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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 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]

Azoxystrobin is an important fungicide to control soybean rust. It is an urgent task to clarify the residue characteristic of azoxystrobin in soybean. In this study, ultra performance liquid chromatography tandem mass spectrometry was used to establish a method for the determination of azoxystrobin residues in stems, leaves, pods and soil in soybean planting system. The limit of quantification (LOQ) was 0.005 mg/kg. The results of field experiments in Anhui, Shandong, and Jilin showed that azoxystrobin 250 g/L SC was mainly deposited in soybean leaves after application and the deposition concentration decreased gradually from north to south. The degradation rates were faster in the upper stems and leaves, and there was no significant difference between regions. The relationship between the control effect of azoxystrobin on soybean rust was compared under different doses, and the residues were provided. The minimum effective dose of azoxystrobin was 225 g a.i./hm² . The research results of this paper are of great significance to guide the scientific application of azoxystrobin in soybean production and the practice of pesticide reduction on soybean. [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 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]

In this paper, Cerasus humilis introduced from Jilin Province was cultivated in Yichun area of Xiaoxing’an Mountains. By analyzing leaf surface characteristics, the phenotypic diversity of Cerasus humilis in alpine regions was discussed, which provided technical support for the optimization and improvement of berry varieties in cold region and their development and utilization. The experimental sample plots were established by two designs of land cultivation and plastic greenhouse cultivation respectively. The leaf area, genetic variation coefficient and dry matter content of the leaves were analyzed by combining with the data of leaf length, leaf width and leaf area. Results showed that: the variation range of phenotypic traits of leaves ranged from 21.87% to 34.44%, and all strains showed abundant genetic variation characteristics, which was directly related to the differences of environmental factors. Different strains showed different degree of genetic activity under different site conditions, and the most obvious one was No. 4. After the introduction into Yichun area, the differences of genetic richness between different strains of Cerasus humilis did exist and the differences in the phenotypic characteristics were all from environmental factors. The similarity of genetic activity among the strains also proved that the genetic characters in the Cerasus humilis strains were relatively stable. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) has been increasingly used to aid agricultural production and land management, and this paper investigates the feasibility of using ultra-high resolution UAV images to differentiate land usage. We took a farmland at Dehui of Jilin province as an example and acquired its UAV images. The digital surface model (DSM) and the digital orthophoto map (DOM) of the region was generated using the digital photogrammetry software. We then calculated the regional terrain factors and the vegetation indices, and combined them with the original orthophoto model to construct the baseline images for land use classification. The tool of estimation of scale parameters (ESP) was used to extract the optimal scale for segmentation, and a level of objective was constructed after processing the multi-scale segmentation based on the optimal value of each parameter. Terrain and morphological features of each object was used for the classification. It included two steps. The first one was to perform an object-oriented classification of all the five features based on the random forest algorithm. We used a five-feature classifications to analyze the impact of different features. The first one only used the spectrum feature to classify and the index features, morphological features, terrain features and textural features were added consecutively for further classification. The overall accuracy, kappa coefficient, the omission errors and the commission errors for each feature were compared. In the second step, Boruta feature selection method was applied to the original feature space to obtain an optimal feature subset. Based on the optimal feature subset, land use classification was conducted using the random forest algorithm, naive Bayesian algorithm, logistic regression method and the support vector machine (SVM). Using the same optimal feature subset, the influence of each method on the classification was tested. The results showed that the accuracy of the five feature selection schemes was 93.72%, 97.35%, 96.93%, 97.77%, and 98.04% respectively. Adding morphological features reduced accuracy, while adding other features improved accuracy. The scheme with five features gave the best result. The commission was mainly between the bare land and residential land, and the omissions were mainly among grassland, water canals and roads. The confusion between them was likely to be caused by the similarity of spectral, morphology, textural properties and their similar positions. In this study, the important features for classification were spectrum features, textural features, index features and terrain features, and the least important features were morphological features. There were 72 features passing the Boruta test and forming the optimal feature subset. Based on this feature space, the overall accuracy with the above four different algorithms was 98.19%, 96.79%, 90.22% and 96.23% respectively. The classification using the random forest algorithm gave the best result. In conclusion, adding the terrain features can assist classification of land coverage and improve accuracy. Compared with other algorithms, the random forest algorithm is most robust in classification of land coverage in using high dimensional feature space. [ABSTRACT FROM AUTHOR]