博士，教授，博士生导师
陕西省农业信息感知与智能服务重点实验室主任
精准农业关键技术与装备学科团队负责人
陕西省杨凌示范区西农路22号
邮编：712100
电话：029-87092391
传真：029-87092391
Email：songyangfeifei#163.com
个人简介
宋怀波，男，山东济宁人，工学博士，教授，博士生导师，陕西省农业信息感知与智能服务重点实验室主任，精准农业关键技术与装备学科团队负责人。中国畜牧兽医学会信息技术分会副理事长，中国农业工程学会数字乡村专委会副主任委员、副秘书长，陕西农机学会第七届理事会常务理事，“科创中国”农业工程科技服务团高级专家，中国农业工程学会畜牧工程分会理事，陕西省图形图像学会理事，中国农学会农业监测预警分会理事、农业工程学会青年工作委员会委员，人工智能学会智能农业专委会会员。主要从事农情信息获取及识别、高通量表型快速获取技术、大型畜禽健康行为智能识别技术等方面的理论研究。先后主持国家重点研发计划项目、国家自然科学基金青年项目、面上项目，国家重点研发项目子课题，陕西省重点研发计划项目，陕西省自然科学基金，国家863 项目子课题。农业机械学报、农业大数据学报、中国牛业科学杂志编委，东北农业大学学报青年编委，Artificial Intelligence in Agriculture副主编，在Biosystems Engineering, Computers and Electronics in Agriculture等期刊发表SCI/EI收录期刊论文100余篇，入选Elsevier 2024“中国高被引学者”榜单、中国知网高被引学者Top1%。
一.教育背景
2004.9-2009.6 山东大学机械学院，机械电子工程专业（硕博连读）
二.工作经历
2009.7-2012.12, 讲师，西北农林科技大学机电学院
2013.1-2019.12，副教授，西北农林科技大学机电学院
2020.1-至今，教授，西北农林科技大学机电学院
三.研究领域
大型畜禽健康行识别技术
高通量植株表型获取技术
农情信息获取技术
四.开设课程
长期从事数字图像处理、智能检测与控制和农业信息技术的教学与科研工作，先后承担本科生数字图像处理、机器视觉与图像分析、数据库原理、电视原理等课程，为硕士研究生讲授图像分析与机器视觉技术课程，参编“十二五”国家级规划教材《数字图像处理》教材1部，主编《智慧农业工程案例》1部，副主编《畜禽智慧养殖技术与装备》1部，主讲的《数字图像处理》课程获批陕西省一流课程，获陕西省教学成果一等奖1项。
五.学术成果及代表性文章
（1）项目情况
1）牛羊智能饲喂与环控技术集成应用示范，国家重点研发计划项目，主持，在研，2100万元。
2）奶牛爬跨行为跨时空解析机理及发情状态表征模型研究，国家自然科学基金面上项目，主持，在研，50万元。
3）奶牛基本运动行为跨境识别机理及健康状况预测模型研究，国家自然科学基金面上项目，主持，在研，54万元。
4）奶牛智能健康识别方法研究，国家重点研发计划项目子课题，主持，结题，119万元。
（2）代表性文章
[1] Li, J., Zeng, P., Yue, S., Qin, L., Song, H. (2025) Automatic body condition scoring system for dairy cows in group state based on improved YOLOv5 and video analysis. Artificial Intelligence in Agriculture. 15(2), 350-362.
[2] Pu, L., Zhao, Y., Kang, H., Kong, X., Du, X., Song, H. (2025) DenseDFFNet: Dense connected dual-stream feature fusion network for calf manure segmentation and diarrhea recognition. Computers and Electronics in Agriculture. 234, 110328.
[3] Wang, Z., Hua, Z. Zhang, S., Xu, X., Wen Y., Song, H. (2025) Detection and tracking of oestrus dairy cows based on improved YOLOv8n and TransT models. Biosystems Engineering. 252, 61-76.
[4] Xu, X., Wang, Y., Shang, Y., Yang, G., Hua, Z., Wang, Z., Song, H. (2025) Few-shot cow identification via meta-learning. Information Processing in Agriculture, 12(1): 80-90.
[5] Deng, H., Yang, G., Xu, X., Hua, Z., Liu, J., Song, H.(2025) Fusion of CREStereo and MobileViT-Pose for rapid measurement of cattle body size, Computers and Electronics in Agriculture. 232, 110103.
[6] Xu, X., Deng, H., Wang, Y., Zhang, S., Song, H. (2024) Boosting cattle face recognition under uncontrolled scenes by embedding enhancement and optimization, 164: 111951.
[7] Yang, L., Xu, X., Zhao, J.,Song, H. (2024) Fusion of RetinaFace and Improved FaceNet for individual cow identification in natural scenes, Information Processing in Agriculture. 11, 512-523.
[8] Wang, Y., Xu, X., Zhang, S.,Wen, Y., Pu, L., Zhao, Y., Song, H. (2024) Adaptive group sample with central momentum contrast loss for unsupervised individual identification of cows in changeable conditions, Applied Soft Computing Journal. 167, 112340.
[9] Li, R., Wen, Y., Zhang, S., Xu, X., Ma, B., Song, H. (2024) Automated measurement of beef cattle body size via key point detection and monocular depth estimation, Expert Systems With Applications. 224, 123042.
[10] Wang, Z., Hua, Z., Wen, Y., Zhang, S., Xu, X.,Song, H. (2024) E-YOLO: Recognition of estrus cow based on improved YOLOv8n model. Expert Systems with Applications. 238, 122212.
[11] Wang, Y., Xu, X., Wang, Z.,Li, R., Hua, Z., Song, H. (2023) ShuffleNet-Triplet: A lightweight re-identification network for dairy cows in natural scenes, Computers and Electronics in Agriculture, 205,107632.
[12] Hua, Z., Wang, Z., Xu, X., Kong, X., Song, H. (2023) An effective PoseC3D model for typical action recognition of dairy cows based on skeleton features,Computers and Electronics in Agriculture. 212, 108152.
[13] Yang, G., Li, R., Zhang, S., Wen, Y., Xu, X., Song, H. (2023) Extracting cow point clouds from multi-view RGB images with an improved YOLACT++ instance segmentation, Expert Systems with Applications. 230, 120730.
[14] Wang, Y., Li, R., Wang, Z., Hua, Z., Jiao, Y., Duan, Y., Song, H. (2023) E3D: An Efficient 3D CNN for the recognition of dairy cow's basic motion behaviour. Computers and Electronics in Agriculture, 205 (2023) 107607:1-12.
[15] Yang, G., Xu, X., Song, L., Zhang, Q., Duan, Y.,Song, H. (2022) Automated measurement of dairy cows body size via 3D point cloud data analysis, Computers and Electronics in Agriculture. 200, 107218.
[16] Li, Z., Zhang, Q., Lv, S., Han, M., Jiang, M., Song, H. (2022) Fusion of RGB, optical flow and skeleton features for the detection of lameness in dairy cows. Biosystems Engineering, 218 (2022) 62-77
[17] Li, Z., Song, L., Duan, Y., Wang, Y., Song, H. (2022) Basic motion behaviour recognition of dairy cows based on skeleton and hybrid convolution.. Computers and Electronics in Agriculture, 196, 106889.
[18] Ma, S., Zhang, Q., Li, T., Song, H. (2022) Basic Motion Behavior Recognition of Single Dairy Cow Based on Improved Rexnet 3D Network. Computers and Electronics in Agriculture, 194, 106772.
[19] Jiang, B., Song, H., Wang, H,. Li C. (2022) Dairy cow lameness detection using a back curvature feature. 194, 106729.
[20] Tan, C., Li, C, He, D., Song, H.Towards real-time tracking and counting of seedlings with a one-stage detector and optical flow.Computers and Electronics in Agriculture, 193, 106683.
[21] Jiang, M., Song, L., Wang, Y., Li, Z,. Song, H. (2022) Fusion of the YOLOv4 network model and visual attention mechanism to detect low-quality young apples in a complex environment.Precision Agriculture, 2022,23(2): 559-577.
[22] Wu, D., Wang, Y., Han, M, Song, L. Shang, Y., Zhang, X., Song, H. (2021) Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Computers and Electronics in Agriculture.182, 106016.
[23] Wu, D., Lv, S., Jiang, M, Song, H. (2021) Using a CNN-LSTM for basic behaviors detection of a single dairy cow in a complex environment. Computers and Electronics in Agriculture. 178, 105742.
[24] Yin, X., Wu, D., Shang, Y., Jiang, B., Song, H. (2020) Using an EfficientNet-LSTM for the Recognition of Single Cow’s Motion Behaviours in a Complicated Environment. Computers and Electronics in Agriculture, 177, 105707.
[25] Jiang, B., Yin, X., Song, H. (2020) Single-stream long-term optical flow convolution network for action recognition of lameness dairy cow. Computers and Electronics in Agriculture, 175, 105536.
[26] Wu, D., Wu, Q. Yin, X, Jiang, B., Wang, H., He, D., Song, H. (2020) Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector. Biosystems Engineering, 189, 150-163.