Field Assessment Strategies: Assessing and Classifying Blight Disease in Wild Blueberry Populations Using Multispectral and Hyperspectral Sensors

Abstract

(1) Background: Monilinia and Botrytis blight are significant diseases affecting wild blueberry fields, leading to substantial yield losses. Traditional methods for disease assessment rely on destructive sampling, which is labor-intensive and subjective. This study explored the use of multispectral and hyperspectral sensors through simple and machine learning approaches to detect and assess Monilinia and Botrytis blight diseases. (2) Methods: In this study, we adopted two experimental approaches: plot and patch assessment trials. These were conducted using a randomized complete block design at three locations in Nova Scotia. Disease detection was performed using vegetative indices (VIs) and spectral reflectance analysis, with destructive samples also assessed. Analysis of variance, correlations and classification approaches were used in the analysis. (3) Results: Significant spectral differences were observed between healthy and diseased plants, particularly in the near-infrared region (715–1050 nm). Nine significant wavelength bands were identified for blight disease detection. Classifier analysis revealed that support vector machines (SVM) and random forests (RF) outperformed k-nearest neighbors (KNN), achieving an overall accuracy of 96.6% and 76.8% in the broad and severity disease level classifications. (4) Conclusions: Despite some limitations, these findings underscore the potential of remote sensing tools for efficient, non-destructive disease management in wild blueberry fields.

Plant phenotyping relevance

野生ブルーベリーの病徴・重症度をマルチスペクトル／ハイパースペクトルセンサーと分類手法で非破壊的に検出・評価する方法が研究の中心であり、植物病害状態の表現型計測に該当する。

Code and data availability

The supplied blocks describe UAV multispectral imagery, handheld hyperspectral readings, and ML classification, but contain no data availability statement, public dataset deposit, or author code repository. The only URLs present (agriculture.canada.ca, cdn.dal.ca, njaes.rutgers.edu) are cited background references, not

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