At the beginning of the "Road to Precision Agriculture" column, many students expressed their interest in the application of 3D remote sensing technology and multi-spectral technology in agriculture . Today, we are going to discuss this topic specifically, talk about the application of these technologies in plant growth monitoring, pest and disease monitoring, research and development status at home and abroad, and explore their possible applications in plant protection drones .
First, we have to be clear about what 3D remote sensing information is. The so-called 3D remote sensing information refers to the integrated remote sensing information that combines the three-dimensional position and remote sensing spectral data.
At present, the organic three-dimensional imager can obtain the three-dimensional position and remote sensing spectrum information of the ground target synchronously from the air, and realize the integrated acquisition of positioning and qualitative data.
The current high resolution 3D remote sensing images are mainly used for:
1. Terrain analysis , such as slope analysis, landform analysis, water system analysis, structural analysis, etc.;
2. Geological disaster investigation , by establishing a 3D simulation model of the disaster body, extracting the characteristic information of the disaster body and evaluating the danger of the disaster body;
3. The mineral resources survey of the mining area is to combine the high-precision digital elevation model (DEM, Digital Elevation Model, digital representation of the ground topography through limited terrain elevation data) with the high-resolution remote sensing image of the mining area to generate three-dimensional remote sensing images. Reflect complex terrain, geomorphology, and geological features;
4. Other aspects, such as urban landscape design, 3D simulation of military terrain, engineering line selection, etc.
Application of 3D Remote Sensing Technology in Pest and Disease Monitoring
For the remote sensing monitoring of pests and diseases, it is mainly based on the spectral response of crops affected by different stresses. The related research at home and abroad mainly focuses on four levels: 1) physiological mechanism of spectral response of pests and diseases; 2) spectral response characteristics of pests and diseases; 3) application Vegetation index for pest and disease monitoring; 4) Remote sensing identification and degree discrimination algorithm for pests and diseases.
Generally speaking, the physiological mechanism of spectral response caused by plant pests and diseases is basically clear; there are many studies on the spectral response characteristics of pests and diseases, mainly focusing on typical pests and diseases of typical crops such as wheat stripe rust, powdery mildew, rice brown planthopper, Rice blast, tomato late blight, etc., but the types of crops and pests studied are far from comprehensive; it has been studied to establish the relationship between remote sensing information and the occurrence and extent of pests and diseases through dozens of types of vegetation indices.
Regarding the identification and differentiation algorithm, it is mainly to establish the relationship between the disease and the spectral features. There are many model models, which can be roughly divided into two categories: one is based on hyperspectral non-imaging data, and the other is image-based data. Analytical method. These methods involve multivariate statistical analysis, data mining algorithms and image analysis methods, in order to hope that the model has high precision and specificity.
Application of Remote Sensing Technology for the Growth Monitoring of Different Growth Periods of Crops
Application of 3D Remote Sensing Technology in Plant Protection UAV
Obviously, 3D remote sensing technology can also be applied to plant protection drones in the future. In particular, if you can use low-altitude UAV remote sensing mapping, modeling, agricultural analysis and "Internet +" technology, construct a "3D spatial model platform for farmland forest land", provide space model, crop information, pest information and location of farmland forest land. Relationships, and even some environmental data (including temperature, humidity, light, etc.) can greatly help guide plant protection drones to fly and accurately apply drugs, and expand the application range of plant protection drones. This is just a vision, but I can look forward to it.
Using drones as a platform, research on monitoring plant growth, monitoring pests and diseases, and predicting harvests by remote sensing has been carried out, especially the production estimation/predictive harvest is one of the earliest research directions of remote sensing technology applied to agriculture. Mature agricultural remote sensing application research.
Remote sensing-based yield estimation began as early as the 1970s, such as the 1974 US Department of Agriculture (USDA), NASA and other departments to implement the "large-area crop estimation experiment", the LACIE program, mainly using Landsat MSS images. Estimate the area planted with wheat.
In the domestic research and development, there have been almost 30 years. In the mid-1980s, with the support of the State Economic Commission, the National Meteorological Administration organized the NOAA/AVHRR satellite remote sensing estimation study of winter wheat in 11 provinces and cities in the north, and established remote sensing. Image area measurement method.
In 1997, the “Ninth Five-Year Plan†major and special support project of the Chinese Academy of Sciences, “China Resource and Environment Remote Sensing Information System and Agricultural Quick Reportâ€, realized large-scale remote sensing monitoring and production forecasting of wheat, corn, soybean and rice in China.
During the “Eleventh Five-Year Plan†period, Beijing Normal University and the National Bureau of Statistics established the “ Remote Sensing Measurement and Estimation System for Grain Crop Planting Areas in the Major Grain Producing Areas of the Country â€.
At present, the main crop remote sensing monitoring and running systems at home and abroad (integrated crop acreage survey, growth monitoring and final yield estimation) are in the United States, the European Union and China.
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