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

doi:10.12133/j.smartag.SA202411017

Abstract

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

CLC Number:

TP391
S-1

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.

Figures/Tables 10

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Table 1

Research methods and control effects of different parameters are applied to facility environment

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］

Table 1

Fig. 5

Table 2

Methods and effects of nutrition regulation in facility environment

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］

Table 2

Fig. 6

Fig. 7

Fig. 8

References 120

1	李天来, 齐明芳, 孟思达. 中国设施园艺发展60年成就与展望[J]. 园艺学报, 2022, 49(10): 2119-2130.
	LI T L, QI M F, MENG S D. Sixty years of facility horticulture development in China: Achievements and prospects[J]. Acta horticulturae sinica, 2022, 49(10): 2119-2130.
2	赵春江. 智慧农业发展现状及战略目标研究[J]. 智慧农业(中英文), 2019, 1(1): 1-7.
	ZHAO C J. State-of-the-art and recommended developmental strategic objectivs of smart agriculture[J]. Smart agriculture, 2019, 1(1): 1-7.
3	樊胜根, 龙文进, 孟婷. 加快形成农业新质生产力引领农业强国建设[J/OL]. 中国农业大学学报(社会科学版), 1-15. [2024-11-19].
	FAN S G, LONG W J, MENG T. Accelerating new quality agricultural productive forces to build up China's strength in agriculture[J/OL]. Journal of China agicultural university(social sciences), 1-15. [2024-11-19].
4	LI Y, LIU P, LI B.Water and fertilizer integration intelligent control system of tomato based on internet of things[C]// Cloud Computing and Security, 4th International Conference, ICCCS 2018. Cham, Germany: Springer, 2018: 209-220.
5	SĂCĂLEANU D I, MATACHE M G, ROȘU Ș G, et al. IoT-enhanced decision support system for real-time greenhouse microclimate monitoring and control[J]. Technologies, 2024, 12(11): ID 230.
6	DOOYUM UYEH D, AKINSOJI A, ASEM-HIABLIE S, et al. An online machine learning-based sensors clustering system for efficient and cost-effective environmental monitoring in controlled environment agriculture[J]. Computers and electronics in agriculture, 2022, 199: ID 107139.
7	TASHAKKORI R, HAMZA A S, CRAWFORD M B. Beemon: An IoT-based beehive monitoring system[J]. Computers and electronics in agriculture, 2021, 190: ID 106427.
8	WOLFERT S, GE L, VERDOUW C, et al. Big data in smart farming: A review[J]. Agricultural systems, 2017, 153: 69-80.
9	AMIRI-ZARANDI M, DARA R A, DUNCAN E, et al. Big data privacy in smart farming: A review[J]. Sustainability, 2022, 14(15): ID 9120.
10	YANG F, LIU Z Q, WANG Y X, et al. A variety test platform for the standardization and data quality improvement of crop variety tests[J]. Frontiers in plant science, 2023, 14: ID 1077196.
11	LIU K N, YU J R, HUANG Z W, et al. Autonomous navigation system for greenhouse tomato picking robots based on laser SLAM[J]. Alexandria engineering journal, 2024, 100: 208-219.
12	HU W Y, HONG W, WANG H K, et al. A study on tomato disease and pest detection method[J]. Applied sciences, 2023, 13(18): ID 10063.
13	LIU J, WANG X W, ZHU Q Y, et al. Tomato brown rot disease detection using improved YOLOv5 with attention mechanism[J]. Frontiers in plant science, 2023, 14: ID 1289464.
14	NAWAZ M, NAZIR T, JAVED A, et al. A robust deep learning approach for tomato plant leaf disease localization and classification[J]. Scientific reports, 2022, 12(1): ID 18568.
15	ZU L L, HAN M Z, LIU J Q, et al. Design and experiment of nondestructive post-harvest device for tomatoes[J]. Agriculture, 2022, 12(8): ID 1233.
16	KHAN A, HASSAN T, SHAFAY M, et al. Tomato maturity recognition with convolutional transformers[J]. Scientific reports, 2023, 13(1): ID 22885.
17	FEI Z J, JOUNG J G, TANG X M, et al. Tomato functional genomics database: A comprehensive resource and analysis package for tomato functional genomics[J]. Nucleic acids research, 2011, 39(): D1156-D1163.
18	SURESH B V, ROY R, SAHU K, et al. Tomato genomic resources database: An integrated repository of useful tomato genomic information for basic and applied research[J]. PLoS one, 2014, 9(1): ID e86387.
19	KIM H, KIM B S, SHIM J E, et al. TomatoNet: A genome-wide co-functional network for unveiling complex traits of tomato, a model crop for fleshy fruits[J]. Molecular plant, 2017, 10(4): 652-655.
20	ZHANG N, WU H R, ZHU H J, et al. Tomato disease classification and identification method based on multimodal fusion deep learning[J]. Agriculture, 2022, 12(12): ID 2014.
21	FUKADA K, HARA K, CAI J Y, et al. An automatic tomato growth analysis system using YOLO transfer learning[J]. Applied sciences, 2023, 13(12): ID 6880.
22	GUO X D, SUN Y, YANG H. FF-net: Feature-fusion-based network for semantic segmentation of 3D plant point cloud[J]. Plants, 2023, 12(9): ID 1867.
23	GAO J, ZHANG J X, ZHANG F, et al. LACTA: A lightweight and accurate algorithm for cherry tomato detection in unstructured environments[J]. Expert systems with applications, 2024, 238: ID 122073.
24	LI R Z, JI Z J, HU S K, et al. Tomato maturity recognition model based on improved YOLOv5 in greenhouse[J]. Agronomy, 2023, 13(2): ID 603.
25	TZACHOR A, DEVARE M, RICHARDS C, et al. Large language models and agricultural extension services[J]. Nature food, 2023, 4: 941-948.
26	MENG X X, LI J, SU X L, et al. Large-scale ontology development and agricultural application based on knowledge domain framework[J]. Journal of integrative agriculture, 2012, 11(5): 808-822.
27	OPENAI, ACHIAM J, ADLER S, et al. GPT-4 technicalreport[EB/OL]. arXiv: 2303.08774v6, 2024.
28	BALDUCCI F, IMPEDOVO D, PIRLO G. Machine learning applications on agricultural datasets for smart farm enhancement[J]. Machines, 2018, 6(3): ID 38.
29	宋玉琴, 姬引飞, 朱紫娟. 基于CC2530和CC2592集群温室环境监测系统的设计[J]. 现代电子技术, 2015, 38(22): 69-72.
	SONG Y Q, JI Y F, ZHU Z J. Design of CC2530 and CC2592 based environmental monitoring system for gather greenhouses[J]. Modern electronics technique, 2015, 38(22): 69-72.
30	李亚迪, 苗腾, 朱超, 等. 北方日光温室智能监控系统的设计与实现[J]. 中国农业科技导报, 2016, 18(5): 94-101.
	LI Y D, MIAO T, ZHU C, et al. The design and implementation of intelligent monitoring system for solar greenhouse in Northern China[J]. Journal of agricultural science and technology, 2016, 18(5): 94-101.
31	尚志跃, 曾成, 张馨, 等. 基于商业云平台植物工厂环境监测系统的研究与实现[J]. 农机化研究, 2017, 39(9): 7-13, 30.
	SHANG Z Y, ZENG C, ZHANG X, et al. Research and implementation of plant environmental monitoring system based on commercial cloud platform[J]. Journal of agricultural mechanization research, 2017, 39(9): 7-13, 30.
32	曹帅, 钱谦, 张娅玲, 等. 基于LoRa的智慧农田土壤环境监测系统研究[J]. 农业装备与车辆工程, 2024, 62(1): 18-22.
	CAO S, QIAN Q, ZHANG Y L, et al. Research on LoRa-based smart farmland soil environment monitoring system[J]. Agricultural equipment & vehicle engineering, 2024, 62(1): 18-22.
33	QIN X Y, WU G P, LEI J, et al. Detecting inspection objects of power line from cable inspection robot LiDAR data[J]. Sensors, 2018, 18(4): ID 1284.
34	杨代强. 矿用智能轮式巡检机器人路径跟踪及运动分析[J]. 煤矿机械, 2020, 41(2): 96-98.
	YANG D Q. Path tracking and motion analysis of mining intelligent wheel inspection robot[J]. Coal mine machinery, 2020, 41(2): 96-98.
35	龙卓群, 雷日兴. 履带式行走机器人避障自动控制系统设计[J]. 自动化与仪器仪表, 2018(8): 68-70.
	LONG Z Q, LEI R X. Design of automatic obstacle avoidance control system for tracked walking robot[J]. Automation & instrumentation, 2018(8): 68-70.
36	王建平, 杨宗晔. 温室兰花生长状态监测移动视觉机器人的研究[J]. 中国农机化学报, 2016, 37(10): 185-194, 253.
	WANG J P, YANG Z Y. Research of mobile visual robot for orchid growth state monitoring in greenhouse[J]. Journal of Chinese agricultural mechanization, 2016, 37(10): 185-194, 253.
37	ROLDÁN J J, GARCIA-AUNON P, GARZÓN M, et al. Heterogeneous multi-robot system for mapping environmental variables of greenhouses[J]. Sensors, 2016, 16(7): ID 1018.
38	BAI G, GE Y F, SCOBY D, et al. NU-Spidercam: A large-scale, cable-driven, integrated sensing and robotic system for advanced phenotyping, remote sensing, and agronomic research[J]. Computers and electronics in agriculture, 2019, 160: 71-81.
39	赵春江, 吴华瑞, 刘强, 等. 基于Voronoi的无线传感器网络覆盖控制优化策略[J]. 通信学报, 2013, 34(9): 115-122.
	ZHAO C J, WU H R, LIU Q, et al. Optimization strategy on coverage control in wireless sensor network based on Voronoi[J]. Journal on communications, 2013, 34(9): 115-122.
40	AKKAŞ M A, SOKULLU R. An IoT-based greenhouse monitoring system with Micaz motes[J]. Procedia computer science, 2017, 113: 603-608.
41	赵轩, 纪文刚, 卓思超. 基于GPRS网络的温室大棚远程监控系统设计[J]. 工业仪表与自动化装置, 2017(1): 98-101, 122.
	ZHAO X, JI W G, ZHUO S C. Design of remote monitoring system of greenhouse based on GPRS[J]. Industrial instrumentation & automation, 2017(1): 98-101, 122.
42	潘鹤立, 景林, 钟凤林, 等. 基于ZigBee和3G/4G技术的分布式果园远程环境监控系统的设计[J]. 福建农林大学学报(自然科学版), 2014, 43(6): 661-667.
	PAN H L, JING L, ZHONG F L, et al. Design of remote environmental monitoring and control system for distributed orchard based on the technologies of ZigBee and 3G/4G[J]. Journal of Fujian agriculture and forestry university (natural science edition), 2014, 43(6): 661-667.
43	陈栋. 多源异构农业感知数据接入系统的设计与实现[D]. 泰安: 山东农业大学, 2014.
	CHEN D. Design and implementation of multi-source and heteogeneous agricultural sensor data access system[D]. Tai'an: Shandong Agricultural University, 2014.
44	赵瑞华, 赵妍, 贺晓龙. 羊肚菌菌核形成影响因素与分子机制研究进展[J]. 食用菌学报, 2024, 31(5): 104-112.
	ZHAO R H, ZHAO Y, HE X L. Advances in influencing factors and molecular mechanisms of sclerotium formation in morchella spp[J]. Acta edulis fungi, 2024, 31(5): 104-112.
45	朱继荣, 王成莹, 耿丽, 等. 羊肚菌菌核生物发生及其影响因素研究进展[J]. 食药用菌, 2024, 32(3): 149-156.
	ZHU J R, WANG C Y, GENG L, et al. Research progress on the biogenesis and influencing factors of sclerotium in Morchella[J]. Edible and medicinal mushrooms, 2024, 32(3): 149-156.
46	MORALES-GARCÍA J, BUENO-CRESPO A, MARTÍN EZ-ESPAÑA R, et al. Evaluation of low-power devices for smart greenhouse development[J]. The journal of supercomputing, 2023, 79: 10277-10299
47	LOUKATOS D, LYGKOURA K A, MARAVEAS C, et al. Enriching IoT modules with edge AI functionality to detect water misuse events in a decentralized manner[J]. Sensors, 2022, 22(13): ID 4874.
48	韦玉翡, 赵建贵, 高安琪, 等. 温室环境参数模糊专家控制系统的设计[J]. 江苏农业科学, 2021, 49(6): 183-188.
	WEI Y F, ZHAO J G, GAO A Q, et al. Design of fuzzy expert control system for greenhouse environmental parameters[J]. Jiangsu agricultural sciences, 2021, 49(6): 183-188.
49	王梦阳, 李亚敏, 曾立华. 大田卷盘拖管水溶肥平移灌溉控制系统的设计[J]. 农机化研究, 2018, 40(1): 80-84.
	WANG M Y, LI Y M, ZENG L H. Design of the control system of water soluble fertilizer in the field coil[J]. Journal of agricultural mechanization research, 2018, 40(1): 80-84.
50	SENGUPTA A, MUKHERJEE A, DAS A, et al. Growfruit: An IoT-based radial growth rate monitoring device for fruit[J]. IEEE consumer electronics magazine, 2022, 11(3): 38-43.
51	李志鑫, 王一鸣, 魏嵘喆, 等. 不同色温LED补光对温室番茄幼苗质量的影响[J]. 山东农业科学, 2024, 56(8): 80-86.
	LI Z X, WANG Y M, WEI R Z, et al. Effects of supplementary LED light with different color temperature on quality of greenhouse tomato seedlings[J]. Shandong agricultural sciences, 2024, 56(8): 80-86.
52	章子文, 梁思程, 张洪奇, 等. 设施温室物联网智能测控系统研究[J]. 山东农业大学学报(自然科学版), 2024, 55(4): 633-643.
	ZHANG Z W, LIANG S C, ZHANG H Q, et al. Research on intelligent measurement and control system of greenhouse Internet of Things[J]. Journal of Shandong agricultural university (natural science edition), 2024, 55(4): 633-643.
53	吴清丽, 高攀, 高茂盛. 多智能算法融合的苗期番茄最佳温度决策方法[J]. 西北农业学报, 2024, 33(9): 1795-1805.
	WU Q L, GAO P, GAO M S. A decision-making method for optimal temperature of tomato seedling based on multi-intelligent algorithms[J]. Acta agriculturae boreali-occidentalis sinica, 2024, 33(9): 1795-1805.
54	李新澳, 刘元义, 王丽娟, 等. 设施农业臭氧杀菌闭环控制系统设计与试验[J]. 中国农机化学报, 2024, 45(9): 62-68.
	LI X A, LIU Y Y, WANG L J, et al. Design and experiment of closed-loop control system for ozone sterilization in facility agriculture[J]. Journal of Chinese agricultural mechanization, 2024, 45(9): 62-68.
55	전숙례, 이진흥, , 등. 김성억 기상 및 환경 센서 데이터 기반 생육 환경 최적화 연구[J]. 센서학회지, 2024, 33(4): 230-236.
	JEON S L, LEE J, KIM S E, et al. Optimization of growth environments based on meteorological and environmental sensor data[J]. Journal of sensor science and technology, 2024, 33(4): 230-236.
56	ZHANG W, ZHONG W Y, LIU Z D, et al. Precision regulation and forecasting of greenhouse tomato growth conditions using an improved GA-BP model[J]. Sustainability, 2024, 16(10): ID 4161.
57	LIN Y S, FANG S L, KANG L, et al. Combining recurrent neural network and sigmoid growth models for short-term temperature forecasting and tomato growth prediction in a plastic greenhouse[J]. Horticulturae, 2024, 10(3): ID 230.
58	RAHMAN M S, GUO H Q, HAN J J. Dehumidification requirement modelling and control strategy for greenhouses in cold regions[J]. Computers and electronics in agriculture, 2021, 187: ID 106264.
59	CHOI S H, WOO Y H, JANG D C, et al. Model-based calculations of ventilation rate, carbon dioxide (CO₂) absorption rate by plants, and amounts of ventilated and supplied CO₂ for tomato hydroponic greenhouse during a winter season[J]. Horticultural science and technology, 2023, 41(4): 379-389.
60	MOON T, SON J E. Knowledge transfer for adapting pre-trained deep neural models to predict different greenhouse environments based on a low quantity of data[J]. Computers and electronics in agriculture, 2021, 185: ID 106136.
61	谭昊, 苗人云, 刘天海, 等. 成都平原地区梯棱羊肚菌人工栽培生态环境参数范围及变化规律[J]. 西南农业学报, 2021, 34(1): 27-39.
	TAN H, MIAO R Y, LIU T H, et al. Ranges and dynamics of environmental factors for morchella importuna cultivation in Chengdu Plain area[J]. Southwest China journal of agricultural sciences, 2021, 34(1): 27-39.
62	李仔密, 马渊浩, 刘萍, 等. 不同颜色地膜对羊肚菌生长发育的影响[J]. 食用菌学报, 2023, 30(3): 19-29.
	LI Z M, MA Y H, LIU P, et al. Effects of colored plastic mulches on growth and development of morchella[J]. Acta edulis fungi, 2023, 30(3): 19-29.
63	白静, 陈明杰, 唐利华, 等. 温度对梯棱羊肚菌抗氧化酶活及其基因表达的影响[J]. 菌物学报, 2021, 40(12): 3276-3285.
	BAI J, CHEN M J, TANG L H, et al. Effects of temperature on antioxidant enzyme activities and gene expression in morchella importuna[J]. Mycosystema, 2021, 40(12): 3276-3285.
64	HUANG S G, LIU Q Y, WU Y, et al. Edible mushroom greenhouse environment prediction model based on attention CNN-LSTM[J]. Agronomy, 2024, 14(3): ID 473.
65	胡瑾. 基于作物光合需求的设施光环境调控方法与技术研究[D]. 杨凌: 西北农林科技大学, 2016.
	HU J. Research on method and technology of light environment control of facility based on crop photosynthetic demand[D]. Yangling: Northwest A & F University, 2016.
66	黄曼绮, 王旭. 基于TRIZ的温室大棚机器人监测系统设计[J]. 实验室研究与探索, 2023, 42(5): 88-91, 148.
	HUANG M Q, WANG X. Design of monitoring system for greenhouse robot based on TRIZ[J]. Research and exploration in laboratory, 2023, 42(5): 88-91, 148.
67	郭威, 吴华瑞, 朱华吉. 设施温室影像采集与环境监测机器人系统设计及应用[J]. 智慧农业(中英文), 2020, 2(3): 48-60.
	GUO W, WU H R, ZHU H J. Design and application of facility greenhouse image collecting and environmental data monitoring robot system[J]. Smart agriculture, 2020, 2(3): 48-60.
68	程雅雯, 康睿, 任妮, 等. 基于改进YOLOv7的设施番茄苗期株高检测方法研究[J]. 中国农业信息, 2024, 36(2): 1-16.
	CHENG Y W, KANG R, REN N, et al. An improved YOLOv7-based height detection method for seedling tomato in greenhouse[J]. China agricultural informatics, 2024, 36(2): 1-16.
69	FAN X X, LU N, XU W S, et al. Response of flavor substances in tomato fruit to light spectrum and daily light integral[J]. Plants, 2023, 12(15): ID 2832.
70	孙亚楠, 段琳博, 钟华昱, 等. 温室气体排放与番茄产量对水肥气耦合的响应机制研究[J]. 农业机械学报, 2024, 55(5): 312-322.
	SUN Y N, DUAN L B, ZHONG H Y, et al. Response mechanism of soil greenhouse gas emission and yield of greenhouse tomato to water-fertilizer-air coupling[J]. Transactions of the Chinese society for agricultural machinery, 2024, 55(5): 312-322.
71	杨洁, 左太强, 顾鲁同, 等. 环境因子对金耳和羊肚菌生长的调控解析[J]. 蔬菜, 2023(10): 53-56.
	YANG J, ZUO T Q, GU L T, et al. Effects of environmental factors on growth of tremella aurantialba and morchella esculenta[J]. Vegetables, 2023(10): 53-56.
72	边银丙. 浅析羊肚菌稳产高产相关的科学技术问题[J]. 食用菌学报, 2024, 31(1): 31-37.
	BIAN Y B. A brief analysis of scientific and technical problems related to stable and high yield of morels[J]. Acta edulis fungi, 2024, 31(1): 31-37.
73	贺国强, 魏金康, 刘奇正, 等. 羊肚菌产业发展情况及展望[J]. 蔬菜, 2021(4): 27-32.
	HE G Q, WEI J K, LIU Q Z, et al. Development and prospect of morehella esculenta industry[J]. Vegetables, 2021(4): 27-32.
74	伏俊伟. 日光温室羊肚菌高压雾化及环境监测系统研究[D]. 阿拉尔: 塔里木大学, 2024.
	FU J W. Research on high-pressure atomization and environmental monitoring system for Morchella in solar greenhouse[D]. Alaer: Tarim University, 2024.
75	卢营蓬, 王春霞, 易文裕, 等. 羊肚菌分段式变温热风干燥试验研究[J]. 中国农机化学报, 2020, 41(4): 111-116.
	LU Y P, WANG C X, YI W Y, et al. Experimental study on hot-air drying of Morchella under stages-varying temperatures process[J]. Journal of Chinese agricultural mechanization, 2020, 41(4): 111-116.
76	赵文举, 曹伟, 吴克倩, 等. 盐碱地水肥耦合对基质栽培番茄生长与产量品质的影响[J]. 排灌机械工程学报, 2024, 42(6): 619-626.
	ZHAO W J, CAO W, WU K Q, et al. Effects of water and fertilizer coupling on growth, yield and quality of tomato in saline-alkali soil[J]. Journal of drainage and irrigation machinery engineering, 2024, 42(6): 619-626.
77	YU X Y, LUO Y Z, BAI B, et al. Prediction model of nitrogen, phosphorus, and potassium fertilizer application rate for greenhouse tomatoes under different soil fertility conditions[J]. Agronomy, 2024, 14(6): ID 1165.
78	YU X M, ZHANG J W, ZHANG Y H, et al. Identification of optimal irrigation and fertilizer rates to balance yield, water and fertilizer productivity, and fruit quality in greenhouse tomatoes using TOPSIS[J]. Scientia horticulturae, 2023, 311: ID 111829.
79	刘天翔, 郭文忠, 韩晓蓓, 等. 基于称量反馈的设施番茄灌溉系统的构建与应用[J]. 农业工程学报, 2024, 40(13): 85-96.
	LIU T X, GUO W Z, HAN X B, et al. Construction and application of a weighing feedback-based irrigation system for facility tomatoes[J]. Transactions of the Chinese society of agricultural engineering, 2024, 40(13): 85-96.
80	孙一鑫, 马乐乐, 苗丽丽, 等. 基于光辐射时滞效应的温室番茄蒸腾量模型的构建[J]. 西北农林科技大学学报(自然科学版), 2023, 51(2): 83-92.
	SUN Y X, MA L L, MIAO L L, et al. Construction of greenhouse tomato transpiration model based on light radiation time-lag effect[J]. Journal of northwest A & F university (natural science edition), 2023, 51(2): 83-92.
81	韩雪, 曲梅, 李银坤, 等. 不同施肥水平对温室番茄生长、氮吸收及产量品质的影响[J]. 中国土壤与肥料, 2021(2): 162-169.
	HAN X, QU M, LI Y K, et al. Effects of different fertilization levels on tomato growth, nitrogen uptake, yield and quality in greenhouse[J]. Soil and fertilizer sciences in China, 2021(2): 162-169.
82	张智, 和志豪, 洪婷婷, 等. 基于多层次模糊评判的樱桃番茄综合生长水肥耦合调控[J]. 农业机械学报, 2019, 50(12): 278-287.
	ZHANG Z, HE Z H, HONG T T, et al. Coupling regulation of water and fertilizer factors for comprehensive growth of cherry tomatoes based on multi-level fuzzy comprehensive evaluation[J]. Transactions of the Chinese society for agricultural machinery, 2019, 50(12): 278-287.
83	张宇, 郭文忠, 林森. 基于Penman-Monteith模型和PRA算法的番茄灌溉研究[J/OL]. 吉林农业大学学报, 2022. (2022-04-29)[2024-10-20].
	ZHANG Y, GUO W Z, LIN S.Tomato irrigation based on Penman-Monteith model and PRA algorithm[J/OL]. Journal of Jilin agricultural university, 2022. (2022-04-29)[2024-10-20].
84	魏子涵, 魏军, 孔繁建, 等. 不同外源营养袋配方和放置数量对六妹羊肚菌营养品质的影响[J]. 菌物学报, 2024, 43(11): 134-145.
	WEI Z H, WEI J, KONG F J, et al. Effects of different formulation and consumption of bagged exogenous nutrient on the nutritional properties of morchella sextelata[J]. Mycosystema, 2024, 43(11): 134-145.
85	黄天艺, 吴华瑞, 朱华吉. 基于多模态数据驱动的黄瓜温室湿度预测方法[J]. 电子测量技术, 2023, 46(16): 97-104.
	HUANG T Y, WU H R, ZHU H J. Research on humidity prediction method of cucumber greenhouse based on multi-mode data driving[J]. Electronic measurement technology, 2023, 46(16): 97-104.
86	李珊珊, 赵春江, 吴华瑞, 等. 基于CNN-BiLSTM融合神经网络的温室甘蓝潜在蒸散量预测[J/OL]. 吉林农业大学学报, 2021. (2021-11-16)[2024-10-22].
	LI S S, ZHAO C J, WU H R, et al.Prediction of potential evapotranspiration of greenhouse cabbage based on CNN-BiLSTM fusion neural network[J/OL]. Journal of Jilin agricultural university, 2021. (2021-11-16)[2024-10-22].
87	何峰, 吴华瑞, 史扬明, 等. 基于改进SlowFast模型的设施黄瓜农事行为识别方法[J]. 智慧农业(中英文), 2024, 6(3): 118-127.
	HE F, WU H R, SHI Y M, et al. Recognition method of facility cucumber farming behaviours based on improved SlowFast model[J]. Smart agriculture, 2024, 6(3): 118-127.
88	吴华瑞, 钟熙, 赵春江, 等. 农村社区动态交互式三维虚拟漫游系统的实现[J]. 农业工程学报, 2008, 24(2): 176-180.
	WU H R, ZHONG X, ZHAO C J, et al. Realization of the dynamic interactive 3D virtual wandering system in the rural community based on VRML[J]. Transactions of the Chinese society of agricultural engineering, 2008, 24(2): 176-180.
89	王亚楠, 吴华瑞, 黄锋. 高并发Web应用系统的性能优化分析与研究[J]. 计算机工程与设计, 2014, 35(8): 2976-2981.
	WANG Y N, WU H R, HUANG F. Optimization analysis and research of high concurrency Web application system performance[J]. Computer engineering and design, 2014, 35(8): 2976-2981.
90	钱春阳, 王建春, 李凤菊, 等. 基于FRAM和NB-IOT的设施温室智能环境采集系统设计[J]. 江西农业学报, 2019, 31(5): 122-125.
	QIAN C Y, WANG J C, LI F J, et al. Design of intelligent environment acquisition system for facility greenhouse based on FRAM and NB-IOT[J]. Acta agriculturae Jiangxi, 2019, 31(5): 122-125.
91	李扬, 王建春, 吴华瑞, 等. 基于LoRa与模糊控制的温室环境调控系统设计[J]. 江苏农业科学, 2021, 49(20): 210-216.
	LI Y, WANG J C, WU H R, et al. Design of greenhouse environment control system based on LoRa and fuzzy control[J]. Jiangsu agricultural sciences, 2021, 49(20): 210-216.
92	于景鑫, 杜森, 吴勇, 等. 基于云原生技术的土壤墒情监测系统设计与应用[J]. 农业工程学报, 2020, 36(13): 165-172.
	YU J X, DU S, WU Y, et al. Design and application of soil moisture content monitoring system based on cloud-native technology[J]. Transactions of the Chinese society of agricultural engineering, 2020, 36(13): 165-172.
93	杨改改, 高贤强. 基于微服务架构的农业数据云存储研究[J]. 浙江农业科学, 2022, 63(4): 853-856.
	YANG G G, GAO X Q. Research on agricultural data cloud storage platform based on micro-services[J]. Journal of Zhejiang agricultural sciences, 2022, 63(4): 853-856.
94	杨信廷, 王明亭, 徐大明, 等. 基于区块链的农产品追溯系统信息存储模型与查询方法[J]. 农业工程学报, 2019, 35(22): 323-330.
	YANG X T, WANG M T, XU D M, et al. Data storage and query method of agricultural products traceability information based on blockchain[J]. Transactions of the Chinese society of agricultural engineering, 2019, 35(22): 323-330.
95	孙传恒, 于华竟, 徐大明, 等. 农产品供应链区块链追溯技术研究进展与展望[J]. 农业机械学报, 2021, 52(1): 1-13.
	SUN C H, YU H J, XU D M, et al. Review and prospect of agri-products supply chain traceability based on blockchain technology[J]. Transactions of the Chinese society for agricultural machinery, 2021, 52(1): 1-13.
96	刘双印, 雷墨鹥兮, 徐龙琴, 等. 基于区块链的农产品质量安全可信溯源系统研究[J]. 农业机械学报, 2022, 53(6): 327-337.
	LIU S Y, LEI M Y X, XU L Q, et al. Development of reliable traceability system for agricultural products quality and safety based on blockchain[J]. Transactions of the Chinese society for agricultural machinery, 2022, 53(6): 327-337.
97	杨锋, 吴华瑞, 朱华吉, 等. 基于Hadoop的海量农业数据资源管理平台[J]. 计算机工程, 2011, 37(12): 242-244.
	YANG F, WU H R, ZHU H J, et al. Massive agricultural data resource management platform based on hadoop[J]. Computer engineering, 2011, 37(12): 242-244.
98	江顺, 陈荣宇, 林伟君, 等. 基于大数据的智能农业云服务平台设计与实现[J]. 安徽农业科学, 2022, 50(16): 190-197.
	JIANG S, CHEN R Y, LIN W J, et al. Design and implementation of intelligent agricultural cloud service platform based on big data technology[J]. Journal of Anhui agricultural sciences, 2022, 50(16): 190-197.
99	WSPANIALY P, MOUSSA M. A detection and severity estimation system for generic diseases of tomato greenhouse plants[J]. Computers and electronics in agriculture, 2020, 178: ID 105701.
100	郑超杰, 李少波, 蒲睿强, 等. 基于轻量化卷积神经网络的番茄叶片病害识别[J]. 江苏农业科学, 2024, 52(11): 225-231.
	ZHENG C J, LI S B, PU R Q, et al. Tomato leaf disease recognition based on lightweight convolutional neural network[J]. Jiangsu agricultural sciences, 2024, 52(11): 225-231.
101	刘拥民, 刘翰林, 石婷婷, 等. 一种优化的Swin Transformer番茄叶片病害识别方法[J]. 中国农业大学学报, 2023, 28(4): 80-90.
	LIU Y M, LIU H L, SHI T T, et al. Tomato leaf disease recognition based on an optimized Swin Transformer[J]. Journal of China agricultural university, 2023, 28(4): 80-90.
102	李淑菲, 李凯雨, 乔岩, 等. 基于可见光光谱和改进YOLOv5的自然场景下黄瓜病害检测方法[J]. 光谱学与光谱分析, 2023, 43(8): 2596-2600.
	LI S F, LI K Y, QIAO Y, et al. Cucumber disease detection method based on visible light spectrum and improved YOLOv5 in natural scenes[J]. Spectroscopy and spectral analysis, 2023, 43(8): 2596-2600.
103	符首夫. 基于卷积神经网络的农作物昆虫识别与计数[D]. 镇江: 江苏科技大学, 2020.
	FU S F. Crop insect recognition and counting based on convolutional neural network[D]. Zhenjiang: Jiangsu University of Science and Technology, 2020.
104	王卫民, 符首夫, 顾榕蓉, 等. 基于卷积神经网络的虫情图像分割和计数方法[J]. 计算机工程与科学, 2020, 42(1): 110-116.
	WANG W M, FU S F, GU R R, et al. An insect image segmentation and counting method based on convolutional neural network[J]. Computer engineering & science, 2020, 42(1): 110-116.
105	陈俭. 基于卷积神经网络和度量学习的害虫检测方法研究[D]. 杭州: 浙江大学, 2021.
	CHEN J. Research on pest detection method based on convolutional neural network and metric learning[D]. Hangzhou: Zhejiang University, 2021.
106	ZU L L, ZHAO Y P, LIU J Q, et al. Detection and segmentation of mature green tomatoes based on mask R-CNN with automatic image acquisition approach[J]. Sensors, 2021, 21(23): 7842.
107	CHEN W B, LIU M C, ZHAO C J, et al. MTD-YOLO: Multi-task deep convolutional neural network for cherry tomato fruit bunch maturity detection[J]. Computers and electronics in agriculture, 2024, 216: ID 108533.
108	LI X X, MA N, HAN Y H, et al. AHPPEBot: Autonomous robot for tomato harvesting based on phenotyping and pose estimation[C]// 2024 IEEE International Conference on Robotics and Automation (ICRA). Piscataway, New Jersey, USA: IEEE, 2024: 18150-18156.
109	杨信廷, 刘彤, 韩佳伟, 等. 基于Swin Transformer与GRU的低温贮藏番茄成熟度识别与时序预测研究[J]. 农业机械学报, 2024, 55(3): 213-220.
	YANG X T, LIU T, HAN J W, et al. Low temperature storage tomato maturity recognition and time series prediction based on Swin Transformer-GRU[J]. Transactions of the Chinese society for agricultural machinery, 2024, 55(3): 213-220.
110	SHEN T, ZHANG F, CHENG J W. A comprehensive overview of knowledge graph completion[J]. Knowledge-based systems, 2022, 255: ID 109597.
111	LU Y C, LU X Y, ZHENG L P, et al. Application of multimodal transformer model in intelligent agricultural disease detection and question-answering systems[J]. Plants, 2024, 13(7): ID 972.
112	KIRILLOV A, MINTUN E, RAVI N, et al. Segment any‐thing[EB/OL]. arXiv: 2304.02643, 2023.
113	林森, 郭文忠, 郑建锋, 等. 基于知识图谱和机器视觉的智慧草莓生产托管服务系统实践[J]. 农业工程技术, 2021, 41(4): 17-20.
	LIN S, GUO W Z, ZHENG J F, et al. Practice of smart strawberry production hosting service system based on knowledge graph and machine vision[J]. Agricultural engineering technology, 2021, 41(4): 17-20.
114	张宇, 于合龙, 郭文忠, 等. 基于知识图谱的番茄种植管理可视化查询[J]. 农机化研究, 2024, 46(3): 8-13.
	ZHANG Y, YU H L, GUO W Z, et al. Visual query of tomato irrigation based on knowledge graph[J]. Journal of agricultural mechanization research, 2024, 46(3): 8-13.
115	FENG X G, ZHAO C J, WANG C S, et al. A vegetable leaf disease identification model based on image-text cross-modal feature fusion[J]. Frontiers in plant science, 2022, 13: ID 918940.
116	王彤, 张立杰, 王铭, 等. 融合RoBERTa-WWM和全局指针网络的农业病害实体关系联合抽取研究[J]. 河北农业大学学报, 2024, 47(3): 113-120, 129.
	WANG T, ZHANG L J, WANG M, et al. Joint extraction of agricultural disease entity and relations by combining RoBERTA-WWM and global pointer[J]. Journal of Hebei agricultural university, 2024, 47(3): 113-120, 129.
117	王春山. 基于多模态数据与知识融合的开放环境蔬菜病害识别方法研究[D]. 保定: 河北农业大学, 2023.
	WANG C S. Research on recognition method of vegetable diseases in open environment based on multimodal data and knowledge fusion[D]. Baoding: Hebei Agricultural University, 2023.
118	饶海笛.基于语义的作物病虫害多模态知识问答方法研究[D]. 合肥: 安徽农业大学, 2023.
	RAO H D. Semantic-based multimodal knowledge question and answer method for crop pests and diseases[D]. Hefei: Anhui Agricultural University, 2023.
119	赵春江. 农业知识智能服务技术综述[J]. 智慧农业(中英文), 2023, 5(2): 126-148.
	ZHAO C J. Agricultural knowledge intelligent service technology: A review[J]. Smart agriculture, 2023, 5(2): 126-148.
120	LYU B W, WU H R, CHEN W B, et al. VEG-MMKG: Multimodal knowledge graph construction for vegetables based on pre-trained model extraction[J]. Computers and electronics in agriculture, 2024(226): ID 109398.

[1]	CAO Bingxue, LI Hongfei, ZHAO Chunjiang, LI Jin. The Path of Smart Agricultural Technology Innovation Leading Development of Agricultural New Quality Productivity [J]. Smart Agriculture, 2024, 6(4): 116-127.
[2]	ZHAO Chunjiang. Agricultural Knowledge Intelligent Service Technology: A Review [J]. Smart Agriculture, 2023, 5(2): 126-148.