基于计算机视觉的种子分布信息检测关键技术研究现状与趋势  

Research progress and the prospect of crucial technology of seed spacing information detection based on computer vision

作　　者：高振 卢彩云[1,2] 李洪文 何进[1,2] 王庆杰 郭朝阳[1,2] GAO Zhen;LU Caiyun;LI Hongwen;HE Jin;WANG Qingjie;GUO Zhaoyang(College of Agricultural Engineering,Jiangsu University,Zhenjiang 212013,China;Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]中国农业大学工学院,北京市100083 [2]农业农村部河北北部耕地保育科学观测实验站,北京市100083

出　　处：《智能化农业装备学报（中英文）》2023年第3期50-60,共11页Journal of Intelligent Agricultural Mechanization

基　　金：国家重点研发计划(2022YFD1500902);中国农业大学2115人才工程。

摘　　要：种子分布信息检测是保证播种机性能的重要前提,为播种质量检测、定深定位施肥、田间除草和收获等作物生长全过程精准高效管理提供基础数据。计算机视觉技术通过从图像和视频数据中提取有意义的信息,并通过分析解释物理现象,在多个领域得到广泛应用。在系统分析现有文献基础上,从技术应用场景、硬件设备、软件处理算法等角度探讨了计算机视觉技术在种子分布信息检测领域的研究进展。研究发现,计算机视觉技术在种子分布信息检测中仍然存在准确性和稳定性的挑战,且种子分布信息检测需要实时性和大规模数据处理能力,而传统的算法和设备难以满足这些需求,数据融合和分析的能力仍然有待提高。种子分布信息的准确检测需要综合利用多种传感器数据,并进行有效的数据处理和分析,以获取更全面和准确的结果。针对上述问题,提出以下建议:首先,深度学习和机器学习算法应用的广泛性是关键,通过利用大规模的标注数据进行模型训练,可以提高算法的准确性和鲁棒性;其次,需要提高算法的适应性,针对不同的土壤条件、种子类型和光照变化进行优化,以确保算法在各种环境下都能有效工作;同时,发展多传感器数据融合技术,通过将图像数据与其他传感器数据(如激光扫描和热红外图像)进行融合,可以提供更全面和准确的种子分布信息。本研究旨在为未来计算机视觉在种子分布信息检测领域的发展提供参考。Seed distribution information detection is an essential prerequisite for ensuring the performance of seeders,providing primary data for precise and efficient management of crop growth processes such as seed quality detection,depth determination and fertilization,field weeding,and harvesting.Computer vision technology has been widely applied in multiple fields by extracting meaningful information from image and video data and analyzing and explaining physical phenomena.Based on a systematic analysis of existing literature,this article explored the research progress of computer vision technology in seed distribution information detection from the perspectives of technology application scenarios,hardware devices,software processing algorithms,etc.The study found that computer vision technology still faces challenges in accuracy and stability in seed distribution information detection,and seed distribution information detection requires real-time and large-scale data processing capabilities.Traditional algorithms and devices are difficult to meet these needs,and data fusion and analysis capabilities still need to be improved.Accurate interpretation of seed distribution information requires comprehensive utilization of multiple sensor data and effective data processing and analysis to obtain more comprehensive and accurate results.In response to the above issues,we proposed the following suggestions:Firstly,the widespread application of deep learning and machine learning algorithms is crucial.By utilizing large-scale annotated data for model training,the accuracy and robustness of the algorithm can be improved;Secondly,it is necessary to improve the adaptability of the algorithm and optimize it for different soil conditions,seed types,and light changes to ensure that the algorithm can work effectively in various environments;At the same time,developing multi-sensor data fusion technology can provide more comprehensive and accurate seed distribution information by fusing image data with other sensor data,such as laser

关 键 词：计算机视觉 播种粒距 排种器 导种管 排种器试验台 播种末端 

分 类 号：S223.23[农业科学—农业机械化工程]