in Oita Prefecture in Japan. Experimental results show that the proposed method has promise in practical applications. In particular, we found that the high frequency of posture changes has played a central role in accurately predicting the time of calving.
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