Sabine Daniela Bauer and Filip Korč and Wolfgang Förstner, "The potential of automatic methods of classification to identify leaf diseases from multispectral images", Precision Agriculture. Vol. 12(3), pp. 361-377. 2011.

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Three methods of automatic classification of leaf diseases are described based on high-resolution multispectral stereo images. Leaf diseases are economically important as they can cause a loss of yield. Early and reliable detection of leaf diseases has important practical relevance, especially in the context of precision agriculture for localized treatment with fungicides. We took stereo images of single sugar beet leaves with two cameras (RGB and multispectral) in a laboratory under well controlled illumination conditions. The leaves were either healthy or infected with the leaf spot pathogen Cercospora beticola or the rust fungus Uromyces betae. To fuse information from the two sensors, we generated 3-D models of the leaves. We discuss the potential of two pixelwise methods of classification: k-nearest neighbour and an adaptive Bayes classification with minimum risk assuming a Gaussian mixture model. The medians of pixelwise classification rates achieved in our experiments are 91% for Cercospora beticola and 86% for Uromyces betae. In addition, we investigated the potential of contextual classification with the so called conditional random field method, which seemed to eliminate the typical errors of pixelwise classification.

@article{Bauer2011potential,
  author = {Bauer, Sabine Daniela and Kor{\vc}, Filip and F\"orstner, Wolfgang},
  title = {The potential of automatic methods of classification to identify leaf diseases from multispectral images},
  journal = {Precision Agriculture},
  year = {2011},
  volume = {12},
  number = {3},
  pages = {361--377},
  doi = {10.1007/s11119-011-9217-6}
}

Sabine Daniela Bauer, "Automatische Detektion von Krankheiten auf Blättern von Nutzpflanzen". Thesis at: Institute of Photogrammetry, University of Bonn. 2011.

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Summary
This thesis presents a novel, automatic method to detect leaf diseases on agricultural crop. The use of pattern recognition methods to detect leaf diseases is a very young research area. But an applicable method for the automatic detection of leaf diseases would be of great benefit for example in the area of precision agriculture. The proposed method consists of a hierarchical classification process. In the first step of this process a pixelwise, adaptive Bayes classification is executed. Thereby the adaptive Bayes classifier is optimised for the detection of leaf diseases. In the second step a smoothing of the pixelwise result is carried out using a majority filter or a conditional random field (CRF). The advantages and disadvantages of the two ways of smoothing are the subject of a discussion in this thesis. The last step of the classification process consists of a regionbased Maximum-Likelihood classification. A particular challenge in the development of the classification method lies in the consideration of leaf diseases with very small leaf spots. To achieve high classification accuracies for these leaf diseases, too, an adequate rating strategy was formulated and used for the optimisation of the classification process. The developed classification method is based on images of a RGB - and a Multispectral (MS) - camera. The images of the two cameras were fused using a 3D-model of each single leaf. The prepared technique for the generation of the 3D-models of the leaves provides additionally the opportunity to determine the position and the orientation of the leaves in the field. Within the framework of the development of the classification process further questions were examined. So it was investigated, in how far an enhancement of the feature vector with neighbourhood information affects the classification result and if it is possible to apply segmentation methods like the watershed algorithm for the recognition of leaf spots. In addition the transferability of the developed classification method to other plants and leaf diseases was proofed.
Zusammenfassung
In dieser Arbeit wird ein neuartiges, automatisches Verfahren zur Detektion von Krankheiten auf Blättern von Nutzpflanzen vorgestellt. Die Anwendung von Mustererkennungsverfahren zur Detektion von Blattkrankheiten ist noch ein sehr junges Forschungsgebiet. Ein einsetzbares Verfahren zur automatischen Detektion von Blattkrankheiten würde aber z.B. im Präzisionspflanzenbau zur Reduzierung der Spritzmittelmenge von großem Nutzen sein. Das in dieser Arbeit vorgeschlagene Verfahren besteht aus einem hierarchischen Klassifikationsprozess. Im ersten Schritt dieses Prozesses wird eine pixelweise, adaptive Bayesklassifikation durchgeführt. Die adaptive Bayesklassifikation ist dabei speziell auf die Erkennung von Blattkrankheiten hin optimiert worden. Im zweiten Schritt erfolgt eine Glättung des pixelweisen Klassifikationsergebnisses durch eine Majoritätsfilterung oder mit Hilfe von bedingten Markoffschen Zufallsfeldern. Welche Vor- und Nachteile die beiden Glättungsarten bieten, wird in dieser Arbeit diskutiert. Der letzte Schritt des hierarchischen Klassifikationsprozesses besteht aus einer regionenbasierten Maximum-Likelihood-Klassifikation. Eine besondere Herausforderung bei der Entwicklung des Klassifikationsverfahrens lag in der Berücksichtigung von Blattkrankheiten mit sehr kleinen Blattflecken. Um auch für diese Blattkrankheiten hohe Klassifikationsgenauigkeiten zur erzielen, wurde eine geeignete Bewertungsstrategie zur Beurteilung der Klassifikationsergebnisse entworfen und zur Optimierung des Klassifikationsprozesses verwendet. Das entwickelte Klassifikationsverfahren basiert auf Bildern von einer RGB - und einer Multispektral (MS) - Kamera. Die Bilder der beiden Kameras wurden über ein 3D-Modell des jeweiligen Blattes fusioniert. Das zur Erstellung der 3D-Modelle entwickelte Verfahren bietet dabei neben der Sensorfusion auch das Potential zur Bestimmung der Lage und Orientierung von Blättern im Feld. Im Rahmen der Entwicklung des Klassifikationsprozesses wurden weitere Fragestellungen bearbeitet. So wurde u.a. untersucht, inwieweit eine Erweiterung des Merkmalsvektors um Nachbarschaftsinformationen das Klassifikationsergebnis beeinflusst und ob Segmentierungsverfahren wie der Wasserscheidenalgorithmus zur Erkennung von Blattflecken eingesetzt werden können. Desweiteren wurde die Übertragbarkeit des Verfahrens auf andere Nutzpflanzen und Blattkrankheiten erforscht.

@phdthesis{Bauer2011Automatische,
  author = {Bauer, Sabine Daniela},
  title = {Automatische Detektion von Krankheiten auf Bl\"attern von Nutzpflanzen},
  school = {Institute of Photogrammetry, University of Bonn},
  year = {2011}
}

Sabine Daniela Bauer and Filip Korč and Wolfgang Förstner, "Investigation into the classification of diseases of sugar beet leaves using multispectral images", In Precision Agriculture 2009. Wageningen, pp. 229-238. 2009.

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This paper reports on methods for the automatic detection and classification of leaf diseases based on high resolution multispectral images. Leaf diseases are economically important as they could cause a yield loss. Early and reliable detection of leaf diseases therefore is of utmost practical relevance - especially in the context of precision agriculture for localized treatment with fungicides. Our interest is the analysis of sugar beet due to their economical impact. Leaves of sugar beet may be infected by several diseases, such as rust (Uromyces betae), powdery mildew (Erysiphe betae) and other leaf spot diseases (Cercospora beticola and Ramularia beticola). In order to obtain best classification results we apply conditional random fields. In contrast to pixel based classifiers we are able to model the local context and contrary to object centred classifiers we simultaneously segment and classify the image. In a first investigation we analyse multispectral images of single leaves taken in a lab under well controlled illumination conditions. The photographed sugar beet leaves are healthy or either infected with the leaf spot pathogen Cercospora beticola or with the rust fungus Uromyces betae. We compare the classification methods pixelwise maximum posterior classification (MAP), objectwise MAP as soon as global MAP and global maximum posterior marginal classification using the spatial context within a conditional random field model.

@inproceedings{Bauer2009Investigation,
  author = {Bauer, Sabine Daniela and Kor{\vc}, Filip and F\"orstner, Wolfgang},
  title = {Investigation into the classification of diseases of sugar beet leaves using multispectral images},
  booktitle = {Precision Agriculture 2009},
  year = {2009},
  pages = {229--238}
}