Automated identification of sugar beet diseases using smartphones
| Autor: | Lisa Hallau, Marion Neumann, Kristian Kersting, Benjamin Klatt, Erich-Christian Oerke, C. Bauckhage, B. Kleinhenz, Anne-Katrin Mahlein, M. Röhrig, T. Klein, C. Kuhn, Ulrike Steiner |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
Plant Science Disease Horticulture 01 natural sciences CLs upper limits Cercospora Genetics Leaf spot biology business.industry fungi food and beverages Pattern recognition 04 agricultural and veterinary sciences biology.organism_classification Biotechnology Support vector machine Identification (information) Radial basis function kernel 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Sugar beet Artificial intelligence business Agronomy and Crop Science 010606 plant biology & botany |
| Zdroj: | Plant Pathology. 67:399-410 |
| ISSN: | 0032-0862 |
| Popis: | Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB-image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server based on texture features using colour, intensity, and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary-class and multi-class classification approaches, i.e. the separation between diseased and non-diseased, and the differentiation among leaf diseases and non-infected tissue. The classification accuracy for the differentiation of CLS, Ramularia leaf spot, Phoma leaf spot, beet rust, and bacterial blight was 82%, better than that of sugar beet experts classifying diseases also from images. Still, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improve decision making in integrated disease control. This article is protected by copyright. All rights reserved. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text