Abdulridha Jaafar, Ampatzidis Yiannis, Qureshi Jawwad, Roberts Pamela
Department of Agricultural and Biological Engineering, Southwest Florida Research and Education Center, University of Florida, Immokalee, FL, United States.
Department of Entomology and Nematology, Southwest Florida Research and Education Center, University of Florida, Immokalee, FL, United States.
Front Plant Sci. 2022 May 20;13:791018. doi: 10.3389/fpls.2022.791018. eCollection 2022.
Remote sensing and machine learning (ML) could assist and support growers, stakeholders, and plant pathologists determine plant diseases resulting from viral, bacterial, and fungal infections. Spectral vegetation indices (VIs) have shown to be helpful for the indirect detection of plant diseases. The purpose of this study was to utilize ML models and identify VIs for the detection of downy mildew (DM) disease in watermelon in several disease severity (DS) stages, including low, medium (levels 1 and 2), high, and very high. Hyperspectral images of leaves were collected in the laboratory by a benchtop system (380-1,000 nm) and in the field by a UAV-based imaging system (380-1,000 nm). Two classification methods, multilayer perceptron (MLP) and decision tree (DT), were implemented to distinguish between healthy and DM-affected plants. The best classification rates were recorded by the MLP method; however, only 62.3% accuracy was observed at low disease severity. The classification accuracy increased when the disease severity increased (e.g., 86-90% for the laboratory analysis and 69-91% for the field analysis). The best wavelengths to differentiate between the DS stages were selected in the band of 531 nm, and 700-900 nm. The most significant VIs for DS detection were the chlorophyll green (Cl green), photochemical reflectance index (PRI), normalized phaeophytinization index (NPQI) for laboratory analysis, and the ratio analysis of reflectance spectral chlorophyll-a, b, and c (RARSa, RASRb, and RARSc) and the Cl green in the field analysis. Spectral VIs and ML could enhance disease detection and monitoring for precision agriculture applications.
遥感和机器学习（ML）可以协助和支持种植者、利益相关者以及植物病理学家确定由病毒、细菌和真菌感染导致的植物病害。光谱植被指数（VIs）已被证明有助于间接检测植物病害。本研究的目的是利用ML模型并识别用于检测西瓜霜霉病（DM）在几个病害严重程度（DS）阶段（包括低、中（1级和2级）、高和非常高）的植被指数。通过台式系统（380 - 1000纳米）在实验室收集叶片的高光谱图像，并通过基于无人机的成像系统（380 - 1000纳米）在田间收集。实施了两种分类方法，即多层感知器（MLP）和决策树（DT），以区分健康植物和受DM影响的植物。MLP方法记录了最佳分类率；然而，在低病害严重程度时仅观察到62.3%的准确率。当病害严重程度增加时，分类准确率提高（例如，实验室分析为86 - 90%，田间分析为69 - 91%）。用于区分DS阶段的最佳波长在531纳米波段以及700 - 900纳米波段中被选定。用于DS检测的最显著的植被指数在实验室分析中是叶绿素绿（Cl green）、光化学反射指数（PRI）、归一化脱镁叶绿素指数（NPQI），在田间分析中是反射光谱叶绿素a、b和c的比率分析（RARSa、RASRb和RARSc）以及Cl green。光谱植被指数和ML可以增强精准农业应用中的病害检测和监测。
