Tuğrul Koç Mehmet, Kaya Rıza, Özkan Kemal, Ceyhan Merve, Gürel Uğur, Fidantemiz Fatih Yavuz
Faculty of Agriculture, Osmangazi University, Eskişehir, Turkey.
Plant Protection Department, Turkish Sugar Factories Corporation Sugar Institute, Etimesgut, Ankara, Turkey.
PeerJ. 2025 Jun 3;13:e19530. doi: 10.7717/peerj.19530. eCollection 2025.
Agricultural production is crucial for nutrition, but it frequently faces challenges such as decreased yield, quality, and overall output due to the adverse effects of diseases and pests. Remote sensing technologies have emerged as valuable tools for diagnosing and monitoring these issues. They offer significant advantages over traditional methods, which are often time-consuming and limited in sampling. High-resolution images from drones and satellites provide fast and accurate solutions for detecting and diagnosing crops' health and identifying pests and diseases affecting them.
The research focused on the early detection of leaf spot (.) and powdery mildew (), which cause significant economic losses in sugar beet before visible symptoms emerge. The study was accomplished by capturing images of uncrewed aerial vehicle (UAV) in field conditions. To effectively evaluate different detection methods in agricultural contexts, the study targeted two key areas: (1) monitoring in fields without pesticide application, utilizing the Metos climate station early warning system alongside UAV-based image analysis, and (2) monitoring powdery mildew, which involved visual disease detection and targeted spraying based on UAV image processing. Trial plots were established for this purpose, with six replications for each method.
UAV-based images show that Normalized Difference Vegetation Index values in leaves decreased before disease onset. This change is an important warning sign for the emergence of the disease. Additionally, the study demonstrated that early detection of diseases is possible using K-nearest neighbors and logistic regression algorithms, exhibiting high discrimination and predictive accuracy.
农业生产对营养至关重要，但由于病虫害的不利影响，它经常面临诸如产量、质量和总产量下降等挑战。遥感技术已成为诊断和监测这些问题的宝贵工具。与传统方法相比，它们具有显著优势，传统方法往往耗时且采样有限。无人机和卫星的高分辨率图像为检测和诊断作物健康状况以及识别影响作物的病虫害提供了快速准确的解决方案。
该研究聚焦于在甜菜出现可见症状之前对叶斑病（.）和白粉病（）进行早期检测，这两种病害会造成重大经济损失。该研究通过在田间条件下拍摄无人机图像来完成。为了在农业环境中有效评估不同的检测方法，该研究针对两个关键领域：（1）在未施用农药的田间进行监测，利用Metos气候站预警系统以及基于无人机的图像分析，（2）监测白粉病，这涉及基于无人机图像处理的目视病害检测和靶向喷洒。为此设立了试验田，每种方法进行六次重复。
基于无人机的图像显示，在病害发生前叶片的归一化植被指数值下降。这种变化是病害出现的重要警示信号。此外，该研究表明，使用K近邻算法和逻辑回归算法可以早期检测病害，具有很高的判别能力和预测准确性。
