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

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

Abstract

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

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.

Figures/Tables 8

References 127

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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