Tea tree anthracnose diffusion prediction method based on remote sensing image and lattice point analysis

By combining remote sensing imagery and grid analysis with UAV multispectral imagery and continuous time-series environmental data, a tea anthracnose spread prediction model was constructed. This model solves the problems of low disease prediction accuracy and neglect of environmental factors in traditional models at the tea garden plot scale, and achieves precise control of tea anthracnose.

CN121095758AActive Publication Date: 2025-12-09ZHEJIANG UNIV OF WATER RESOURCES & ELECTRIC POWER
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Patent Information

Application Number
CN202511132191.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-13
Publication Date
2025-12-09
Estimated Expiration
2045-08-13

AI Technical Summary

Technical Problem

Existing tea anthracnose spread prediction models rely on empirical models, have low spatial resolution, and are difficult to accurately simulate the dynamic spread path and local spread hotspots of the disease at the tea garden plot scale. Furthermore, they ignore the complex interactions of environmental factors, resulting in low prediction accuracy.

Method used

Using a method based on remote sensing imagery and grid analysis, tea trees and soil backgrounds were identified through UAV multispectral imagery, and uniform grids were divided. A disease spread prediction model was constructed by combining continuous temporal environmental data to dynamically simulate the spatial spread and evolution trend of diseases. A spatiotemporal coupled model was constructed by integrating multi-temporal UAV remote sensing monitoring data and environmental factors.

Benefits of technology

It improved the accuracy and reliability of predicting the spread of anthracnose in tea trees, provided precise control measures, and reduced economic losses.

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Abstract

The invention discloses a tea tree anthracnose diffusion prediction method based on a remote sensing image and lattice point analysis, and the method comprises the following steps: obtaining an unmanned plane multispectral image of a tea tree land parcel, and carrying out the recognition of a soil background region, a healthy tea tree region and an infected region; dividing the tea tree plot into uniform grid spaces, defining the state of each grid point as a healthy grid point or an infected grid point, setting a diffusivity calculation method, and calculating the diffusivity of each grid point; setting a disease diffusion rule, updating the state of each grid point according to the disease diffusion rule, and completing the prediction of anthracnose diffusion. According to the invention, by fusing multi-temporal unmanned aerial vehicle remote sensing monitoring data and continuous time sequence environment data, a space-time coupling model for describing a disease infection mechanism between adjacent grid points is constructed, and the spatial diffusion and evolution trend of diseases is dynamically simulated; the problems that a traditional disease prediction method is high in experience dependence, poor in spatial dynamic simulation capability, low in temporal-spatial resolution and the like are solved.
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