Identification and Classification of Downy Mildew Severity Stages in Watermelon Utilizing Aerial and Ground Remote Sensing and Machine Learning

Abstract

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.

A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance

“…Based on the data collected from leaves from this region and the visual detection of ginseng roots after harvest, ML algorithms were used to construct the model of root diseases detection. The combination of non-destructively acquired hyperspectral reflectance data and ML algorithms can recognize the tiny changes of hyperspectral reflectance of the asymptomatic patients, that greatly improves the classification accuracy of the model ( Sankaran et al., 2012 ; Abdulridha et al., 2022 ). For example, the logistic regression-based ML algorithms by Appeltans et al.…”

“…Hyperspectral vegetation indices (VIs) were widely deployed to estimate plant biophysical and biochemical traits ( Koh et al., 2022 ). The downy mildew severity stages in watermelon are significantly correlated with the chlorophyll green, photochemical reflectance index and normalized phaeophytinization index ( Abdulridha et al., 2022 ). Improved accuracy of hyperspectral data processing due to advances of machine learning (ML) enabling further development of non-invasive high-throughput plant phenotyping ( Mahlein et al., 2019 ; Arya et al., 2022 ).…”

Useful Feature Extraction and Machine Learning Techniques for Identifying Unique Pattern Signatures Present in Hyperspectral Image Data

“…Hyperspectral imaging and data analysis have recently received considerable attention since the representative data is ultra-high resolution and informative [1][2][3][4][5][6][7].…”

“…Detecting plant diseases and stress factors in early stages of disease development is essential for selective and effective management of crop production. Through other data analysis procedures, such as feature extraction, statistical prediction, and reduced signature spectrums, pinpointing where the spectrums differ between a range of normal signatures and abnormal feature spectrums will reveal variations in the species [3,8,9]. Realizing how these spectrums differ because of diseases, abiotic stressors, nutritional deficiencies, or other factors, will give more useful information to the farmers.…”

“…Laboratory analysis of plant samples for disease detection is time-consuming and labor-intensive. For that reason, several disease detection methods have been developed utilizing advanced and sophisticated hyperspectral data analysis approaches [3,8,9] and MLAs. These unique applications will be reviewed.…”