智能化农业装备学报（中英文）

土壤墒情监测技术研究现状与发展趋势

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高宁 ¹^,² , 张安琪 ²^,³ , 梅鹤波 ² , 杨兴华 ¹^,² , 甘蕾 ² , 孟志军 ¹^,²^,³^,^*

智能化农业装备学报（中英文） | 2024,5(3): 51-62

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智能化农业装备学报（中英文） | 2024 , 5 (3) : 51 -62

土壤墒情监测技术研究现状与发展趋势

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高宁¹^,², 张安琪²^,³, 梅鹤波², 杨兴华¹^,², 甘蕾², 孟志军¹^,²^,³^,^*

作者信息

^1.黑龙江八一农垦大学工程学院，黑龙江大庆，163319

^2.北京市农林科学院智能装备技术研究中心，北京市，100097

^3.智能农业动力装备全国重点实验室，北京市，100097

通讯作者:

^* 孟志军，男，1975年生，河南潢川人，博士，研究员；研究方向为农机智能装备。E-mail：mengzj@nercita.org.cn

作者简介:

高宁，男，1997年生，山东菏泽人，硕士研究生；研究方向为农机专用传感器。E-mail：gaoning5998@163.com

Current status and development trends of soil moisture monitoring technologies

Ning GAO¹^,², Anqi ZHANG²^,³, Hebo MEI², Xinghua YANG¹^,², Lei GAN², Zhijun MENG¹^,²^,³^,^*

Affiliations

^1.College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China

^2.Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

^3.State Key Laboratory of Intelligent Agricultural Power Equipment, Beijing 100097, China

出版时间: 2024-08-15 doi: 10.12398/j.issn.2096-7217.2024.03.006

文章导航

摘要

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随着农业生产对田间管理要求的不断提升，传统的农机设备已逐渐难以适应现代智慧农业的生产需求。在这一背景下，土壤墒情监测技术作为现代农田管理中获取土壤含水率信息的关键手段，正扮演着推动农机向自动化、智能化发展的重要角色。为此，深入梳理和分析国内外土壤墒情监测技术研究现状，重点围绕土壤墒情监测的方式及原理、模型构建算法以及信号处理方法这3个方面技术的研究进展进行阐述，通过对比分析，归纳与总结了国内外在监测方式、原理、模型构建算法和信号处理方法上的异同以及实际应用中存在的问题和面临的挑战，并提出了土壤墒情监测技术在这3个方面未来的发展趋势：土壤墒情监测方式方面，构建多源土壤墒情监测信息平台，以实现更全面的数据收集和分析；模型构建算法方面，通过采用机器学习和深度学习算法，为不同土壤环境和作业场景量身定制模型算法模块，提高监测设备准确性和适用性；信号处理方面，加强多源信号融合技术的应用，降低作业环境对监测设备的影响。

关键词

土壤墒情 / 机载式监测 / 原位监测 / 机器学习 / 深度学习 / 信号处理

Abstract

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As agricultural production continues to increase its requirements for field management, traditional agricultural machinery and equipment have gradually become difficult to meet the production needs of modern smart agriculture. In this context, soil moisture monitoring technology, as a key means of obtaining soil moisture information in modern agricultural management, is playing an important role in promoting the development of agricultural machinery towards intelligence and automation. Therefore, this study thoroughly reviews and analyzes the current research status of soil moisture monitoring technology at home and abroad, focusing on the research progress of three aspects of soil moisture monitoring methods and principles, model construction algorithms, and signal processing methods. Through comparative analysis, the differences and similarities in monitoring methods, principles, model construction algorithms, and signal processing methods at home and abroad, as well as the problems and challenges in practical applications, are summarized. The future development trends of soil moisture monitoring technology in these three aspects are proposed: in terms of soil moisture monitoring methods, a multi-source soil moisture monitoring information platform is constructed to achieve more comprehensive data collection and analysis; In terms of model construction algorithms, machine learning and deep learning algorithms are adopted to customize model algorithm modules for different soil environments and working scenarios, improving the accuracy and applicability of monitoring equipment; In terms of signal processing, the application of multi-source signal fusion technology is strengthened to reduce the impact of the working environment on monitoring equipment.

Key words

soil moisture / airborne monitoring / in situ monitoring / machine learning / deep learning / signal processing

引用本文

高宁, 张安琪, 梅鹤波, 杨兴华, 甘蕾, 孟志军. 土壤墒情监测技术研究现状与发展趋势. 智能化农业装备学报（中英文）, 2024 , 5 (3) : 51 -62 . DOI: 10.12398/j.issn.2096-7217.2024.03.006

Ning GAO, Anqi ZHANG, Hebo MEI, Xinghua YANG, Lei GAN, Zhijun MENG. Current status and development trends of soil moisture monitoring technologies[J]. Journal of Intelligent Agricultural Mechanization, 2024 , 5 (3) : 51 -62 . DOI: 10.12398/j.issn.2096-7217.2024.03.006

基金

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国家重点研发计划项目(2021YFD2000402-2)
山东省重点研发计划（重大科技创新工程）项目(2022CXGC010608)
云南省陈立平专家工作站(202105AF150030)

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2024年第5卷第3期

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文章信息

doi: 10.12398/j.issn.2096-7217.2024.03.006

接收时间：2024-03-05
首发时间：2026-11-05
出版时间：2024-08-15

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收稿日期：2024-03-05
修回日期：2024-07-03

基金

国家重点研发计划项目(2021YFD2000402-2)

山东省重点研发计划（重大科技创新工程）项目(2022CXGC010608)

云南省陈立平专家工作站(202105AF150030)

作者信息

^1.黑龙江八一农垦大学工程学院，黑龙江大庆，163319

^2.北京市农林科学院智能装备技术研究中心，北京市，100097

^3.智能农业动力装备全国重点实验室，北京市，100097

通讯作者:

^* 孟志军，男，1975年生，河南潢川人，博士，研究员；研究方向为农机智能装备。E-mail：mengzj@nercita.org.cn

参考文献

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https://castjournals.cast.org.cn/joweb/znhnyzbxb/CN/10.12398/j.issn.2096-7217.2024.03.006

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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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