Advanced Machine Learning Methods for Early Detection of ... · Titelmaster Advanced Machine...

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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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20.09.2010 Advanced Machine Learning Methods for Early Detection ...

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

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