# Location method of airborne plant disease source based on a non-local-interpolation algorithm

## TL;DR

This paper introduces a new method to locate the source of airborne plant diseases using a non-local-interpolation algorithm and sensitive spore detection.

## Contribution

A novel non-local-interpolation algorithm is proposed for early and accurate localization of airborne plant disease sources.

## Key findings

- A spore concentration sensor based on Mie scattering theory achieved a maximum counting error of 10%.
- The proposed method achieved localization accuracies of 94.7% and 92.9% in windless and windy conditions, respectively.
- The method uses a particle diffusion model and multi-sensor network to trace disease sources during early propagation stages.

## Abstract

The early stage pathogens of plant diseases have the characteristic of low concentration and difficult detection, which exacerbates the difficulty of tracing the disease, leading to rapid spread and difficulty in effective control. Currently, common plant disease detection techniques such as imaging and spectroscopy can only be applied after the occurrence and manifestation of diseases, and it is difficult to accurately locate the source of disease outbreaks during spore germination or propagation stages. Therefore, this paper proposes a method for locating the source of airborne plant diseases based on the non-local-interpolation algorithm. Firstly, a highly sensitive concentration sensor was designed based on Mie scattering theory to accurately count spores in plant diseases, and a multi-sensor collaborative computing network model was constructed. Secondly, by collecting spore quantity data at different locations, a particle diffusion model is established to summarize the propagation patterns of particles under specific regional conditions. Finally, a non-local-interpolation algorithm coupled with improved power-law equations was designed for precise localization of airborne plant disease sources under different wind and direction conditions. The experimental results in the greenhouse show that the maximum error of light scattering counting does not exceed 10%; Under windless and windy conditions, our method achieved localization accuracies of 94.7% and 92.9%, respectively, with approximately three nodes per square meter. This provides new ideas and insights for early diagnosis and precise localization of plant diseases.