An accurate and rapid pig detection algorithm based on video image processing technology can be helpful to identify abnormal pigs and take timely measures to reduce the incidence of diseases. In order to solve the problems of low computational efficiency and low precision in pig detection algorithm based on sliding windows, this paper proposed a simple and efficient pig detection algorithm. A two-level support vector machine model was trained to calculate the probabilities of sliding windows by using gradient and gray distribution features of pigs. The principal component analysis convolution kernels were trained to extract foreground and background features of pig images. The support vector machine was used to classify sliding windows to obtain windows where pigs were located, and the non-maximum suppression algorithm was used to eliminate redundant windows to complete the target detection. The experiments showed that the proposed algorithm blending gradient and gray distribution features had a higher recall rate than the BING algorithm. The recall rate was up to 99.21% using 500 windows. The classification accuracy of sliding windows in this paper was 95.21%, which was higher than that of the PCANet. By calculating the omission detection rate, the misdetection rate, and the average detection time, it can be seen that in the detection methods of the proposed algorithm, BING + PCANet, faster rcnn and yolo, the performance of the proposed algorithm was optimal. INDEX TERMS Target detection, pig, principal component analysis, sliding window.

The background models are crucially important for the object extraction for moving objects detection in a video. The Gaussian mixture model (GMM) is one of popular methods in the background models. Gaussian mixture model which applied to the pig target detection has some shortcomings such as low efficiency of algorithm, misjudgment points and ghosts. This study proposed an improved algorithm based on adaptive Gaussian mixture model, to overcome the deficiencies of the traditional Gaussian mixture model in pig object detection. Based on Gaussian mixture background model, this paper introduced two new parameters of video frames m and T 0. The Gaussian distribution was scanned once every m frames, the excessive Gaussian distribution was deleted to improve the convergence speed of the model. Meanwhile, using different learning rates to suppress ghosts, a higher decreasing learning rate was adopted to accelerate the background modeling before T 0 , the background model would become stable as the time continued and a smaller learning rate could be used. In order to maintain a stable background and reduce noise interference, a fixed learning rate after T 0 was used. Results of experiments indicated that this algorithm could quickly build the initial background model, detect the moving target pigs, and extract the complete contours of the target pigs'. The algorithm is characterized by good robustness and adaptability.

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With massive and intensive developmentof Chinese live pig industry, the imbalance of feeding nutritionturns out to be a primary problem. The main reason is that the nutrition needs to be adaptive tolive pig's exact growth conditions in production management process. Regarding this, the paper aims at developing a feedingselection model for pig breeding based on major nutrients predictive model proposed in the Nutrient Requirements of Swine published by U.S. NRC in 1998. The objective of this model is to achieve a minimum cost of feeding stuff under the premise that the nutrients satisfy pig's needs. According to values proposed in NRC and nutritional elements data, a feedingselection model was developedto give proper suggestions on feeding dietary nutrients as well as exact type and quantity of pig feeding stuff. The work presented in this paper would contribute to the optimal precision feeding strategies so as to guarantee live pig growth quality in management process.