Cow detection and tracking system utilizing multi-feature tracking algorithm.

In modern cattle farm management systems, video-based monitoring has become important in analyzing the high-level behavior of cattle for monitoring their health and predicting calving for providing timely assistance. Conventionally, sensors have been used for detecting and tracking their activities. As the body-attached sensors cause stress, video cameras can be used as an alternative. However, identifying and tracking individual cattle can be difficult, especially for black and brown varieties that are so similar in appearance. Therefore, we propose a new method of using video cameras for recognizing cattle and tracking their whereabouts. In our approach, we applied a combination of deep learning and image processing techniques to build a robust system. The proposed system processes images in separate stages, namely data pre-processing, cow detection, and cow tracking. Cow detection is performed using a popular instance segmentation network. In the cow tracking stage, for successively associating each cow with the corresponding one in the next frame, we employed the following three features: cow location, appearance features, as well as recent features of the cow region. In doing so, we simply exploited the distance between two gravity center locations of the cow regions. As color and texture suitably define the appearance of an object, we analyze the most appropriate color space to extract color moment features and use a Co-occurrence Matrix (CM) for textural representation. Deep features are extracted from recent cow images using a Convolutional Neural Network (CNN features) and are also jointly applied in the tracking process to boost system performance. We also proposed a robust Multiple Object Tracking (MOT) algorithm for cow tracking by employing multiple features from the cow region. The experimental results proved that our proposed system could handle the problems of MOT and produce reliable performance.

Bioremediation of wastewater from edible oil refinery factory using oleaginous microalga Desmodesmus sp. S1.

Edible oil industry produced massive wastewater, which requires extensive treatment to remove pungent smell, high phosphate, carbon oxygen demand (COD), and metal ions prior to discharge. Traditional anaerobic and aerobic digestion could mainly reduce COD of the wastewater from oil refinery factories (WEORF). In this study, a robust oleaginous microalga Desmodesmus sp. S1 was adapted to grow in WEORF. The biomass and lipid content of Desmodesmus sp. S1 cultivated in the WEORF supplemented with sodium nitrate were 5.62 g·L(-1) and 14.49%, whereas those in the WEORF without adding nitrate were 2.98 g·L(-1) and 21.95%. More than 82% of the COD and 53% of total phosphorous were removed by Desmodesmus sp. S1. In addition, metal ions, including ferric, aluminum, manganese and zinc were also diminished significantly in the WEORF after microalgal growth, and pungent smell vanished as well. In comparison with the cells grown in BG-11 medium, the cilia-like bulges and wrinkles on the cell surface of Desmodesmus sp. S1 grown in WEORF became out of order, and more polyunsaturated fatty acids were detected due to stress derived from the wastewater. The study suggests that growing microalgae in WEORF can be applied for the dual roles of nutrient removal and biofuel feedstock production.