Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques

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 ar...

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Veröffentlicht in:Sensors (Basel, Switzerland) Switzerland), 2024-12, Vol.24 (23), p.7774
Hauptverfasser: Knauer, Uwe, Warnemünde, Sebastian, Menz, Patrick, Thielert, Bonito, Klein, Lauritz, Holstein, Katharina, Runne, Miriam, Jarausch, Wolfgang
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container_title Sensors (Basel, Switzerland)
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creator Knauer, Uwe
Warnemünde, Sebastian
Menz, Patrick
Thielert, Bonito
Klein, Lauritz
Holstein, Katharina
Runne, Miriam
Jarausch, Wolfgang
description 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, the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms was investigated to detect the symptoms solely based on the spectral signature of collected leaf samples. In the growing seasons 2019 and 2020, a total of 1160 leaf samples were collected. 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. The 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. Classification models like rRBF achieved an accuracy of 0.971 in a controlled environment with stratified data for a single variety. Combined models for multiple varieties from field test samples achieved classification accuracies of 0.731. Including spatial distribution of spectral data further improves the results to 0.751. Prediction of qPCR results by regression based on spectral data achieved RMSE of 14.491 phytoplasma per plant cell.
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subjects Algorithms
apple
Breakdowns
Candidatus Phytoplasma mali
Chlorophyll
Cultivars
Data collection
Data mining
disease monitoring
Disease prevention
Disease transmission
Experiments
hyperspectral
Hyperspectral Imaging - methods
Image Processing, Computer-Assisted - methods
Infections
Information management
Leaves
Machine Learning
Malus
Methods
Neural networks
Plant diseases
Plant Diseases - microbiology
Plant Leaves
Quarantine
random forest
Sensors
Spectrum analysis
Unmanned aerial vehicles
Vegetation
title Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques
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