智能化农业装备学报（中英文）

全景视觉技术在农业信息感知中的应用现状与发展趋势

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王超柱 ¹ , 程俊潞 ² , 乔璐 ³ , 李文春 ⁴ , 邱威 ² , 田光兆 ²^,^*^,^*

智能化农业装备学报（中英文） | 2026,7(2): 142-152

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智能化农业装备学报（中英文） | 2026 , 7 (2) : 142 -152

全景视觉技术在农业信息感知中的应用现状与发展趋势

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王超柱¹, 程俊潞², 乔璐³, 李文春⁴, 邱威², 田光兆²^,^*^,^*

作者信息

^1.江苏省农业机械试验鉴定站，江苏南京，210017

^2.南京农业大学工学院，江苏南京，211800

^3.农业农村部南京农业机械化研究所，江苏南京，210014

^4.图木舒克银丰现代农业装备有限公司，新疆图木舒克，843900

通讯作者:

^* 田光兆，男，1983年生，河南信阳人，博士，副教授；研究方向为智能化植保技术与装备。E-mail：tgz@njau.edu.cn

作者简介:

王超柱，男，1981年生，江苏南京人，硕士，高级工程师；研究方向为农业机械智能化鉴定技术。E-mail：wangchaozhu1981@163.com

Application status and development trends of panoramic vision technology in agricultural information perception

Chaozhu Wang¹, Junlu Cheng², Lu Qiao³, Wenchun Li⁴, Wei Qiu², Guangzhao Tian²

Affiliations

^1.Jiangsu Agricultural Machinery Testing and Appraisal Station, Nanjing 210017, China

^2.College of Engineering, Nanjing Agricultural University, Nanjing 211800, China

^3.Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China

^4.Tumushuke Yinfeng Modern Agricultural Equipment Co., Ltd., Tumushuke 843900, China

Corresponding Author:

^* Tian Guangzhao, E-mail: tgz@njau.edu.cn

Wang Chaozhu, E-mail: wangchaozhu1981@163.com

出版时间: 2026-05-15 doi: 10.12398/j.issn.2096-7217.2026.02.013

文章导航

摘要

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在传统农业向智慧农业转型的过程中，农业场景对规模化、高精度信息感知的需求日益增长。传统单、双目视觉由于视野受限，难以满足复杂环境下全方位监测的要求。全景视觉技术凭借360°无死角视野、信息获取快速完整及实时性强等优势，已成为农业智能化升级中不可替代的关键技术手段。本文旨在系统梳理全景视觉系统在农业领域的应用现状与发展现状，为全景视觉技术与农业装备的深度融合提供理论参考。以成像原理和硬件结构为依据，将系统划分为云台旋转式、多相机阵列式、鱼眼镜头式及折反射镜式四类架构，并结合典型案例分析其在作物监测、表型调查及农机导航等领域的应用演进。研究表明，全景视觉已形成多层次应用格局：云台旋转式系统畸变小，但实时性差，适用于静态长势监测；多相机系统帧率高且位姿误差小，能够支撑动态高维表型重构与定位；鱼眼系统结构简单，经校正可抑制边缘畸变，实现广域监控；折反射系统紧凑且无延迟，导航避障偏差小。当前全景视觉在农业应用中仍存在复杂环境适应性瓶颈、动态场景实时处理短板、三维信息重构效率与精度不足以及技术标准化与系统兼容性缺失四大问题。为此，未来应将全景视觉技术聚焦于多模态传感器融合感知，利用深度学习驱动模型自适应优化以提升泛化能力，并与农业物联网深度协同构建感知—决策—调控一体化体系。同时，加强技术开源化与行业标准化建设，将加速全景视觉技术在精准农业中的规模化落地应用。

关键词

全景视觉 / 智慧农业 / 深度学习 / 信息感知 / 多传感器融合 / 三维重建

Abstract

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In the transition from traditional agriculture to smart agriculture, the demand for large-scale, high-precision information perception in agricultural scenarios is increasing. Traditional monocular and binocular vision, limited by their field of view, struggle to meet the requirements of all-round monitoring in complex environments. Panoramic vision technology, characterized by 360° unobstructed field of view, fast and complete information acquisition, and strong real-time performance, has become an irreplaceable key technical means in the intelligent upgrading of agriculture. This paper aims to systematically review the application status and development progress of panoramic vision systems in the agricultural field, providing theoretical reference for the in-depth integration of panoramic vision technology and agricultural equipment. Based on imaging principles and hardware structures, the systems are classified into four architectural types: pan-tilt rotating type, multi-camera array type, fisheye lens type, and catadioptric type. Combined with typical cases, the application evolution in fields such as crop monitoring, phenotypic investigation, and agricultural machinery navigation is analyzed. The research shows that panoramic vision has formed a multi-level application pattern: pan-tilt rotating systems have small distortion but poor real-time performance, suitable for static growth monitoring; multi-camera systems have high frame rates and small pose errors, capable of supporting dynamic high-dimensional phenotypic reconstruction and positioning; fisheye systems have a simple structure, and edge distortion can be suppressed through calibration to achieve wide-area monitoring; catadioptric systems are compact with no delay and small deviations in navigation and obstacle avoidance. Currently, panoramic vision in agricultural applications still faces four major issues: bottlenecks in adaptability to complex environments, shortcomings in real-time processing of dynamic scenes, insufficient efficiency and accuracy of 3D information reconstruction, and lack of technical standardization and system compatibility. Therefore, in the future, panoramic vision technology should focus on multi-modal sensor fusion perception, use deep learning-driven adaptive model optimization to improve generalization ability, and deeply collaborate with the agricultural Internet of Things to build an integrated perception-decision-regulation system. Meanwhile, strengthening technology open-sourcing and industry standardization will accelerate the large-scale application of panoramic vision technology in precision agriculture.

Key words

panoramic vision / smart agriculture / deep learning / information perception / multi-sensor fusion / 3D reconstruction

引用本文

王超柱, 程俊潞, 乔璐, 李文春, 邱威, 田光兆. 全景视觉技术在农业信息感知中的应用现状与发展趋势. 智能化农业装备学报（中英文）, 2026 , 7 (2) : 142 -152 . DOI: 10.12398/j.issn.2096-7217.2026.02.013

Chaozhu Wang, Junlu Cheng, Lu Qiao, Wenchun Li, Wei Qiu, Guangzhao Tian. Application status and development trends of panoramic vision technology in agricultural information perception[J]. Journal of Intelligent Agricultural Mechanization, 2026 , 7 (2) : 142 -152 . DOI: 10.12398/j.issn.2096-7217.2026.02.013

基金

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新疆生产建设兵团第三师图木舒克市科技计划项目(KY2025JBGS10)
政府采购项目(JSZB-2024100013)
国家重点研发计划(2024YFD2001101)

参考文献

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2026年第7卷第2期

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doi: 10.12398/j.issn.2096-7217.2026.02.013

接收时间：2025-12-25
首发时间：2026-11-05
出版时间：2026-05-15

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收稿日期：2025-12-25
修回日期：2026-03-16

基金

Science and Technology Program of the Third Division, Xinjiang Production and Construction Corps, Tumsuk City(KY2025JBGS10)

新疆生产建设兵团第三师图木舒克市科技计划项目(KY2025JBGS10)

Government Procurement Project(JSZB-2024100013)

政府采购项目(JSZB-2024100013)

National Key Research and Development Program of China(2024YFD2001101)

国家重点研发计划(2024YFD2001101)

作者信息

^1.江苏省农业机械试验鉴定站，江苏南京，210017

^2.南京农业大学工学院，江苏南京，211800

^3.农业农村部南京农业机械化研究所，江苏南京，210014

^4.图木舒克银丰现代农业装备有限公司，新疆图木舒克，843900

通讯作者:

^* 田光兆，男，1983年生，河南信阳人，博士，副教授；研究方向为智能化植保技术与装备。E-mail：tgz@njau.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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