Gridding rice sheath blight habitat evaluation method integrating multi-source remote sensing information

Abstract

The invention discloses a gridding rice sheath blight habitat evaluation method integrating multi-source remote sensing information. Prediction only depends on punctiform meteorological data and is relatively extensive, and a continuous planar prediction result cannot be provided. The method comprises the following steps: 1, selecting an investigation area, acquiring multi-source remote sensing data of the investigation area and investigating disease severity of the investigation area; 2, determining the position and area of late rice; and 3, extracting rice sheath blight habitat characteristics based on the multi-source remote sensing image. And 4, establishing a disease habitat suitability model. And 5, gridding the measured field, and extracting the remote sensing habitat characteristics of the rice sheath blight disease. And 6, masking each remote sensing habitat feature by using the late rice classification map to obtain each remote sensing habitat factor feature of the late rice.And 7, acquiring the disease habitat suitability degree corresponding to each grid according to the model. And 8, obtaining a disease habitat suitability spatial distribution map. Through investigation and research analysis, most of soil-borne diseases are found to be related to habitat conditions such as host plant growth state and farmland environment.

Description

technical field [0001] The invention belongs to the field of analysis and processing of multi-source satellite remote sensing data and agronomy technology, and in particular relates to a method for assessing rice sheath blight habitat with space grid and integrating multi-source remote sensing information. Background technique [0002] Crop diseases are an important reason for the reduction of grain production in my country and even in the world. With global climate change, the frequency of disease occurrence has been increasing in recent years, posing a serious threat to my country's food security. Therefore, the effective prediction of the occurrence and spread of diseases can play a positive guiding role in the prevention and control work, and reduce the amount of pesticides, which is of great significance to my country's grain production. At present, the prediction of diseases and insect pests is mainly based on point meteorological data, but the occurrence and developm...

AI Technical Summary

Problems solved by technology

At present, the prediction of diseases and insect pests is mainly based on point meteorological data, but the occurrence and development of crop diseases are closely related to different farmland environments and vegetation nutritional status. Therefore, forecasting only by meteorological data is relatively extensive and cannot provide continuous area prediction results.

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