特色农产品设施环境下品质智能管控技术研究现状与展望

doi:10.12133/j.smartag.SA202411017

Smart Agriculture ›› 2024, Vol. 6 ›› Issue (6): 44-62.doi: 10.12133/j.smartag.SA202411017

• 专题--农业知识智能服务和智慧无人农场（上） • 上一篇下一篇

特色农产品设施环境下品质智能管控技术研究现状与展望

郭威¹^,²^,³^,⁴(), 吴华瑞¹^,²^,³^,⁴, 郭旺¹^,²^,³^,⁴, 顾静秋¹^,²^,³^,⁴, 朱华吉¹^,²^,³^,⁴()

^1. 国家农业信息化工程技术研究中心，北京 100097，中国
^2. 北京市农林科学院信息技术研究中心，北京 100097，中国
^3. 农业农村部数字乡村技术重点实验室，北京 100097，中国
^4. 农业农村部农业信息技术重点实验室，北京 100097，中国

收稿日期:2024-11-08 出版日期:2024-11-30
基金项目:
国家重点研发计划(2023YFD1600304); 北京市农林科学院改革与发展项目
作者简介:
郭威，研究方向为农业大数据与人工智能。E-mail：guowei007@nercita.org.cn
通信作者:
朱华吉，博士，研究员，研究方向为农业大数据与人工智能。E-mail：zhuhj007@nercita.org.cn

Research Status and Prospect of Quality Intelligent Control Technology in Facilities Environment of Characteristic Agricultural Products

GUO Wei¹^,²^,³^,⁴(), WU Huarui¹^,²^,³^,⁴, GUO Wang¹^,²^,³^,⁴, GU Jingqiu¹^,²^,³^,⁴, ZHU Huaji¹^,²^,³^,⁴()

^1. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
^2. Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
^3. Key Laboratory of Digital Village Technology, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
^4. Key Laboratory of Agri-Informatics, Ministry of Agriculture and Rural Affairs, Beijing 100097, China

Received:2024-11-08 Online:2024-11-30
Foundation items:The National Key Research and Development Program of China(2023YFD1600304); Reform and Development Project of Beijing Academy of Agriculture and Forestry Sciences
About author:
GUO Wei, E-mail: guowei007@nercita.org.cn
Corresponding author:
ZHU Huaji, E-mail: zhuhj007@nercita.org.cn

摘要/Abstract

摘要：

［目的/意义］ 针对中国中西部特色农产品在设施环境生产过程对品质影响要素监测手段缺乏、智能化管控能力薄弱、品质控制要素耦合关系不明、系统化应用程度低的问题，对现有的设施智能监测、智能管控、平台构建技术进行探索，通过物联网、云平台，以及新一代人工智能技术的应用，为特色农产品全生育期的智能化品质管控体系构建和应用提供依据。 ［进展］ 以特色羊肚菌和番茄的外观、口感、产量三类品质为管控目标，在品质调控数据采集方面介绍了视频监控、环境监测、生理表型数据采集设备，以及轨道式、轮式巡检机器人，提供稳定可靠的图像、视频、监测数据采集手段；在生长过程调控方面，分别从环境监测调控和肥水灌溉投入品调控阐释了当前的做法，提出了能够针对品质进行多要素耦合全生育期的管控方法；介绍了品质智能管控系统，以及多个生产环节的应用，采用云边端一体化架构为获取的多模态数据以及多要素耦合模型面向多种生产场景提供精准适配的应用和服务。 ［结论/展望］ 对比特色羊肚菌和番茄的智能化管控现状，番茄整体技术体系更为成熟，羊肚菌产业可以结合自身的生产过程根据技术的需要进行技术迁移，对严苛光照、稳定的高湿低温等个性化要求进行模型定制，面向中西部大部分特色农产品品控宜采用通用型的多要素耦合模型根据场景特性进行参数、模型局部迁移和微调的方式适配。未来的发展趋势呈现设备设施轻简化、智能技术场景化、服务模式多样化、品质管控在线化、数智生产规模化、数据要素价值化，有效利用智能装备和数据要素的优势是将来激发新质生产力的必然趋势。

关键词: 特色农产品, 多要素耦合, 智能管控模型, 多模态知识图谱, 农业大模型, 新质生产力

Abstract:

[Significance] In view of the lack of monitoring means of quality influence factors in the production process of characteristic agricultural products with in central and western regions of China, the weak ability of intelligent control, the unclear coupling relationship of quality control elements and the low degree of systematic application, the existing technologies described such as intelligent monitoring of facility environment, growth and nutrition intelligent control model, architecture of intelligent management and control platform and so on. Through the application of the Internet of Things, big data and the new generation of artificial intelligence technology, it provides technical support for the construction and application of intelligent process quality control system for the whole growth period of characteristic agricultural products. [Progress] The methods of environmental regulation and nutrition regulation are analyzed, including single parameters and combined control methods, such as light, temperature, humidity, CO₂ concentration, fertilizer and water, etc. The multi-parameter coupling control method has the advantage of more comprehensive scene analysis. Based on the existing technology, a multi-factor coupling method of integrating growth state, agronomy, environment, input and agricultural work is put forward. This paper probes into the system architecture of the whole process service of quality control, the visual identification system of the growth process of agricultural products and the knowledge-driven agricultural technical service system, and introduces the technology of the team in the disease knowledge Q & A scene through multi-modal knowledge graph and large model technology. [Conclusions and Prospects] Based on the present situation of the production of characteristic facility agricultural products and the overall quality of farmers in the central and western regions of China, it is appropriate to transfer the whole technical system such as facility tomato, facility cucumber and so on. According to the varieties of characteristic agricultural products, cultivation models, quality control objectives to adapt to light, temperature, humidity and other parameters, as well as fertilizer, water, medicine and other input plans, a multi-factor coupling model suitable for a specific planting area is generated and long-term production verification and model correction are carried out. And popularize it in a wider area, making full use of the advantages of intelligent equipment and data elements will promote the realization of light simplification of production equipment, scene of intelligent technology, diversification of service models, on-line quality control, large-scale production of digital intelligence, and value of data elements, further cultivate facilities to produce new quality productivity.

Key words: characteristic agricultural products, multi-factor coupling, intelligent management and control model, multimodal knowledge graph, agricultural large model, new quality productivity

中图分类号:

TP391
S-1

郭威, 吴华瑞, 郭旺, 顾静秋, 朱华吉. 特色农产品设施环境下品质智能管控技术研究现状与展望[J]. 智慧农业(中英文), 2024, 6(6): 44-62.

GUO Wei, WU Huarui, GUO Wang, GU Jingqiu, ZHU Huaji. Research Status and Prospect of Quality Intelligent Control Technology in Facilities Environment of Characteristic Agricultural Products[J]. Smart Agriculture, 2024, 6(6): 44-62.

图/表 10

图1

图2

图3

图4

表1

设施环境面向不同参数研究方法和调控效果

作物	调控参数	研究方法	调控效果	文献
番茄	光照	基于最小显著性差异法（Least-Significant Difference，LSD）方法对不同补光方案进行多重比较	相较于红蓝LED光源，采用较低色温的白色LED照明更有利于促进番茄幼苗的生物量积累以及光合色素的合成	李志鑫等^［51］
	通风	基于麻雀搜索算法优化长短期记忆（Long Short-Term Memory，LSTM）网络，实现温室温度预测	通过实地部署验证，温室智能通风调控下番茄产量相较于传统温室模式，增长了约8.5%	章子文等^［52］
	温度	基于神经网络算法和遗传算法建立不同环境条件下番茄光合速率预测模型	不同环境下番茄适宜温度不同，在该模型作用下，一个生产日单位面积番茄幼苗的光合积累提升了一倍	吴清丽等^［53］
	臭氧	基于臭氧浓度阈值判断，通过闭环控制实现温室臭氧杀菌浓度精准控制	臭氧杀菌的闭环调控体系能有效对抗叶霉病、灰霉病及特定害虫，其控制效率分别高达89.3%与92.2%	李新澳等^［54］
	温度、光照	研究线性回归模型表征温室外温度与温室内日光积分	获得了对番茄生长适宜温度和光照条件	전숙례等^［55］
	土壤温湿度、空气温湿度	建立了多层前馈反向传播（Back Propagation，BP）神经网络模型预测温室光照、空气湿度、土壤温度和土壤湿度	预测土壤温湿度和空气温湿度的平均相对误差均在 5% 以内，是调节和预测番茄生长温室环境的可靠依据	Zhang等^［56］
	温度	利用LSTM、门控递归单元和双向LSTM建立了温室温度预测模型	根据预测温度成功模拟了番茄叶面积指数、鲜重和地上干物质，R ²值均高于0.80	Lin等^［57］
	湿度	开发了一种温室除湿需求预测。基于作物特性、温室环境和室外气候，预测了每小时除湿需求的变化，并确定了控制湿度所需的除湿机容量	模型输出通过番茄温室的实际除湿要求进行验证。预测值和测量值之间的R ²值达到0.89，均方根误差值小于最大除湿负荷的10%，具有良好的一致性	Rahman等^［58］
	CO₂	利用番茄光合速率机理模型构建温室当前CO₂与需求CO₂浓度预测模型	温室内CO₂供应效率受通风 CO₂损失的影响大于净同化率的影响	Choi等^［59］
	温度、湿度、辐射、CO₂浓度、外部气候	利用迁移学习和BiLSTM预测温室温度、相对湿度、辐射、CO₂浓度和外部气候温度	BiLSTM模型可以预测环境变化趋势，应用于番茄数据集后得到的预测数据平均R ²分别为0.78和0.81	Moon等^［60］
羊肚菌	光照强度、气温、空气相对湿度、O₂浓度、CO₂浓度、土壤温度和土壤含水率	利用物联网监测数据建立当地羊肚菌栽培生态环境参数的24 h波动模型	经过实验测定，温室环境下羊肚菌达到最高产量时的晴天正午平均光照强度范围大致在2 500至3 500 lx之间。对于累积的空气与土壤温度、空气中的O₂与CO₂浓度以及土壤的湿度水平等关键因素，并未观察到明显的相关性变化	谭昊等^［61］
	地膜	研究7种不同颜色的地膜培育梯棱羊肚菌和六妹羊肚菌。测量地膜内外环境的光照强度、气温、土壤温度，并统计羊肚菌的产量，探究不同颜色地膜对羊肚菌整个生长发育周期的影响	实验得到黑膜、白黑膜、银黑膜以及银膜由于其膜内温度相对较低，不仅能够有效控制杂草的生长，同时也为羊肚菌菌丝的生长创造了有利条件。另一方面，透明膜、绿膜和红膜因其高透光性，对羊肚菌的子囊、子囊孢子和侧丝的正常发育有着积极影响，出菇整齐，生长周期适中，产量较高	李仔密等^［62］
	温度	研究聚焦于不同温度条件对梯棱羊肚菌菌丝生长、特定抗氧化酶的活性及其基因表达水平，以及抗氧化活性物质含量的影响	在5~25 ℃的温度范围内，随着温度的上升，菌丝的生长速率加快，但同时老化过程也加速。在面临温度胁迫时，羊肚菌通过激活不同的抗氧化酶和增加抗氧化活性物质的含量来减少活性氧的积累，从而减轻对菌丝的损害	白静等^［63］
	温度、湿度及CO₂浓度	构建基于注意力机制的卷积神经网络与LSTM神经网络融合模型，旨在预测温室内的温度、湿度及CO₂变化	构建模型在预测温度、湿度及二氧化碳浓度时的误差分别为0.17 ℃（R ²=0.974）、2.06%（R ²=0.804）及8.367 ppm（R ²=0.993）。结果表明，模型在预测食用蘑菇温室内环境参数方面具有较高的准确	Huang等^［64］

表1

图5

表2

设施环境下营养调控方法与效果

作物	调控参数	研究方法	调控效果	文献
番茄	水肥预测	基于遗传算法构建盐碱地基质栽培番茄的最优水肥调控模型	调控模型所得最优灌溉水量、施肥量与实验所得最优灌溉施肥方案接近	赵文举等^［76］
	肥料预测	构建针对目标产量下温室番茄氮磷钾肥料施用量的预测模型。分别运用基于麻雀搜索算法的神经网络模型、改进后的麻雀搜索算法神经网络模型，以及混合算法神经网络模型预测温室番茄的氮磷钾肥料施用量	在高土壤肥力条件下，与基于麻雀搜索算法的神经网络模型相比，其他两种模型的均方误差（Mean Squared Error， MSE）分别降低至0.007和0.005，解释方差分数（Explained Variance Score，EVS）分别提升至0.871和0.908，R ²则分别提升至0.862和0.899	Yu等^［77］
	水肥量与产量耦合	构建了TOPSIS（Technique for Order Preference by Similarity to Ideal Solution）模型，旨在预测滴灌水量与施肥量对番茄水肥生产力、产量及质量指标的综合效应	实验得到当番茄的作物系数（Kcp）值位于0.74~0.78之间，施肥系数（Kf）值在1.13~1.27之间时，番茄的生长表现最佳	Yu等^［78］
	灌溉量温湿度耦合	利用全球定位系统（Global Positioning System，GPS）模块定位灌溉地点，自动计算当日的日出、日落及日中时间，并融合椰糠基质的吸水特性与番茄日常水分需求的变化，将一天动态地划分为四个灌溉阶段。同时，结合温室内的温湿度以及排液电导率，选择标准灌溉模式或特定洗盐模式	相较于辐射累积控制灌溉，该灌溉模式的灌溉量增加了1.8%，肥料使用量减少7.3%，排液比例下降7.9%，且排液的EC值降低了9.3%；而与定时灌溉相比，灌溉量减少11.3%，肥料使用量降低20.0%，排液比例下降17.9%，排液的EC值也下降了4.9%	刘天翔等^［79］
	灌溉量蒸腾量耦合	利用传感器测量番茄单株每小时的蒸腾量数据，分析番茄蒸腾活动与温室内部环境因素之间的时间滞后关系，构建了融入光辐射延迟效应的温室番茄蒸腾量预测模型	晴朗天气条件下，考虑光辐射延迟效应（1 h）的温室番茄蒸腾模型表现出最小的误差和最高的预测精度。在番茄果实发育的关键阶段，采用模型推荐的灌溉参数（1.2倍预测蒸腾量）能够实现最佳的灌溉管理	孙一鑫等^［80］
	施肥对比	基于水肥一体化技术研究了5种施肥水平（以N计， N0：0 kg/hm²、N1：150 kg/hm² 、N2：300 kg/hm² 、N3：450 kg/hm² 、N4：600 kg/hm²，N-P-K=15-5-25）下番茄生长和氮素养分累积动态变化	N2处理下温室番茄的氮肥农学利用率最高，为N3、N4处理的1.9和2.9倍（P<0.05）。综合考虑番茄生长、产量品质及肥料利用率等指标，本试验条件下，N2施肥水平为供试水肥一体化条件下的适宜肥料用量	韩雪等^［81］
	水肥多要素耦合	以灌水量、施N量、施K₂O量、施CaO量为试验因子，进行正交旋转组合设计，利用多层次模糊评判方法对表征樱桃番茄生长的4类因素及14类子因素进行综合评判，构建水肥多因子对樱桃番茄综合生长的调控模型	灌水量为补充至灌溉上限用量的91.34%~100%、施N量为12.26~13.50 g/株、施K₂O量为2.92~5.13 g/株、施CaO量为2.69~4.39 g/株时，多层次模糊综合评判指数有最优区间，最有利于樱桃番茄的综合生长	张智等^［82］
	灌溉决策与专家知识融合	在灌溉决策模型中考虑了作物实时状态，构建了模块化番茄知识图谱，施用路径排序法计算灌溉调整值，对彭曼模型初始值调整，选择专家知识最高分	提出的基于Penman-Monteith（P-M）模型和路径排序算法相结合的方法比传统P-M模型方法和基于视觉经验方法的果实总产量、单株果实均产量、果实均重、果实均重百分比分别提高了5 346.1 g、178.2 g、5.16 g、2.22%和875.2g、29.17 g、9.54 g、4.18%	张宇等^［83］
羊肚菌	营养	通过实地实验，设立小麦占比45%（定义为高浓度）与20%（定义为低浓度）的两种外置营养袋配比，并结合四种不同数量的营养袋配置，系统评估了六妹羊肚菌的农业性状、产出量以及其子实体的营养构成	不同处理下的菌盖长度、直径，以及单个菌菇的重量等农业性状并未展现出明显差异。高浓度组中，随着营养袋数量的增多，产量呈现显著上升趋势，最高纪录达到了每667 m²产量1 028.52 kg；相比之下，低浓度组内的产量差异不显著，最低为每667 m²产量576.10 kg	魏子涵等^［84］

表2

图6

图7

图8

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特色农产品设施环境下品质智能管控技术研究现状与展望

Research Status and Prospect of Quality Intelligent Control Technology in Facilities Environment of Characteristic Agricultural Products

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