智能化农业装备学报（中英文）

工厂化育秧育苗智能播种性能检测技术应用现状与趋势

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邹学杰 ¹^,² , 刘大为 ¹^,²^,³^,⁴^,^*^,^* , 秦锋 ¹^,² , 李旭 ¹^,²^,³^,⁴ , 徐新明 ¹^,² , 龚明 ³^,⁴

智能化农业装备学报（中英文） | 2025,6(4): 91-102

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智能化农业装备学报（中英文） | 2025 , 6 (4) : 91 -102

工厂化育秧育苗智能播种性能检测技术应用现状与趋势

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邹学杰¹^,², 刘大为¹^,²^,³^,⁴^,^*^,^*, 秦锋¹^,², 李旭¹^,²^,³^,⁴, 徐新明¹^,², 龚明³^,⁴

作者信息

^1.湖南农业大学机电工程学院，湖南长沙，410128

^2.智能农机装备湖南省重点实验室，湖南长沙，410128

^3.农业农村部南方智能育秧（苗）重点实验室，湖南益阳，413055

^4.湖南省智能育秧育苗装备工程技术研究中心，湖南益阳，413055

通讯作者:

^* 刘大为，男，1983年生，湖南益阳人，博士，副教授；研究方向为农业机械设计理论及装备。E-mail：liudawei8361@163.com

作者简介:

邹学杰，男，1999年生，湖南祁东人，硕士研究生；研究方向为农业机械技术及智能装备。E-mail：zxj15200516360@163.com

Application status and trend of intelligent sowing performance detection technology in factory-based seedling raising

Xuejie ZOU¹^,², Dawei LIU¹^,²^,³^,⁴, Feng QIN¹^,², Xu LI¹^,²^,³^,⁴, Xinming XU¹^,², Ming GONG³^,⁴

Affiliations

^1.College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China

^2.Hunan Key Laboratory of Intelligent Agricultural Machinery Equipment, Changsha 410128, China

^3.Key Laboratory of Southern Intelligent Seedling, Ministry of Agriculture and Rural Affairs, Yiyang 413055, China

^4.Hunan Research Center of Engineering Technology for Intelligent Seedling Equipment, Yiyang 413055, China

Corresponding Author:

^* LIU Dawei, E-mail: liudawei8361@163.com

ZOU Xuejie, E-mail: zxj15200516360@163.com

出版时间: 2025-11-15 doi: 10.12398/j.issn.2096-7217.2025.04.009

文章导航

摘要

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随着农业科技的发展，工厂化育秧育苗技术在现代农业生产中逐渐成为重要的组成部分。智能播种检测技术的引入，显著提高了育苗过程的效率与精准度，推动了农业生产的智能化和自动化进程。播种智能检测是实现工厂化育秧育苗高效生产的关键。播种检测技术逐步从光电检测、电容检测转变为视觉检测与高光谱技术相结合等方式。概述了光电检测、电容检测、视觉检测、高光谱检测技术方法的适用对象，总结了现有光电、电容、视觉检测技术的优劣。在光电检测中研究人员一般在检测指标、重漏播检测、高尘环境检测精度改进3个方面进行研究，一般适用于大豆、玉米等大颗粒作物，对于电容检测主要分析电容结构对播种检测的影响，除极板结构优化方面外，通过信号处理进行算法优化，降低环境和信号干扰，提升了检测精度和工作稳定性，减少了误差率。对于视觉检测国内外研究主要针对漏播、种子流、种子下落速度、种子检测算法模型以及深度学习算法创新等方向研究，针对传统检测技术精度不高、电容检测距离远近、视觉检测误差较大等问题，提出育秧育苗智能播种检测技术从检测漏播到其他种子生化性质、预测育秧育苗产量的发展趋势，为构建基于高光谱技术智能育秧播种性能检测相关技术的发展提供重要的决策依据和借鉴意义。

关键词

光电检测 / 电容检测 / 视觉检测 / 播种性能 / 品质检测

Abstract

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With the development of agricultural science and technology, factory seedling nursery technology has gradually become an important part of modern agricultural production. The introduction of intelligent seeding detection technology significantly improves the efficiency and accuracy of the seedling nursery process and promotes the intelligent and automated process of agricultural production. Intelligent seeding detection is the key to achieving efficient production of factory seedling nursery. Seeding detection technology has gradually changed from photoelectric detection and capacitive detection to visual detection combined with hyperspectral technology and other methods. The applicable objects of photoelectric detection, capacitance detection, visual detection, and hyperspectral detection technology methods are outlined, and the advantages and disadvantages of existing photoelectric, capacitance, and visual detection technologies are summarised. In photoelectric detection researchers generally study three aspects of detection indicators, heavy leakage detection, and improvement of detection accuracy in high dust environments, which are generally applicable to large grain crops such as soybeans, corn, etc. For capacitive detection, the main analysis of capacitance structure on seeding detection, in addition to the optimization aspects of the polar plate structure, algorithm optimization through signal processing to reduce environmental and signal interference, improve detection accuracy and work stability, reducing the error rate. For the visual detection of domestic and international research is mainly aimed at the leakage of seeding, seed flow, seed falling speed seed, detection algorithm model and deep learning algorithm innovation and other directions of research, for the traditional detection technology accuracy is not high, capacitance detection distance is far away from the problem of visual detection error, put forward the development trend of intelligent seeding detection technology for seedlings and nursery seedlings from detecting leakage of seeding to the other seed biochemical properties, and predicting the yield of seedlings and nursery seedlings, and so on. Provide important decision-making basis and reference significance for building the development of intelligent seedling sowing performance detection related technology based on hyperspectral technology.

Key words

photoelectric detection / capacitive detection / visual detection / seeding performance / quality detection

引用本文

邹学杰, 刘大为, 秦锋, 李旭, 徐新明, 龚明. 工厂化育秧育苗智能播种性能检测技术应用现状与趋势. 智能化农业装备学报（中英文）, 2025 , 6 (4) : 91 -102 . DOI: 10.12398/j.issn.2096-7217.2025.04.009

Xuejie ZOU, Dawei LIU, Feng QIN, Xu LI, Xinming XU, Ming GONG. Application status and trend of intelligent sowing performance detection technology in factory-based seedling raising[J]. Journal of Intelligent Agricultural Mechanization, 2025 , 6 (4) : 91 -102 . DOI: 10.12398/j.issn.2096-7217.2025.04.009

基金

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国家重点研发计划项目(2020YFD1000305-4)
国家留学基金管理委员会资助项目(202308430207)
湖南省教育厅科学研究项目(21A0131)
湖南省教育厅科学研究项目(22B0186)

参考文献

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2025年第6卷第4期

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文章信息

doi: 10.12398/j.issn.2096-7217.2025.04.009

接收时间：2024-01-22
首发时间：2026-11-05
出版时间：2025-11-15

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收稿日期：2024-01-22
修回日期：2024-06-07

基金

National Key Research and Development Program of China(2020YFD1000305-4)

国家重点研发计划项目(2020YFD1000305-4)

China Scholarship Council(202308430207)

国家留学基金管理委员会资助项目(202308430207)

Hunan Provincial Education Department Scientific Research Project(21A0131)

湖南省教育厅科学研究项目(21A0131)

Hunan Provincial Education Department Research Project(22B0186)

湖南省教育厅科学研究项目(22B0186)

作者信息

^1.湖南农业大学机电工程学院，湖南长沙，410128

^2.智能农机装备湖南省重点实验室，湖南长沙，410128

^3.农业农村部南方智能育秧（苗）重点实验室，湖南益阳，413055

^4.湖南省智能育秧育苗装备工程技术研究中心，湖南益阳，413055

通讯作者:

^* 刘大为，男，1983年生，湖南益阳人，博士，副教授；研究方向为农业机械设计理论及装备。E-mail：liudawei8361@163.com

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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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