智能化农业装备学报（中英文）

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

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高振 ¹^,² , 卢彩云 ¹^,²^,^* , 李洪文 ¹^,² , 何进 ¹^,² , 王庆杰 ¹^,² , 郭朝阳 ¹^,²

智能化农业装备学报（中英文） | 2023,4(3): 50-60

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智能化农业装备学报（中英文） | 2023 , 4 (3) : 50 -60

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

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高振¹^,², 卢彩云¹^,²^,^*, 李洪文¹^,², 何进¹^,², 王庆杰¹^,², 郭朝阳¹^,²

作者信息

^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

通讯作者:

^* 卢彩云，女，1986年生，河北沧州人，硕士，副教授，博导；研究方向为保护性耕作。Email：lucaiyun@cau.edu.cn

作者简介:

高振，男，1994年生，山东济南，博士研究生；研究方向为智能农机装备。E-mail：1248010060@qq.com

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

Zhen GAO¹^,², Caiyun LU¹^,²^,^*, Hongwen LI¹^,², Jin HE¹^,², Qingjie WANG¹^,², Zhaoyang GUO¹^,²

Affiliations

^1.College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

^2.Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China

出版时间: 2023-08-15 doi: 10.12398/j.issn.2096-7217.2023.03.006

文章导航

摘要

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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。

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

Abstract

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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 scanning and thermal infrared images. This study aimed to provide a reference for the future development of computer vision in seed distribution information detection.

Key words

computer vision / seed spacing / seed metering device / seed guide tube / seed metering device test bench / seeding end

引用本文

高振, 卢彩云, 李洪文, 何进, 王庆杰, 郭朝阳. 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. 智能化农业装备学报（中英文）, 2023 , 4 (3) : 50 -60 . DOI: 10.12398/j.issn.2096-7217.2023.03.006

Zhen GAO, Caiyun LU, Hongwen LI, Jin HE, Qingjie WANG, Zhaoyang GUO. Research progress and the prospect of crucial technology of seed spacing information detection based on computer vision[J]. Journal of Intelligent Agricultural Mechanization, 2023 , 4 (3) : 50 -60 . DOI: 10.12398/j.issn.2096-7217.2023.03.006

基金

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

参考文献

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卢彩云，高振，李洪文，等．玉米播种装置及玉米播种质量检测方法[P]．中国：CN113079755B，2022-12-13.

2023年第4卷第3期

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doi: 10.12398/j.issn.2096-7217.2023.03.006

接收时间：2023-05-09
首发时间：2026-11-05
出版时间：2023-08-15

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收稿日期：2023-05-09
修回日期：2023-07-23

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国家重点研发计划(2022YFD1500902)

中国农业大学2115人才工程

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^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

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^* 卢彩云，女，1986年生，河北沧州人，硕士，副教授，博导；研究方向为保护性耕作。Email：lucaiyun@cau.edu.cn

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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