数字农科院2.0

Crop Disease Source Location and Monitoring System Based on Diffractive Light Identification Airborne Spore Sensor Network

文献类型： 外文期刊

作者： Zhang, Jing;Pan, Chen;Liu, Shuhua;Kou, Yanjun;Tang, Jian;Wang, Yafei;Yang, Ning;Huang, Rubing

作者机构：

关键词： Crop disease monitoring;diffraction imaging;Internet of Things (IoT) system;location of disease;particle diffusion model

期刊名称： IEEE INTERNET OF THINGS JOURNAL

ISSN： 2327-4662

年卷期： 2022 年 9.0 卷 13 期

页码：

收录情况： SCIE（2022版） ; EI（2022版）

摘要： Traditional methods based on the Internet of Things (IoT) or IoT methods based on microscopic imaging are difficult to automatically realize early warning of crop diseases. In this article, a diffraction imaging IoT system based on spore detection is proposed to indirectly monitor crop diseases instead of directly taking crop disease images. Multiple NB-IoT nodes are deployed to build an IoT system to realize the judgment of spore diffraction image transmission, which is based on the detection of environmental temperature and humidity. The method of digital image processing is applied to filter out impurities and count microparticles with the accuracy of 85%. By obtaining the number of spores in different positions, the microparticles diffusion model is established to study the law of microparticles transmission in specific space. According to the diffusion model, the weighted centroid and particle filter algorithm are applied to locate the particle source in windless and windy conditions. Thirteen nodes are arranged in a 2 m x 2 m laboratory to carry out the experiment. The maximum error in windless and windy conditions is 0.18 and 0.35 m. Compared with the traditional microscopic imaging-based IoT method, the detection limit of the proposed diffraction imaging method is 1/50. It provides inspiration for the IoT in the early detection and disease location of crop diseases.

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