设施番茄生长模型与智能种植技术：研究进展与未来展望

doi:10.11924/j.issn.1000-6850.casb2024-0747

中国农学通报 ›› 2025, Vol. 41 ›› Issue (27): 54-70.doi: 10.11924/j.issn.1000-6850.casb2024-0747

• 资源·环境·生态·土壤 • 上一篇下一篇

设施番茄生长模型与智能种植技术：研究进展与未来展望

许佳¹(), 郑建华²(), 何鹏¹, 古乐¹, 霍耀¹, 汤顺杰¹

¹ 四川省农业科学院农业信息与农村经济研究所，成都 610060
² 中国农业科学院农业信息研究所/国家新闻出版署农业融合出版知识挖掘与知识服务重点实验室，北京 100081

收稿日期:2024-12-09 修回日期:2025-04-09 出版日期:2025-09-25 发布日期:2025-10-07
通讯作者:
郑建华，女，1986年出生，甘肃张掖人，高级工程师，博士研究生，研究方向：农业信息管理与知识服务。通信地址：100081 北京市海淀区中关村南大街12号中国农业科学院农业信息研究所，Tel：010-82109652-808，E-mail：zhengjianhua@caas.cn。
作者简介:
许佳，女，1996年出生，四川遂宁人，助理馆员，硕士研究生，研究方向：知识服务与科学计量学。通信地址：610060 四川省成都市锦江区净居寺路20号附101号四川省农业科学院农业信息与农村经济研究所，E-mail：xujia_tjnu@163.com。
基金资助:
国家新闻出版署农业融合出版知识挖掘与知识服务重点实验室开放课题“农业产业发展需求与关键生产知识耦合技术研究”(2024KMKS04); “天府粮仓”数字农业川渝联合创新重点实验室首批“揭榜挂帅”科技攻关任务“设施番茄智能种植策略模型及应用”[TFLCSZ-JB3(2023-2024)]

Growth Models and Smart Planting Technologies for Greenhouse Tomatoes: Research Progress and Future Prospects

XU Jia¹(), ZHENG Jianhua²(), HE Peng¹, GU Le¹, HUO Yao¹, TANG Shunjie¹

¹ Agricultural Information and Rural Economy Institute of Sichuan Academy of Agricultural Sciences, Chengdu 610060
² Agricultural Information Institute of Chinese Academy of Agricultural Sciences/ Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing, National Press and Publication Administration, Beijing 100081

Received:2024-12-09 Revised:2025-04-09 Published:2025-09-25 Online:2025-10-07

摘要/Abstract

摘要：

本研究旨在深入剖析设施番茄生长模型与智能种植技术的主要进展，为设施番茄产业的可持续发展提供科学参考。采用文献计量学方法，结合InCites和VOSviewer工具，从国家、机构、作者及研究主题等维度，全面回顾与分析该领域的研究成果与发展趋势。结果显示，近20年来，全球对设施番茄的研究关注度显著增加，中国、西班牙和加拿大等国家的研究尤为活跃。国外研究侧重于环境可持续性与资源高效利用，而中国研究则聚焦于水肥一体化与土壤微环境调控。生长模型方面，解释性模型展现出更强的优势。智能感知与决策技术在灌溉施肥、病虫害监测、果实识别与采摘等环节显著提高了生产效率和产品质量，并优化了生产管理模式，为农业自动化和智能化奠定了基础。未来研究方向应包括：深化生长模型研究，提升预测精度和适应性；推动智能化技术集成应用，促进设施番茄种植的智能化升级；加强抗逆性品种选育和精准灌溉施肥技术研发；推进病虫害识别技术的精准化和智能化。

关键词: 设施农业, 番茄, 环境因素, 生长模型, 智能种植技术, 研究进展

Abstract:

This study aims to comprehensively analyze the major advancements in growth models and smart planting technologies for greenhouse tomatoes, providing scientific insights for sustainable development in greenhouse tomato industry. Using bibliometrics, combined with InCites and VOSviewer, this study systematically reviews and analyzes research outcomes and trends in the field across dimensions of country, institution, author, and research topic. Over the past two decades, global research attention on greenhouse tomatoes has significantly increased, with active research in China, Spain, and Canada. Foreign studies focus on environmental sustainability and resource-use efficiency, while domestic research emphasizes integrated water-fertilizer management and soil micro-environment regulation. Among growth models, explanatory models demonstrate superior performance. Smart sensing and decision-making technology have substantially improved production efficiency and product quality in irrigation-fertilization, pest and disease monitoring, and fruit identification and harvesting, while optimizing production management and laying the foundation for agricultural automation and intelligence. Future research should deepen growth-model developments to enhance predictive accuracy and adaptability, promote integrated development of intelligent technologies to advance smart greenhouse tomato cultivation, strengthen breeding for stress-resistant cultivars and precision irrigation-fertilization techniques, and advance the precision and intelligence of pest and disease identification technologies.

Key words: protected agriculture, tomatoes, environmental factors, growth models, smart planting technologies, research advancements

许佳, 郑建华, 何鹏, 古乐, 霍耀, 汤顺杰. 设施番茄生长模型与智能种植技术：研究进展与未来展望[J]. 中国农学通报, 2025, 41(27): 54-70.

XU Jia, ZHENG Jianhua, HE Peng, GU Le, HUO Yao, TANG Shunjie. Growth Models and Smart Planting Technologies for Greenhouse Tomatoes: Research Progress and Future Prospects[J]. Chinese Agricultural Science Bulletin, 2025, 41(27): 54-70.

图/表 8

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姓名	所属机构	发文量 /篇	Q1 期刊论文	被引频次 /次	CNCI值	国际合作论文百分比/%	国内合作论文百分比/%	国家 /地区
龚雪文	华北水利水电大学	14	10	297	2.39	14.29	71.43	中国
Rieradevall, Joan	巴塞罗那自治大学	13	12	671	1.86	38.46	61.54	西班牙
杜太生	中国农业大学	13	11	696	3.03	53.85	30.77	中国
Katsoulas, Nikolaos	塞萨利大学	13	11	196	0.83	61.54	7.69	希腊
康绍忠	中国农业大学	11	9	552	3.00	36.36	36.36	中国
Hao, Xiuming	加拿大农业与农业食品部	10	2	42	5.60	40	50	加拿大
Fatnassi, H.	法国国家航天研究院	10	5	356	1.33	80	0	法国
林杉	中国农业大学	10	10	400	2.53	90	10	中国
葛建坤	华北水利水电大学	10	7	165	2.10	20	70	中国
Gabarrell, Xavier	巴塞罗那自治大学	10	9	312	1.93	50	40	西班牙

姓名	所属机构	发文量 /篇	Q1 期刊论文	被引频次 /次	CNCI值	国际合作论文百分比/%	国内合作论文百分比/%	国家 /地区
龚雪文	华北水利水电大学	14	10	297	2.39	14.29	71.43	中国
Rieradevall, Joan	巴塞罗那自治大学	13	12	671	1.86	38.46	61.54	西班牙
杜太生	中国农业大学	13	11	696	3.03	53.85	30.77	中国
Katsoulas, Nikolaos	塞萨利大学	13	11	196	0.83	61.54	7.69	希腊
康绍忠	中国农业大学	11	9	552	3.00	36.36	36.36	中国
Hao, Xiuming	加拿大农业与农业食品部	10	2	42	5.60	40	50	加拿大
Fatnassi, H.	法国国家航天研究院	10	5	356	1.33	80	0	法国
林杉	中国农业大学	10	10	400	2.53	90	10	中国
葛建坤	华北水利水电大学	10	7	165	2.10	20	70	中国
Gabarrell, Xavier	巴塞罗那自治大学	10	9	312	1.93	50	40	西班牙

维度	描述性模型	解释性模型
模型精度	在模型构建的特定条件下，精度较高；但外推到新条件时，模型精度显著下降	在特定条件下，精度可能略低于经过精细校准的描述性模型；但在环境变化时，仍能保持相对稳定的精度
可解释性	可解释性低。模型参数通常缺乏明确的生物学意义，被视为“黑箱”，难以揭示作物生长的内在机制	可解释性高。模型基于已知的生理生态机制构建，参数具有明确的物理或生物学意义，能有效解释模拟结果
数据需求	数据需求相对较低。主要依赖特定环境下的作物生长数据，用于统计回归分析	数据需求高。需要大量详尽的生理、生态和环境数据来支撑复杂的机理过程
计算复杂度	计算复杂度低。模型结构简单，通常由少量数学方程组成，计算速度快，适合在线实时控制	计算复杂度高。模型结构复杂，涉及多个相互作用的子模块，计算量大，对计算机性能要求较高
适用范围	适用范围窄。模型基于特定条件建立，难以推广到其他品种、地点或环境条件，外推能力弱	适用范围广。基于普适的生理生态原理，可适用于不同品种、地点和环境条件，具有较强的通用性和外推能力
模型耦合潜力	模型耦合潜力低。由于模型参数的特定性和缺乏机制基础，与气候模型、经济模型等的耦合能力有限	模型耦合潜力高。模块化的结构设计使其易于与土壤模型、病虫害模型、经济模型等进行耦合，构建更复杂的综合系统

维度	描述性模型	解释性模型
模型精度	在模型构建的特定条件下，精度较高；但外推到新条件时，模型精度显著下降	在特定条件下，精度可能略低于经过精细校准的描述性模型；但在环境变化时，仍能保持相对稳定的精度
可解释性	可解释性低。模型参数通常缺乏明确的生物学意义，被视为“黑箱”，难以揭示作物生长的内在机制	可解释性高。模型基于已知的生理生态机制构建，参数具有明确的物理或生物学意义，能有效解释模拟结果
数据需求	数据需求相对较低。主要依赖特定环境下的作物生长数据，用于统计回归分析	数据需求高。需要大量详尽的生理、生态和环境数据来支撑复杂的机理过程
计算复杂度	计算复杂度低。模型结构简单，通常由少量数学方程组成，计算速度快，适合在线实时控制	计算复杂度高。模型结构复杂，涉及多个相互作用的子模块，计算量大，对计算机性能要求较高
适用范围	适用范围窄。模型基于特定条件建立，难以推广到其他品种、地点或环境条件，外推能力弱	适用范围广。基于普适的生理生态原理，可适用于不同品种、地点和环境条件，具有较强的通用性和外推能力
模型耦合潜力	模型耦合潜力低。由于模型参数的特定性和缺乏机制基础，与气候模型、经济模型等的耦合能力有限	模型耦合潜力高。模块化的结构设计使其易于与土壤模型、病虫害模型、经济模型等进行耦合，构建更复杂的综合系统

设施番茄生长模型与智能种植技术：研究进展与未来展望

Growth Models and Smart Planting Technologies for Greenhouse Tomatoes: Research Progress and Future Prospects

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