Multispectral Tree Health Detection via Neural Networks
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Solution Overview
Problem
Current methods for identifying infected trees, particularly those affected by bark beetles, are time-consuming and visually challenging, leading to potential over-cutting of trees and environmental impact.
Innovation Solution
A computer-implemented method using multispectral imaging and convolutional neural networks to build a training database and detect the health status of trees, incorporating indexes like NDVI, AVI, and NDMI, enabling precise and efficient identification of tree health and disease presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection methods are used to identify infected trees, then the identification process can be performed with simple equipment, but the process is time-consuming and lacks precision
Solution Approach 1:
The patent replaces manual visual inspection with an automated image processing system using multispectral cameras and convolutional neural networks. The system captures multispectral images, computes vegetation indexes (NDVI, AVI, NDMI), and automatically detects infected trees, eliminating the time-consuming nature of manual inspection while significantly improving identification precision through objective spectral analysis.
Solution Approach 2:
The patent transforms the identification process by changing from visual parameters to spectral parameters. By capturing images in multiple spectral bands and computing vegetation indexes, the system detects subtle spectral changes indicative of infestation that are imperceptible to the human eye, thereby improving precision without increasing time investment.
2Difficulty of detecting and measuring
If manual identification methods are used, then equipment complexity is low, but the difficulty of detecting and measuring tree health increases
Solution Approach 1:
The patent introduces multispectral vegetation indexes (NDVI, AVI, NDMI) as intermediary parameters that translate complex spectral data into interpretable health indicators. These indexes serve as mediators between the raw multispectral data and the final detection output, simplifying the detection process by highlighting spectral signatures associated with infestation while managing system complexity through established computational methods.
3Reliability
If infested trees are cut down to prevent disease spread, then the spread of bark beetle disease is controlled, but environmental impact increases and forestry revenues decrease
Solution Approach 1:
The patent enables preliminary identification of infected trees before they become sources of widespread infestation. By detecting spectral changes early in the infestation process, the system allows for targeted intervention only on affected trees rather than preventive cutting of entire areas, thereby maintaining disease control effectiveness while reducing environmental impact and preserving forestry revenues.
Data Source
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AI summary
The invention relates to a method for determining the health status of vegetal elements. The method comprises: - a phase of building a training database by associating, for each zone of space, input data indicative of the position and health status of target vegetal elements for said zone of space and a concatenation of computed multispectral indexes for said zone of space, - a phase of training a detection model on the basis of the training database so as to obtain a trained detection model configured for determining the position and health status of target vegetal elements in a zone of space as a function of a set of multispectral images of the zone of space, and - a phase of operating the trained detection model.