Detection of Apple Proliferation Disease by Hyperspectral Sensors and Machine Learning Based Image Analysis

Abstract

Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human observers consider multiple symptoms, but these are difficult to measure automatically in the field. Therefore, we investigated the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms to detect the symptoms solely based on the spectral signature of collected leaf samples. In the growing seasons 2019 and 2020, we collected a total of 1,160 leaf samples. Hyperspectral imaging with a dual camera setup in spectral bands from 400 nm to 2500 nm was accompanied with subsequent PCR analysis of the samples to provide reference data for the machine learning approaches. Data processing consists of preprocessing for segmentation of the leaf area, feature extraction, classification and subsequent analysis of relevance of spectral bands. Results show, that imaging multiple leaves of a tree enhances detection results, that spectral indices are a robust means to detect the diseased trees, and that the potentials of the full spectral range can be exploited using machine learning approaches.

Plant phenotyping relevance

葉のハイパースペクトル画像と機械学習により、リンゴ樹の病徴・罹病状態を自動検出する手法が研究の中心であり、植物病害フェノタイピング手法に該当する。

Code and data availability

The paper's hyperspectral apple-proliferation datasets are not publicly deposited; the Data Availability Statement says they are available only upon request. No public code, model, or image repository with an authors' URL is provided.

No evidence-backed public reproduction asset is currently recorded.