A method for predicting the spread of anthracnose in tea trees based on remote sensing imagery and grid analysis

By using UAV remote sensing imagery and grid analysis, a model for predicting the spread of anthracnose in tea trees was constructed. This solved the problems of traditional models relying on experience and having low spatial resolution, enabling precise control of anthracnose in tea gardens and improving the accuracy and reliability of disease prediction.

CN121095758BActive Publication Date: 2026-05-26ZHEJIANG UNIV OF WATER RESOURCES & ELECTRIC POWER
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ZHEJIANG UNIV OF WATER RESOURCES & ELECTRIC POWER
Filing Date
2025-08-13
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing tea anthracnose spread prediction models rely on experience, 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 fail to fully consider 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 are identified through UAV multispectral imagery, and uniform grids are divided. A disease spread prediction model is constructed by combining continuous temporal environmental data to dynamically simulate the spatial spread and evolution trend of diseases. By integrating multi-temporal UAV remote sensing monitoring data and environmental factors, disease spread rules are set, and grid status is updated to predict disease spread.

Benefits of technology

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

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Abstract

This invention discloses a method for predicting the spread of anthracnose in tea trees based on remote sensing imagery and grid analysis, comprising the following steps: acquiring multispectral images of tea plantations from unmanned aerial vehicles (UAVs), identifying soil background areas, healthy tea tree areas, and infected areas; dividing the tea plantation into a uniform grid space, defining the state of each grid point as either a healthy grid point or an infected grid point, setting a diffusion rate calculation method, and calculating the diffusion rate of each grid point; setting disease diffusion rules, updating the state of each grid point according to the disease diffusion rules, and completing the prediction of anthracnose spread. This invention, by fusing multi-temporal UAV remote sensing monitoring data and continuous time-series environmental data, constructs a spatiotemporal coupling model describing the disease infection mechanism between adjacent grid points, dynamically simulating the spatial spread and evolution trend of the disease, overcoming the problems of strong experience dependence, poor spatial dynamic simulation capability, and low spatiotemporal resolution of traditional disease prediction methods.
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