{"id":2231,"date":"2025-03-21T07:58:35","date_gmt":"2025-03-21T07:58:35","guid":{"rendered":"http:\/\/www.xingshitu.com\/?p=2231"},"modified":"2025-03-21T07:58:35","modified_gmt":"2025-03-21T07:58:35","slug":"%e5%86%9c%e7%94%b0%e5%ae%9e%e6%97%b6%e7%9b%91%e6%b5%8b%e7%b3%bb%e7%bb%9f%e6%a6%82%e5%bf%b5%e4%b8%8e%e5%ba%94%e7%94%a8","status":"publish","type":"post","link":"http:\/\/www.xingshitu.com\/en\/2025\/03\/21\/%e5%86%9c%e7%94%b0%e5%ae%9e%e6%97%b6%e7%9b%91%e6%b5%8b%e7%b3%bb%e7%bb%9f%e6%a6%82%e5%bf%b5%e4%b8%8e%e5%ba%94%e7%94%a8\/","title":{"rendered":"Concepts and applications of real-time farmland monitoring systems"},"content":{"rendered":"

summaries<\/em><\/strong><\/em><\/strong><\/em><\/h3>\n\n\n\n

The real-time farmland monitoring system integrates multi-source sensing and Internet of Things (IoT) technologies to realize dynamic collection and precise management of crop growth and environmental data. This paper is based onstar view<\/strong>s.\"Multi-spectral camera + LiDAR + scientific observation in one shot<\/strong>\"As an example.It describes its technical architecture, functions and application value, verifies its scientificity with cases, and provides references for agricultural precision management and sustainable development.<\/strong><\/p>\n\n\n\n

I. Concepts and needs for a real-time monitoring system for agricultural land<\/strong><\/p>\n\n\n

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Real-time farmland monitoring system<\/strong>(Farmland Real-time Monitoring System, FRMS) is based on modern sensing technology, real-time access to multi-dimensional data such as crop growth, soil environment, meteorology, etc., and combines the data analysis model to realize accurate judgment of agricultural conditions. Its core objectives:<\/p>\n\n\n\n

1. <\/strong>dynamic perception<\/strong>: Breaking through the limitations of manual observation and realizing the continuous collection of farmland data;<\/p>\n\n\n\n

2. <\/strong>accurate diagnosis<\/strong>: Multi-parameter fusion analysis to identify crop stresses (e.g., drought, pests and diseases);<\/p>\n\n\n\n

3. <\/strong>scientific decision-making<\/strong>: To support water and fertilizer management, disaster early warning, yield forecasting, and optimizing resource use.<\/p>\n\n\n\n

Traditional agricultural monitoring techniquesrestrict<\/strong>In a single parameter, low resolution and artificial dependence, it is difficult to meet the needs of modern agriculture. Integrated multi-spectral imaging, LiDAR and IoT technology comprehensive monitoring system, become the industry to upgrade thecrux<\/strong>Path.<\/p>\n\n\n\n

II. Technical system and core components<\/strong><\/p>\n\n\n\n

2.1<\/em>\u00a0multispectral imaging technology<\/em><\/strong><\/p>\n\n\n

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Developed by StarViewXST-PhotoNet Series<\/a>multispectral camera<\/strong>Through synergistic multi-band observations.Breakthrough<\/strong>Spectral limitations of RGB cameras<\/strong>(math.) genusSupporting the analysis of vegetation indices (NDVI, GCC, etc.) and phenological period dynamics<\/strong>::<\/p>\n\n\n\n

-<\/strong> dual mode imaging<\/strong>: Standard RGB images (Mode-1) were combined with narrow-band spectral images (Mode-2) to capture chlorophyll absorption features in the 650\u00b110 nm red band, and to resolve canopy structure in the 850\u00b110 nm near-infrared band;<\/p>\n\n\n\n

- <\/strong>high spatial and temporal resolution<\/strong>: Hourly continuous observation (5:00-20:00), combined with a 20x optical zoom head, supports group-individual-organ multi-scale monitoring;<\/p>\n\n\n\n

-\u00a0<\/strong>Vegetation index optimization<\/strong>: Calculate 25 indices (e.g., EXG, NDVI) based on 5-band narrowband data, with an accuracy improvement of more than 301 TP3T over broadband data.<\/p>\n\n\n\n

application scenario<\/strong>::Crop development period tracking (e.g.<\/a>Winter wheat in Hebei and Henan<\/a>(Climate monitoring)<\/a>,Stress response analysis (e.g., alfalfa harvesting and snowfall events at Qingyang station)<\/a>(NDVI anomaly detection)<\/a>The<\/p>\n\n\n

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Growing season variation curve of alfalfa NDVI at Qingyang station<\/p>\n\n\n

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Fluctuations in NDVI due to alfalfa harvest: first crop started on June 9, second crop in late July, third crop in late October<\/p>\n\n\n\n

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Snowfall induced NDVI fluctuations: snow began to fall on December 11 and largely melted by the 21st; snow fell again on January 15 of the following year<\/p>\n\n\n\n

2.2 In-situ LIDAR technology<\/em><\/strong><\/p>\n\n\n

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XST-LiDARNet in situ eco-lidar<\/a><\/strong>pass (a bill or inspection etc)Active 3D point cloud scanning<\/strong>(math.) genusBreaking the bottleneck of canopy penetration in passive optical sensors<\/strong>::<\/p>\n\n\n\n

- <\/strong>Point cloud processing algorithms<\/strong>: Achieve vegetation-ground separation based on a non-growing season ground reference library to extract parameters such as canopy height, percent cover (FVC), and leaf area body density (LAI);<\/p>\n\n\n\n

- <\/strong>Dynamic monitoring capacity<\/strong>: One scan per day (452,000 points\/second), supporting analysis of daily changes in vegetation volume (case of monitoring changes in pasture volume in Inner Mongolia grasslands);<\/p>\n\n\n\n

-\u00a0<\/strong>anti-interference design<\/strong>: IP66 protection grade, adapting to -40\u2103~85\u2103 extreme environment, suitable for farmland, forest, grassland and other multi-scenarios;<\/p>\n\n\n\n

- Technical Advantages<\/strong>: LAI inversion error <101 TP3T, and coverage calculations agree with maize growth rhythms up to 951 TP3T.<\/p>\n\n\n\n

application scenario<\/strong>: LIDAR applied to vegetation location monitoring can acquire three-dimensional structure information of vegetation for a long period of time, and get the time series variation of height, volume and other parameters.<\/p>\n\n\n

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Comparative consistency of combined LiDAR (point cloud) and weather camera (live view) systems<\/p>\n\n\n\n

2.3 Multi-source synergistic monitoring platform:<\/em><\/strong>XST-SCI-in-One<\/em><\/strong><\/a>one-stop shopping for scientific research and observation<\/em><\/strong><\/p>\n\n\n

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surrounding environmentIntegration of spectral imaging, lidar, weather and soil sensing modules<\/strong>(math.) genusrealization<\/strong>\"Environment-crop\" synergistic monitoring<\/strong>::<\/p>\n\n\n\n

-<\/strong> Four-dimensional data fusion<\/strong>::<\/strong>Multimodal data fusion to greatly improve the robustness and accuracy of model data;<\/p>\n\n\n\n

- <\/strong>Plant Ontology Parameters<\/strong>: Vegetation indices such as NDVI, LAI, and FVC;<\/p>\n\n\n\n

- <\/strong>meteorological element<\/strong>: Temperature and humidity, wind speed, rainfall, solar radiation (accuracy \u00b10.2\u00b0C\/\u00b12%);<\/p>\n\n\n\n

- <\/strong>Soil parameters<\/strong>: Temperature\/humidity\/salt multi-depth monitoring (resolution 0.1\u00b0C\/0.03%);<\/p>\n\n\n\n

- <\/strong>Hydrological data<\/strong>: Radar flow rate sensors (1 mm\/s accuracy) support irrigation optimization.<\/p>\n\n\n\n

- <\/strong>Edge Computing and Transport<\/strong>: Intelligent data collector (256M memory) processes data in real time and supports255<\/strong>individual sensor access with full 4G transmission;<\/p>\n\n\n\n

-\u00a0<\/strong>data security<\/strong>Local-Cloud dual backup, intermittent transfer and exception alarm (SMS\/email notification).<\/p>\n\n\n\n

application scenario<\/strong>: IoT cloud data with outdoor real-time playback data.<\/p>\n\n\n

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III. Application value and case studies<\/strong><\/p>\n\n\n\n

3.1 Precision agricultural management<\/em><\/strong><\/h5>\n\n\n\n

-<\/strong>Fertilizer optimization<\/strong>: Combine soil moisture with canopy LAI data to dynamically adjust irrigation (e.g., alfalfa case at Qingyang Station);<\/p>\n\n\n\n

-Pest and disease early warning<\/strong>: Detection of canopy temperature anomalies by thermal imaging module (XST-PhotoNet-TI) to detect stress earlier than the naked eye.<\/p>\n\n\n\n

3.2 Disaster response and insurance assessment<\/em><\/strong><\/h5>\n\n\n\n

-<\/strong>Meteorological disaster warning<\/strong>: Real-time wind speed\/rainfall monitoring combined with canopy structure data to assess the risk of collapse (e.g., Inner Mongolia grassland case);<\/p>\n\n\n\n

-<\/strong>Quantification of damage<\/strong>: Lidar point cloud reconstruction of 3D model of the affected area to accurately calculate the percentage of yield reduction.<\/p>\n\n\n\n

3.3 Remote Sensing Ground Verification and Carbon Sink Accounting<\/em><\/strong><\/h5>\n\n\n\n

-<\/strong>Synergy between heaven and earth<\/strong>: Joint calibration of multispectral data with satellite remote sensing (e.g., China Carbon Satellite) to improve the accuracy of regional-scale parameter inversion;<\/p>\n\n\n\n

-<\/strong>Carbon flux estimation:<\/strong>SIFNet chlorophyll fluorescence data (accuracy \u00b12%) underpin photosynthesis modeling and aid dual-carbon goals.<\/p>\n\n\n\n

IV. Challenges and future prospects<\/strong><\/p>\n\n\n

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1.<\/strong>technological bottleneck<\/strong>: The complexity of multi-source data fusion algorithms is high, and they need to be combined with AI models (e.g., deep learning) to improve analysis efficiency;<\/p>\n\n\n\n

2.<\/strong>cost control<\/strong>: Lidar and hyperspectral equipment are expensive and need to be designed modularly to reduce the cost of deployment at scale;<\/p>\n\n\n\n

3.<\/strong>standardized construction<\/strong>: Promote the standardization of monitoring parameters, data formats and interface protocols, and promote multi-platform interoperability.<\/p>\n\n\n\n

pending<\/strong>With the deep integration of low-altitude UAV remote sensing, digital twin technology and large agricultural models, the farmland monitoring system will evolve towards the integration of \"air-sky-earth\" and the closed-loop direction of prediction-decision-execution, providing a technological cornerstone for global food security and ecological sustainability. StarView will continue to contribute to this endeavor.<\/p>\n\n\n\n

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Beijing Star View Technology Co.<\/a><\/h1><\/div>\n\n\n\n

END<\/p>\n<\/div>\n<\/div><\/div>","protected":false},"excerpt":{"rendered":"

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