Zhang Jing, Zhu Linglan, Wang Yifang, Chen Si, Wang Yafei, Li Shifa, Xiao Lu, Yang Ning
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China.
School of Innovative Design, City University of Macau, Macao, Macao SAR, China.
Front Plant Sci. 2025 May 23;16:1553281. doi: 10.3389/fpls.2025.1553281. eCollection 2025.
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.
植物病害的早期病原菌具有浓度低、检测难的特点，这加剧了病害溯源的难度，导致病害迅速传播且难以有效控制。目前，成像和光谱学等常见的植物病害检测技术只能在病害发生和显现后应用，在孢子萌发或传播阶段难以准确确定病害爆发的源头。因此，本文提出一种基于非局部插值算法的气传植物病害源头定位方法。首先，基于米氏散射理论设计了一种高灵敏度浓度传感器，用于精确计数植物病害中的孢子，并构建了多传感器协同计算网络模型。其次，通过收集不同位置的孢子数量数据，建立粒子扩散模型，以总结特定区域条件下粒子的传播模式。最后，设计了一种结合改进幂律方程的非局部插值算法，用于在不同风向和风力条件下精确定位气传植物病害的源头。温室实验结果表明，光散射计数的最大误差不超过10%；在无风和风的条件下，我们的方法分别实现了94.7%和92.9%的定位准确率，每平方米约有三个节点。这为植物病害的早期诊断和精确定位提供了新的思路和见解。
