Advanced Machine Learning Methods for Early Detection of Weeds and Plant Diseases in Precision Crop Protection
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1 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
2 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 2
3 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 hard Hordeum vulgare Galium aparine Galium aparine Veronica persica 3 Hordeum vulgare
4 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 4
5 Support Vector Machines SVM the linear case 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) 5
6 Classification Results Binary Classification Galium aparine, Veronica persica (dicotyles) Hordeum vulgare Classification method Linear discriminant analysis (LDA) Classification accuracy 76.72% Support Vector Machines (SVMs) 83.98% 6 Multiple Classes: 10 dicotyles Hordeum vulgare Classification method Linear discriminant analysis (LDA) Support Vector Machines (SVMs) Classification accuracy 53.30% 69.25%
7 Titelmaster Identification of Plant Diseases with Hyperspectral Indices
8 Hyperspectral Reflection 8 8 (Mahlein 2010)
9 Hyperspectral Signatures and Hyperspectral Indices 9
10 10
11 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 11
12 12
13 Titelmaster Hyperspectral Signature
14 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 14
15 Titelmaster Hyperspectral Fluorescence
16 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 16
17 Variance 17 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
18 Polynomial Fitting y a 2 0 a1x a2x 18 Piecewise polynomial fitting Coefficients (a 0 a n ) of the polynom carry information on the shape of the curve Use coefficients as features for the classifier
19 Results 19 Best results for presymptomatic identification of leaf rust with polynomials of 4th order
20 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 optimization problem : 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 20
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