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Inversion method of crop leaf area index based on prosail model and canopy coverage optimization

A technology of leaf area index and coverage, applied in the direction of nuclear method, image data processing, image analysis, etc., can solve problems such as inability to obtain results, and achieve the effect of reducing flight cost, strong robustness, and cost saving

Active Publication Date: 2022-07-29
HUAZHONG AGRI UNIV
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Problems solved by technology

However, for field crops, due to the influence of canopy structure factors such as ridge structure and soil background, the obtained remote sensing data cannot always be kept "pure", which makes PROSAIL not strictly follow the turbid medium when inverting LAI assuming that very precise results cannot be obtained
In previous studies, no specific solution was proposed for this problem.

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  • Inversion method of crop leaf area index based on prosail model and canopy coverage optimization

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Embodiment Construction

[0034] In order to solve the technical problem, the present invention provides a crop leaf area index inversion method based on PROSAIL model and canopy coverage optimization.

[0035] The crop leaf area index inversion method based on PROSAIL model and canopy coverage optimization is characterized in that it includes the following steps:

[0036] Step 1, using a spectral image sensor to obtain crop spectral data, and preprocessing to obtain reflectance data;

[0037] Step 2, perform threshold segmentation on the image, separate the pure vegetation area in the image, and extract the vegetation reflectance data in the observation area; at the same time, extract the vegetation canopy coverage information in the observation area;

[0038] Step 3: Perform sensitivity analysis on all parameters of the PROSAIL radiative transfer model, and screen out several parameters that are sensitive to the model simulation results;

[0039] Step 4: Set a reasonable value range and step size fo...

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Abstract

The invention discloses a crop leaf area index inversion method based on PROSAIL model and canopy coverage optimization. The present invention utilizes the LAI of canopy coverage parameter optimization and simulation to ensure that accurate results can still be obtained under the condition that the assumption of turbid medium is not fully satisfied, and the overall accuracy is improved. The established neural network model has strong robustness and can be adapted to various situations. The present invention does not have high requirements on the resolution of the image, and with a suitable resolution, similar and ideal results can still be obtained. For remote sensing data collection using drones, the cost of purchasing multispectral cameras can be saved, and the flight height can be increased when acquiring images to reduce flight costs.

Description

technical field [0001] The invention belongs to the field of agricultural automation, in particular to a method for extracting crop leaf area index, in particular to a crop leaf area index inversion method based on PROSAIL model and canopy coverage optimization. Background technique [0002] Leaf area index (LAI) is an important parameter related to many biological and physical processes such as photosynthesis and transpiration in crops. Spatiotemporal LAI information can be used to optimize agricultural management decisions. [0003] By applying remote sensing technology, low-cost, high-efficiency, and damage-free LAI prediction can be achieved. Generally speaking, there are two main methods in the extraction of LAI or other biochemical parameters by remote sensing technology, one is the empirical statistical model algorithm, and the other is the radiative transfer model (RTM) algorithm. The empirical statistical model usually combines the vegetation index to establish an...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/136G06T7/62G06N20/10
CPCG06T7/0002G06T7/136G06T7/62G06N20/10G06T2207/10032G06T2207/20084G06T2207/30188Y02T10/40
Inventor 张建孙博王楚锋谢田晋谢静周广生
Owner HUAZHONG AGRI UNIV
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