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The existing pig counting system based on image processing are seriously affected by light conditions, and the counting accuracy is poor when pigs are crowded and obscured.To realize the intelligent pig counting, the paper proposed a counting scheme based on the improved instance segmentation algorithm was proposed in this study. Aiming at the problems of image illumination and target edge blur, Laplace operator was used to preprocess the images. The feature extraction network of MASK R-CNN network was improved by using Resnet-152 as the Mask R-CNN feature extraction network, and the original Feature Pyramid Network(FPN) was followed by a bottom-up enhancement path, which directly fused the low-level edge features with the high-level features to improve the recognition ability of the target edge bluring. The non maximum suppression process and loss function were optimized and improved to improve the segmentation accuracy. The experiments were carried out at three different real pig farms to verify the counting accuracy, respectively in Hebei province, Jilin province and Inner Mongolia. The size of the cage in Hebei pig farm was 5.5 m × 1.8 m, with an average of 12 pigs in a single pen, with a feeding density of 1.21 pigs/m²; the size of the cage in Jilin pig farm was 5.5 m × 3.9 m, with an average of 22 pigs in a single pen, and the average rearing density was 1.03 pigs/m²; the size of the cage in Inner Mongolia pig farm was 11.4 m × 5.28 m, with an average of 80 pigs in a single pen, the average feeding density was 1.32 pigs/m². The RGB camera is was installed on the top of the pen and acquired the image in daytime. 2 400 images were collected in total, and 2000 images were selected after image preprocessing, and 1 250 images of three pig farms were selected as the original data set according to the ratio of 2:2:1. The training set and verification set were enhanced to 1 500 and 150 images and 250 images for the test set, The experimental results showed that 98 images could realize exact counting and 2 images missed 1 pig in the Jilin pig farm, the accuracy of pig counting was 98%. In Hebei pig farm, 99 images could realize the exact counting and the accuracy of pig counting was 99%. For Inner Mongolia pig farms with high feeding density, the accuracy of pig counting was 86%, among the 50 test images, 7 images missed detection, 4 images missed 1 target, 3 images missed 2 targets, The results can provide the application of the artificial intelligent in agriculture field. [ABSTRACT FROM AUTHOR]

The existing pig counting system based on image processing are seriously affected by light conditions, and the counting accuracy is poor when pigs are crowded and obscured.To realize the intelligent pig counting, the paper proposed a counting scheme based on the improved instance segmentation algorithm was proposed in this paper. Aiming at the problems of image illumination and target edge blur, Laplace operator was used to preprocess the images. The feature extraction network of MASK R-CNN network was improved by using Resnet-152 as the Mask R-CNN feature extraction network, and the original Feature Pyramid Network(FPN) was followed by a bottom-up enhancement path, which directly fused the low-level edge features with the high-level features to improve the recognition ability of the target edge bluring. The non maximum suppression process and loss function were optimized and improved to improve the segmentation accuracy. The experiments were carried out at three different real pig farms to verify the counting accuracy, respectively in Hebei province, Jilin province and Inner Mongolia. The size of the cage in Hebei pig farm was 5.5 m × 1.8 m, with an average of 12 pigs in a single pen, with a feeding density of 1.21 pigs/m²; the size of the cage in Jilin pig farm was 5.5 m × 3.9 m, with an average of 22 pigs in a single pen, and the average rearing density was 1.03 pigs/m²; the size of the cage in Inner Mongolia pig farm was 11.4 m × 5.28 m, with an average of 80 pigs in a single pen, the average feeding density was 1.32 pigs/m². The RGB camera is was installed on the top of the pen and acquired the image in daytime. 2 400 images were collected in total, and 2000 images were selected after image preprocessing, and 1 250 images of three pig farms were selected as the original data set according to the ratio of 2:2:1. The training set and verification set were enhanced to 1 500 and 150 images and 250 images for the test set, The experimental results showed that 98 images could realize exact counting and 2 images missed 1 pig in the Jilin pig farm, the accuracy of pig counting was 98%. In Hebei pig farm, 99 images could realize the exact counting and the accuracy of pig counting was 99%. For Inner Mongolia pig farms with high feeding density, the accuracy of pig counting was 86%, among the 50 test images, 7 images missed detection, 4 images missed 1 target, 3 images missed 2 targets, The results can provide the application of the artificial intelligent in agriculture field. [ABSTRACT FROM AUTHOR]

Pig sounds reflect the stress and health status of pigs, also it is the most easily collected biomarker through non-contact methods. To improve the classification accuracy of pig sound signals, this study used the spectrogram to visualize the time-frequency characteristics, and combined with the deep neural network classification model. Four contents were discussed as followed: 1) The sound data set was constructed. According to the different sound signals, the pig's behavior could be recognized by the classification network. When the pig was in normal statuses, the pig sounds were called as grunts. If the pig was in frightened statuses, such as injected or chased, pig sounds were defined as screams. Before the feeding, when pigs see the food, pigs made long irritable sounds. The sounds were called as howls of hunger. All pig sounds were collected on-farm by the sound collection box. On the farm, a laptop was used as a host computer to display all the working parameters of the collection box. The data transmission and storage scheme adopted the Client/Server architecture. Besides, the worker labeled sounds, according to the behavior. 2) Spectrograms of different sounds built up the training and test dataset of the image recognition network. The pig sound was a stationary signal in short time duration, therefore, continuously calculating the frequency spectrum of the sound signal in the vicinity of the selected instant of time gave rise to a time-frequency spectrum. The study discussed the optimal spectrogram parameters, which were suitable for the structure of the deep neural network. Experiment results showed that the segment length of the pig sounds was 256 samples and the overlap was 128 samples, the classification accuracy of the deep neural network was highest. The spectrogram optimization experiment results showed that the recognition accuracy was improved by 1.8%. 3) The deep neural network was designed. The study used the MobileNetV2 network to achieve recognition, which was based on an inverted residual structure where the shortcut connections were between the thin bottleneck layers. Aiming to the portable platform in the real application, the width factor and the resolution factor were introduced to define a smaller and more efficient architecture. Also, Adam optimizer formed an adequate substitute for the underlying RMSprop optimizer, and it made the loss function convergent faster. Adam optimizer calculated the adaptive parameter-learning rate based on the mean value of the first moment, making full use of the mean value of the second moment of the gradient. The result implied the width factor was chosen as 0.5, the accuracy was highest. 4) Compared experiments had been done. Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Extra Trees (ET) algorithms were compared with the proposed pig sound recognition network. All algorithms were trained and tested on the same sound dataset. Specifically, the proposed algorithm increased the recognition accuracy of screams from 84.5% to 97.1%, and the accuracy of howls was increased from 86.1% to 97.5%. But the recognition accuracy of grunts was decreased from 100% to 97.3%. This was caused by the difference in the principle of different recognition algorithms. Furthermore, through the experiments on the width factor and resolution factor, a smaller and more efficient model was defined based on the standard MobileNetV2 model, and the running speed of the model was significantly improved to meet the needs of practical applications, however, the accuracy remained. This study showed that the abnormal pig vocalization was related to abnormal behavior, so sound recognition could help to monitor behaviors. In the future, the abnormal behaviors combined the sound recognition and video analysis would be discussed. [ABSTRACT FROM AUTHOR]