Agricultural Sensor: Research Progress, Challenges and Perspectives

WANG Rujing

Smart Agriculture ›› 2024, Vol. 6 ›› Issue (1) : 1-17.

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Smart Agriculture

Abbreviation (ISO4): Smart Agriculture      Editor in chief: Chunjiang ZHAO

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Smart Agriculture ›› 2024, Vol. 6 ›› Issue (1) : 1-17. DOI: 10.12133/j.smartag.SA202401017
Topic--Intelligent Agricultural Sensor Technology

Agricultural Sensor: Research Progress, Challenges and Perspectives

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Abstract

Significance Agricultural sensor is the key technology for developing modern agriculture. Agricultural sensor is a kind of detection device that can sense and convert physical signal, which is related to the agricultural environment, plants and animals, into an electrical signal. Agricultural sensors could be applied to monitor crops and livestock in different agricultural environments, including weather, water, atmosphere and soil. It is also an important driving force to promote the iterative upgrading of agricultural technology and change agricultural production methods. Progress The different agricultural sensors are categorized, the cutting-edge research trends of agricultural sensors are analyzed, and summarizes the current research status of agricultural sensors are summarized in different application scenarios. Moreover, a deep analysis and discussion of four major categories is conducted, which include agricultural environment sensors, animal and plant life information sensors, agricultural product quality and safety sensors, and agricultural machinery sensors. The process of research, development, the universality and limitations of the application of the four types of agricultural sensors are summarized. Agricultural environment sensors are mainly used for real-time monitoring of key parameters in agricultural production environments, such as the quality of water, gas, and soil. The soil sensors provide data support for precision irrigation, rational fertilization, and soil management by monitoring indicators such as soil humidity, pH, temperature, nutrients, microorganisms, pests and diseases, heavy metals and agricultural pollution, etc. Monitoring of dissolved oxygen, pH, nitrate content, and organophosphorus pesticides in irrigation and aquaculture water through water sensors ensures the rational use of water resources and water quality safety. The gas sensor monitors the atmospheric CO2, NH3, C2H2, CH4 concentration, and other information, which provides the appropriate environmental conditions for the growth of crops in greenhouses. The animal life information sensor can obtain the animal's growth, movement, physiological and biochemical status, which include movement trajectory, food intake, heart rate, body temperature, blood pressure, blood glucose, etc. The plant life information sensors monitor the plant's health and growth, such as volatile organic compounds of the leaves, surface temperature and humidity, phytohormones, and other parameters. Especially, the flexible wearable plant sensors provide a new way to measure plant physiological characteristics accurately and monitor the water status and physiological activities of plants non-destructively and continuously. These sensors are mainly used to detect various indicators in agricultural products, such as temperature and humidity, freshness, nutrients, and potentially hazardous substances (e.g., bacteria, pesticide residues, heavy metals, etc. Agricultural machinery sensors can achieve real-time monitoring and controlling of agricultural machinery to achieve real-time cultivation, planting, management, and harvesting, automated operation of agricultural machinery, and accurate application of pesticide, fertilizer. [Conclusions and Prospects In the challenges and prospects of agricultural sensors, the core bottlenecks of large-scale application of agricultural sensors at the present stage are analyzed in detail. These include low-cost, specialization, high stability, and adaptive intelligence of agricultural sensors. Furthermore, the concept of "ubiquitous sensing in agriculture" is proposed, which provides ideas and references for the research and development of agricultural sensor technology.

Key words

agricultural sensors / ubiquitous sensing / environmental sensors / soil nutrient sensors / phenotypic sensors / smart agriculture

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WANG Rujing. Agricultural Sensor: Research Progress, Challenges and Perspectives[J]. Smart Agriculture. 2024, 6(1): 1-17 https://doi.org/10.12133/j.smartag.SA202401017

References

1
金欢庆, 热孜燕·瓦卡斯. 中国智慧农业发展现状及对策[J]. 农业展望, 2023, 19(11): 62-66.
JIN H Q, REZIYAN WAKASI. Development status and countermeasures of intelligent agriculture in China[J]. Agricultural outlook, 2023, 19(11): 62-66.
2
刘羽飞, 何勇, 刘飞, 等. 农业传感器技术在我国的应用和市场:现状与未来展望[J]. 浙江大学学报(农业与生命科学版), 2023, 49(3): 293-304.
LIU Y F, HE Y, LIU F, et al. Application and market of agricultural sensor technology in China: Current status and future perspectives[J]. Journal of Zhejiang university (agriculture and life sciences), 2023, 49(3): 293-304.
3
MAHMOUDPOUR M, TORBATI M, MOUSAVI M M, et al. Nanomaterial-based molecularly imprinted polymers for pesticides detection: Recent trends and future prospects[J]. Trends in Analytical Chemistry, 2020, 129: ID 115943.
4
MA P, ZHU H R, LU H, et al. Design of biodegradable wheat-straw based triboelectric nanogenerator as self-powered sensor for wind detection[J]. Nano energy, 2021, 86: ID 106032.
5
DE LARA A, LONGCHAMPS L, KHOSLA R. Soil water content and high-resolution imagery for precision irrigation: Maize yield[J]. Agronomy, 2019, 9(4): 174.
6
VISCARRA ROSSEL R A, BOUMA J. Soil sensing: A new paradigm for agriculture[J]. Agricultural systems, 2016, 148: 71-74.
7
YIN H, CAO Y, MARELLI B, et al. Soil sensors and plant wearables for smart and precision agriculture[J]. Advanced materials, 2021, 33(20): ID e2007764.
8
YU L M, GAO W L, SHAMSHIRI R R, et al. Review of research progress on soil moisture sensor technology[J]. International journal of agricultural and biological engineering, 2021, 14(3): 32-42.
9
XIAO D Q, FENG J Z, WANG N, et al. Integrated soil moisture and water depth sensor for paddy fields[J]. Computers and electronics in agriculture, 2013, 98: 214-221.
10
HABIBULLAH B, ALEXANDER S, DIDIER B, et al. Soil moisture and density monitoring methodology using TDR measurements[J]. International journal of pavement engineering, 2020, 21(10): 1263-1274.
11
VELLIDIS G, TUCKER M, PERRY C, et al. A real-time wireless smart sensor array for scheduling irrigation[J]. Computers and electronics in agriculture, 2008, 61(1): 44-50.
12
XU Y, DUAN J L, JIANG R, et al. Study on the detection of soil water content based on the pulsed acoustic wave (PAW) method[J]. IEEE access, 2021, 9: 15731-15743.
13
ZHENG X M, FENG Z Z, LI L, et al. Simultaneously estimating surface soil moisture and roughness of bare soils by combining optical and radar data[J]. International journal of applied earth observation and geoinformation, 2021, 100: ID 102345.
14
宋豫晓, 王建, 乔晓军, 等. 多功能土壤温度测量仪的研发[J]. 农机化研究, 2010, 32(9): 80-84.
SONG Y X, WANG J, QIAO X J, et al. Development of muti-functional soil temperature measuring instrument[J]. Journal of agricultural mechanization research, 2010, 32(9): 80-84.
15
JACKSON T, MANSFIELD K, SAAFI M, et al. Measuring soil temperature and moisture using wireless MEMS sensors[J]. Measurement, 2008, 41(4): 381-390.
16
MERL T, RASMUSSEN M R, KOCH L R, et al. Measuring soil pH at in situ like conditions using optical pH sensors (pH-optodes)[J]. Soil biology and biochemistry, 2022, 175: ID 108862.
17
NAIR N, AKSHAYA A V, JOSEPH J. An in-situ soil pH sensor with solid electrodes[J]. IEEE sensors letters, 2022, 6(8): 1-4.
18
ELDEEB M A, DHAMU V N, PAUL A, et al. Espial: Electrochemical soil pH sensor for in situ real-time monitoring[J]. Micromachines (basel), 2023, 14(12): ID 2188.
19
HAMMARLING K, ENGHOLM M, ANDERSSON H, et al. Broad-range hydrogel-based pH sensor with capacitive readout manufactured on a flexible substrate[J]. Chemosensors, 2018, 6(3): ID 30.
20
ADESANWO O O, IGE D V, THIBAULT L, et al. Comparison of colorimetric and ICP methods of phosphorus determination in soil extracts[J]. Communications in soil science and plant analysis, 2013, 44(21): 3061-3075.
21
MOONRUNGSEE N, PENCHAREE S, JAKMUNEE J. Colorimetric analyzer based on mobile phone camera for determination of available phosphorus in soil[J]. Talanta, 2015, 136: 204-209.
22
AGARWAL S, BHANGALE N, DHANURE K, et al. Application of colorimetry to determine soil fertility through naive Bayes classification algorithm[C]// 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). Piscataway, New Jersey, USA: IEEE, 2018: 1-6.
23
QIAO Y, ZHANG S. Near-infrared spectroscopy technology for soil nutrients detection based on LS-SVM[C]// Computer and Computing Technologies in Agriculture V, Berlin, German: Springer, 2012: 325-335.
24
PENG Y P, ZHAO L, HU Y M, et al. Prediction of soil nutrient contents using visible and near-infrared reflectance spectroscopy[J]. ISPRS international journal of geo-information, 2019, 8(10): ID 437.
25
HE Y, LIU X, LYU Y, et al. Quantitative analysis of nutrient elements in soil using single and double-pulse laser-induced breakdown spectroscopy[J]. Sensors (basel), 2018, 18(5): ID E1526.
26
GARLAND N T, MCLAMORE E S, CAVALLARO N D, et al. Flexible laser-induced graphene for nitrogen sensing in soil[J]. ACS applied materials & interfaces, 2018, 10(45): 39124-39133.
27
TANG C L, FU D C, WANG R J, et al. An electrochemical microfluidic system for on-site continuous monitoring of soil phosphate[J]. IEEE sensors journal, 2024, 24(5): 6754-6764.
28
KIM H J, HUMMEL J W, SUDDUTH K A, et al. Simultaneous analysis of soil macronutrients using ion-selective electrodes[J]. Soil science society of America journal, 2007, 71(6): 1867-1877.
29
ACHARYA G, DOORNEWEERD D D, CHANG C L, et al. Label-free optical detection of anthrax-causing spores[J]. Journal of the American chemical society, 2007, 129(4): 732-733.
30
YAGHOUBI M, RAHIMI F, NEGAHDARI B, et al. A lectin-coupled porous silicon-based biosensor: Label-free optical detection of bacteria in a real-time mode[J]. Scientific reports, 2020, 10: ID 16017.
31
JIN K S, FALLGREN P H, SANTIAGO N A, et al. Monitoring in situ microbial activities in wet or clayey soils by a novel microbial-electrochemical technology[J]. Environmental technology & innovation, 2020, 18: ID 100695.
32
POTAMITIS I, RIGAKIS I, VIDAKIS N, et al. Affordable bimodal optical sensors to spread the use of automated insect monitoring[J]. Journal of sensors, 2018, 2018: ID 3949415.
33
MANKIN R W, BRANDHORST-HUBBARD J, FLANDERS K L, et al. Eavesdropping on insects hidden in soil and interior structures of plants[J]. Journal of economic entomology, 2000, 93(4): 1173-1182.
34
RUSTIA D J A, LIN C E, CHUNG J Y, et al. Application of an image and environmental sensor network for automated greenhouse insect pest monitoring[J]. Journal of asia-pacific entomology, 2020, 23(1): 17-28.
35
DESAULES A, AMMANN S, SCHWAB P. Advances in long-term soil-pollution monitoring of Switzerland[J]. Journal of plant nutrition and soil science, 2010, 173(4): 525-535.
36
RATTANARAT P, DUNGCHAI W, CATE D, et al. Multilayer paper-based device for colorimetric and electrochemical quantification of metals[J]. Analytical chemistry, 2014, 86(7): 3555-3562.
37
CHEN Y-T, C-YHSEIH, SARANGADHARAN I, et al. Beyond the limit of ideal nernst sensitivity: Ultra-high sensitivity of heavy metal ion detection with ion-selective high electron mobility transistors[J]. ECS Journal of solid state science and technology, 2018, 7(9): Q176-Q183.
38
DAS T R, SHARMA P K. Sensitive and selective electrochemical detection of Cd2 + by using bimetal oxide decorated Graphene oxide (Bi 2 O3/Fe 2 O3 @GO) electrode[J]. Microchemical journal, 2019, 147: 1203-1214.
39
PRASAD B B, JAUHARI D, TIWARI M P. Doubly imprinted polymer nanofilm-modified electrochemical sensor for ultra-trace simultaneous analysis of glyphosate and glufosinate[J]. Biosensors & bioelectronics, 2014, 59: 81-88.
40
VIGNESH KUMAR T H, RAMAN PILLAI S K, CHAN-PARK M B, et al. Highly selective detection of an organophosphorus pesticide, methyl parathion, using Ag–ZnO–SWCNT based field-effect transistors[J]. Journal of materials chemistry C, 2020, 8(26): 8864-8875.
41
张俊卿, 陈翔宇, 王儒敬, 等. 用于水肥系统的养分离子快检装置研制与试验[J]. 农业工程学报, 2022, 38(2): 102-110.
ZHANG J Q, CHEN X Y, WANG R J, et al. Development and experiment of the rapid detection device of the nutrient ion concentrations for fertigation system[J]. Transactions of the Chinese society of agricultural engineering, 2022, 38(2): 102-110.
42
ALAHI MD ESHRAT E, LI X, SUBHAS M, et al. A temperature compensated smart nitrate-sensor for agricultural industry[J]. IEEE transactions on industrial electronics, 2017, 64(9): 7333-7341.
43
ZHANG Y, QI Y, WANG L, et al. Sensing technologies for detection of non-point source pollutants in rice paddy fields[J]. International journal of precision agricultural aviation, 2020, 1(1): 1-13.
44
BAHAMON-PINZON D, MOREIRA G, OBARE S, et al. Development of a nanocopper-decorated laser-scribed sensor for organophosphorus pesticide monitoring in aqueous samples[J]. Microchimica Acta, 2022, 189(7): ID 254.
45
JANG A, ZOU Z W, LEE K K, et al. State-of-the-art lab chip sensors for environmental water monitoring[J]. Measurement science and technology, 2011, 22(3): ID 032001.
46
LIN J Y, TSAI H L, LYU W H. An integrated wireless multi-sensor system for monitoring the water quality of aquaculture [J]. Sensors (basel), 2021, 21(24): ID 8179.
47
顾浩, 王志强, 吴昊, 等. 基于荧光法的溶解氧传感器研制及试验[J]. 智慧农业(中英文), 2020, 2(2): 48-58.
GU H, WANG Z Q, WU H, et al. A fluorescence based dissolved oxygen sensor[J]. Smart agriculture, 2020, 2(2): 48-58.
48
马淑英, 马玉泉, 张丽红, 等. 农业设施中二氧化碳测控仪的研制[J]. 农机化研究, 2007, 29(12): 104-105, 115.
MA S Y, MA Y Q, ZHANG L H, et al. The designing of carbon dioxide density detection instrument used in agriculture[J]. Journal of agricultural mechanization research, 2007, 29(12): 104-105, 115.
49
张尉, 高星星, 方贤才, 等. 适用于农业环境的便携式激光CO2传感器设计[J]. 中国农机化学报, 2017, 38(3): 73-76, 81.
ZHANG W, GAO X X, FANG X C, et al. Design of portable laser CO2 sensor for agricultural environment[J]. Journal of Chinese agricultural mechanization, 2017, 38(3): 73-76, 81.
50
LI X, XU J, JIANG Y, et al. Toward agricultural ammonia volatilization monitoring: A flexible polyaniline/Ti3C2T hybrid sensitive films based gas sensor[J]. Sensors and actuators B: Chemical, 2020, 316: ID 128144.
51
DAS K, JANA B, PRAMANIK M, et al. Chemically synthesized ZnO nanocrystal-based ethylene sensor operative at natural humid condition[J]. Applied physics A, 2022, 128(11): ID 962.
52
YANG X, LEONG J L K, SUN M T, et al. Quantitative determination of ethylene using a smartphone-based optical fiber sensor (SOFS) coupled with pyrene-tagged Grubbs catalyst[J]. Biosensors, 2022, 12(5): ID 316.
53
陈友安, 张建, 高翔, 等. 水稻田甲烷在线监测系统设计[J]. 仪表技术, 2016(9): 7-11.
CHEN Y A, ZHANG J, GAO X, et al. Design of online monitoring system for methane in the paddy field[J]. Instrumentation technology, 2016(9): 7-11.
54
KOMARUDIN M, SEPTAMA H D, YULIANTI T. Autonomous cyber physical systems for monitoring of methane gas in rice field[C]// 2nd International Conference on Smart and Innovative Agriculture (ICoSIA 2021), Paris, France: Atlantis Press, 2022: 138-143.
55
PENG Y, ZHOU J, SONG X, et al. A flexible pressure sensor with ink printed porous graphene for continuous cardiovascular status monitoring[J]. Sensors (basel), 2021, 21(2): ID E485.
56
GONZÁLEZ-SÁNCHEZ C, FRAILE J C, PÉREZ-TURIEL J, et al. Capacitive sensing for non-invasive breathing and heart monitoring in non-restrained, non-sedated laboratory mice[J]. Sensors (basel), 2016, 16(7): ID E1052.
57
ION M, DINULESCU S, FIRTAT B, et al. Design and fabrication of a new wearable pressure sensor for blood pressure monitoring[J]. Sensors (basel), 2021, 21(6): ID 2075.
58
NEETHIRAJAN S, KEMP B. Digital phenotyping in livestock farming[J]. Animals (basel), 2021, 11(7): ID 2009.
59
杨亮, 王辉, 陈睿鹏, 等. 猪专用传感器研究进展[J]. 智能化农业装备学报(中英文), 2023, 4(2): 22-34.
YANG L, WANG H, CHEN R P, et al. Advances in research on pig-specific sensors[J]. Journal of intelligent agricultural mechanization, 2023, 4(2): 22-34.
60
LI J, LIAO Z Q, LIANG T, et al. High sensitivity, fast response and anti-interference crack-based reduced graphene oxide strain sensor for pig acoustic recognition [J]. Computers and electronics in agriculture, 2022, 200: ID 107267.
61
YIN Y, TU D, SHEN W, et al. Recognition of sick pig cough sounds based on convolutional neural network in field situations[J]. Information processing in agriculture, 2021, 8(3): 369-379.
62
ZHAO J, LI X, LIU W, et al. DNN-HMM based acoustic model for continuous pig cough sound recognition[J]. International journal of agricultural and biological engineering, 2020, 13(3): 186-193.
63
GOUGH D A, KUMOSA L S, ROUTH T L, et al. Function of an implanted tissue glucose sensor for more than 1 year in animals[J]. Science translational medicine, 2010, 2(42): ID 42ra53.
64
CHAI Y, CHEN C, LUO X, et al. Cohabiting plant-wearable sensor in situ monitors water transport in plant[J]. Advanced science (weinh), 2021, 8(10): ID 2003642.
65
QU C C, CAO L X, LI M L, et al. Liquid metal-based plant electronic tattoos for in situ monitoring of plant physiology[J]. Science China technological sciences, 2023, 66(6): 1617-1628.
66
YIN S, IBRAHIM H, SCHNABLE P S, et al. A field‐ deployable, wearable leaf sensor for continuous monitoring of vapor‐pressure deficit [J]. Advanced materials technologies, 2021, 6(6): ID 202001246.
67
LEE G, HOSSAIN O, JAMALZADEGAN S, et al. Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring[J]. Science advances, 2023, 9(15): ID eade2232.
68
TANG W, YAN T, WANG F, et al. Rapid fabrication of wearable carbon nanotube/graphite strain sensor for real-time monitoring of plant growth [J]. Carbon, 2019, 147: 295-302.
69
OREN S, CEYLAN H, SCHNABLE P S, et al. High‐resolution patterning and transferring of graphene‐based nanomaterials onto tape toward roll‐to‐roll production of tape‐based wearable sensors [J]. Advanced materials technologies, 2017, 2(12): ID 1700223.
70
WANG S, LI W, CHANG K, et al. Localized surface plasmon resonance-based abscisic acid biosensor using aptamer-functionalized gold nanoparticles[J]. PLoS One, 2017, 12(9): ID e0185530.
71
WEI C, ZHOU H, CHEN C, et al. On-line monitoring 1h-indole-3-acetic acid in plant tissues using molecular imprinting monolayer techniques on a surface plasmon resonance sensor[J]. Analytical letters, 2011, 44(18): 2911-2921.
72
陈玥瑶, 夏静静, 韦芸, 等. 近红外光谱法无损检测平谷产大桃品质方法研究[J]. 分析化学, 2023, 51(3): 454-462.
CHEN Y Y, XIA J J, WEI Y, et al. Research on nondestructive quality test of Pinggu peach by near-infrared spectroscopy[J]. Chinese journal of analytical chemistry, 2023, 51(3): 454-462.
73
SINGH R, ZHANG W, LIU X C, et al. WaveFlex biosensor: MXene-immobilized w-shaped fiber-based LSPR sensor for highly selective tyramine detection[J]. Optics laser technology, 2024, 171: ID 110357.
74
MISHRA R K, HUBBLE L J, MARTÍN A, et al. Wearable flexible and stretchable glove biosensor for on-site detection of organophosphorus chemical threats[J]. ACS sensors, 2017, 2(4): 553-561.
75
ZHAO F, HE J, LI X, et al. Smart plant-wearable biosensor for in situ pesticide analysis[J]. Biosens bioelectron, 2020, 170: ID 112636.
76
ZHANG X N, HUANG X Y, XU Y W, et al. Single-step electrochemical sensing of ppt-level lead in leaf vegetables based on peroxidase-mimicking metal-organic framework[J]. Biosensors and bioelectronics, 2020, 168: ID 112544.
77
TÜMAY S O, ŞANKO V, DEMIRBAS E, et al. Fluorescence determination of trace level of cadmium with pyrene modified nanocrystalline cellulose in food and soil samples[J]. Food and chemical toxicology, 2020, 146: ID 111847.
78
GAI P, GU C, HOU T, et al. Ultrasensitive self-powered aptasensor based on enzyme biofuel cell and DNA bioconjugate: A facile and powerful tool for antibiotic residue detection[J]. Analytical chemistry, 2017, 89(3): 2163-2169.
79
PAN M F, GU Y, ZHANG M Y, et al. Reproducible molecularly imprinted QCM sensor for accurate, stable, and sensitive detection of enrofloxacin residue in animal-derived foods[J]. Food analytical methods, 2018, 11(2): 495-503.
80
TROFIMCHUK E, NILGHAZ A, SUN S, et al. Determination of norfloxacin residues in foods by exploiting the coffee-ring effect and paper-based microfluidics device coupling with smartphone-based detection[J]. journal of food science, 2020, 85(3): 736-743.
81
ALSAMMARRAIE F K, LIN M S. Using standing gold nanorod arrays as surface-enhanced Raman spectroscopy (SERS) substrates for detection of carbaryl residues in fruit juice and milk[J]. Journal of agricultural and food chemistry, 2017, 65(3): 666-674.
82
CHENG J, ZHANG S, WANG S, et al. Rapid and sensitive detection of acrylamide in fried food using dispersive solid-phase extraction combined with surface-enhanced Raman spectroscopy[J]. Food chemistry, 2019, 276: 157-163.
83
XUE F, WANG X, WANG J Q, et al. Deep visual odometry with adaptive memory[J]. IEEE transactions on pattern analysis and machine intelligence, 2022, 44(2): 940-954.
84
YIN X, NOGUCHI N, ISHI K. Development of an obstacle avoidance system for a field robot using a 3D camera[J]. Engineering in agriculture, environment and food, 2013, 6(2): 41-47.
85
肖跃进, 梁春英, 李新宇, 等. 基于云平台的农业作业机械工况监测系统的研究[J]. 黑龙江八一农垦大学学报, 2017, 29(2): 102-107.
XIAO Y J, LIANG C Y, LI X Y, et al. Research on operating condition monitoring system of agricultural machine based on cloud platform[J]. Journal of Heilongjiang bayi agricultural university, 2017, 29(2): 102-107.
86
金鑫, 李倩文, 苑严伟, 等. 2BFJ-24型小麦精量播种变量施肥机设计与试验[J]. 农业机械学报, 2018, 49(5): 84-92.
JIN X, LI Q W, YUAN Y W, et al. Design and test of 2BFJ-24 type variable fertilizer and wheat precision seed sowing machine[J]. Transactions of the Chinese society for agricultural machinery, 2018, 49(5): 84-92.
87
尹文庆, 浦浩, 胡飞, 等. 基于结构光视觉的联合收获机谷粒体积流量测量方法[J]. 农业机械学报, 2020, 51(9): 101-107.
YIN W Q, PU H, HU F, et al. Measurement method of grain volume flow based on structured light[J]. Transactions of the Chinese society for agricultural machinery, 2020, 51(9): 101-107.
88
耿端阳, 谭德蕾, 苏国粱, 等. 压力式谷物产量监测系统优化与试验验证[J]. 农业工程学报, 2021, 37(9): 245-252.
GENG D Y, TAN D L, SU G L, et al. Optimization and experimental verification of grain yield monitoring system based on pressure sensors[J]. Transactions of the Chinese society of agricultural engineering, 2021, 37(9): 245-252.
89
钱震杰, 金诚谦, 刘政, 等. 无人农场中的智能控制技术应用现状与趋势(英文)[J]. 智能化农业装备学报(中英文), 2023, 4: 1-13.
QIAN Z J, JIN C Q, LIU Z, et al. Development status and trends of intelligent control technology in unmanned farms[J]. Journal of intelligent agricultural mechanization, 2023, 4: 1-13.

Funding

National Key Research and Development Program of China(2023YFD1701800)
National Natural Science Foundation of China(12304236)
Anhui Province Science and Technology Major Project(2020b06050001)
Anhui Provincial Natural Science Foundation(2308085QA19)
Science and Technology Mission Program of Anhui Province(S2022t06010123)
The Dean Foundation of Hefei Institutes of Physical Science, Chinese Academy of Sciences(YZJJ2024QN38)

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