CH4 and N2O emissions from China's beef feedlots with ad libitum and restricted feeding in fall and spring seasons.

Accurately quantifying methane (CH4) and nitrous oxide (N2O) emissions from beef operations in China is necessary to evaluate the contribution of beef cattle to greenhouse gas budgets at the national and global level. Methane and N2O emissions from two intensive beef feedlots in the North China Plain, one with a restricted feeding strategy and high manure collection frequency and the other with an ad libitum feeding strategy and low manure collection frequency, were quantified in the fall and spring seasons using an inverse dispersion technique. The diel pattern of CH4 from the beef feedlot with an ad libitum feed strategy (single peak during a day) differed from that under a restricted feeding condition (multiple peaks during a day), but little difference in the diel pattern of N2O emissions between two feeding strategies was observed. The two-season average CH4 emission rates of the two intensive feedlots were 230 and 198gCH4animal(-1)d(-1) and accounted for 6.7% and 6.8% of the gross energy intake, respectively, indicating little impact of the feeding strategy and manure collection frequency on the CH4 conversion factor at the feedlot level. However, the average N2O emission rates (21.2g N2Oanimal(-1)d(-1)) and conversion factor (8.5%) of the feedlot with low manure collection frequency were approximately 131% and 174% greater, respectively, than the feedlot under high frequency conditions, which had a N2O emission rate and conversion factor of 9.2g N2Oanimal(-1)d(-1) and 3.1%, respectively, indicating that increasing manure collection frequency played an important role in reducing N2O emissions from beef feedlots. In addition, comparison indicated that China's beef and dairy cattle in feedlots appeared to have similar CH4 conversion factors.

An Improved DeepLab v3+ Deep Learning Network Applied to the Segmentation of Grape Leaf Black Rot Spots.

The common method for evaluating the extent of grape disease is to classify the disease spots according to the area. The prerequisite for this operation is to accurately segment the disease spots. This paper presents an improved DeepLab v3+ deep learning network for the segmentation of grapevine leaf black rot spots. The ResNet101 network is used as the backbone network of DeepLab v3+, and a channel attention module is inserted into the residual module. Moreover, a feature fusion branch based on a feature pyramid network is added to the DeepLab v3+ encoder, which fuses feature maps of different levels. Test set TS1 from Plant Village and test set TS2 from an orchard field were used for testing to verify the segmentation performance of the method. In the test set TS1, the improved DeepLab v3+ had 0.848, 0.881, and 0.918 on the mean intersection over union (mIOU), recall, and F1-score evaluation indicators, respectively, which was 3.0, 2.3, and 1.7% greater than the original DeepLab v3+. In the test set TS2, the improved DeepLab v3+ improved the evaluation indicators mIOU, recall, and F1-score by 3.3, 2.5, and 1.9%, respectively. The test results show that the improved DeepLab v3+ has better segmentation performance. It is more suitable for the segmentation of grape leaf black rot spots and can be used as an effective tool for grape disease grade assessment.

Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning.

The disease spots on the grape leaves can be detected by using the image processing and deep learning methods. However, the accuracy and efficiency of the detection are still the challenges. The convolutional substrate information is fuzzy, and the detection results are not satisfactory if the disease spot is relatively small. In particular, the detection will be difficult if the number of pixels of the spot is <32 × 32 in the image. In order to effectively address this problem, we present a super-resolution image enhancement and convolutional neural network-based algorithm for the detection of black rot on grape leaves. First, the original image is up-sampled and enhanced with local details using the bilinear interpolation. As a result, the number of pixels in the image increase. Then, the enhanced images are fed into the proposed YOLOv3-SPP network for detection. In the proposed network, the IOU (Intersection Over Union, IOU) in the original YOLOv3 network is replaced with GIOU (Generalized Intersection Over Union, GIOU). In addition, we also add the SPP (Spatial Pyramid Pooling, SPP) module to improve the detection performance of the network. Finally, the official pre-trained weights of YOLOv3 are used for fast convergence. The test set test_pv from the Plant Village and the test set test_orchard from the orchard field were used to evaluate the network performance. The results of test_pv show that the grape leaf black rot is detected by the YOLOv3-SPP with 95.79% detection accuracy and 94.52% detector recall, which is a 5.94% greater in terms of accuracy and 10.67% greater in terms of recall as compared to the original YOLOv3. The results of test_orchard show that the method proposed in this paper can be applied in field environment with 86.69% detection precision and 82.27% detector recall, and the accuracy and recall were improved to 94.05 and 93.26% if the images with the simple background. Therefore, the detection method proposed in this work effectively solves the detection task of small targets and improves the detection effectiveness of the grape leaf black rot.