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Titelmaster
Advanced Machine Learning Methods for Early Detection of
Weeds and Plant Diseases in Precision Crop Protection
Lutz Plümer, Till Rumpf, Christoph Römer
University of Bonn
Insitute of Geodesy and Geoinformation
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Introduction
• Starting Point: Early, presymptomatic detection and
identification of weed and plant diseases
• Shape Features of Image Processing and
Hyperspectral Signatures provide high Potentials
• Challenge: „Interpretation of the Data“
– Data are noisy
– Many features, highly correlated
– Signal/noise relation is demanding
– Labelling is expensive
– Separation boundaries are highly non-linear
• Advanced Machine Learning Methods such Support Vector Machines meet these challenges
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Early Identification of Weed
• Joint work of Till Rumpf with
Roland Gerhards & Martin Weis
• Example: Galium aparine
• Starting Point: Bispectral
images
• Construction of shape parameters
• Good separability between
crop and weed
– Hordeum vulgare &
dicotyles (Galium aparine ,
Veronica persica)
• Separation of Galium aparine
from Veronica persica is hardVeronica persicaGalium aparine
Hordeum vulgare
Hordeum vulgare – Galium aparine
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Shape Features and their Distributions
• Moderate overlap between
Hordeum Vulgare and
Galium aparine
• Strong overlap between
dicotyles
• separation between different
dicotyles is highly non-linear
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Support Vector Machines
• Advanced Machine Learning
Method
• High Generalization
Capability
• Moderate Risk of Overfitting
• Identifies hyperplane with
maximal margin
• Soft margin with penalty
term for classification errors
• Achieve Nonlinearity by
transformation to a feature
space with higher dimension
via appropriate Kernels(Radial Basis Functions -
RBF for instance)
SVM – the linear case
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Classification Results
Binary Classification
• Galium aparine, Veronica
persica (dicotyles)
• Hordeum vulgare
Classification
method
Classification
accuracy
Linear discriminant
analysis (LDA)76.72%
Support Vector Machines (SVMs)
83.98%
Multiple Classes:
• 10 dicotyles
• Hordeum vulgare
Classification
method
Classification
accuracy
Linear discriminant
analysis (LDA)53.30%
Support Vector Machines (SVMs)
69.25%
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Identification of Plant Diseases with Hyperspectral Indices
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Hyperspectral Reflection
8 (Mahlein 2010)
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Hyperspectral Signatures and Hyperspectral Indices
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Early Detection of Cercospora
• Combination of 9 different hyperspectral indices
• Identification of Cercospora before occurrence of visible
Symptoms
• Joint work of Till Rumpf with Oerke, Mahlein et al
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Hyperspectral Signature
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Exploiting the Hypespectral Signature
• Contains lots of (highly
redundant) information
• Identify Medians of
innoculated and
cercospora
• Compare median
differences with deviation
• Identify relevant wavelenghts
• Identify significant subsets of wavelengths
– Maximize relevance
– Minimize redundancy
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Hyperspectral Fluorescence
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Hyperspectral Fluorescence
• Hyperspectral Fluorescence
• Joint work with Noga,
Hunsche, Bührling
• Example: Leaf Rust
• Fluorescence Emission
makes up the balance of
different processes, namely:
• Immune system
• Fungi
• Photosynthesis
• Changes in fluorescence
reveal stress reaction
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Variance
• Variation within class members higher than between classes
• Number of features (wavelenghts) rather high
compared to the number of samples
• Feature Construction
represent the shape of the curve with few parameters
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Polynomial Fitting
2
210 xaxaay
• Piecewise polynomial fitting
• Coefficients (a0…an) of the polynom carry information
on the shape of the curve
• Use coefficients as features for the classifier
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Results
• Best results for presymptomatic identification of leaf rust with
polynomials of 4th order
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Summary & Outlook
• Early detection of several instances of biotic stress
(weed, cercospora, leaf rust)
• Different features: shape descriptors, indices, (subsets
of) wavelengths, descriptors of polynomials
• Support Vector Machines outperform other Classifiers
• Feature Construction & Selection important topic
– What is the best way to represent the information
content of the hyperspectral signal
• Early Identification - a „multi-objective optimizationproblem“: earliest possible date, highest accuracy
• Future research: Exploiting Structure
– of the signal, include temporal dimension, structure
of the leaf, structure of the plant, tailorized kernels