This paper presents a practical classification system for recognising diseased wheat leaves and consists of a number of components. Pre-processing is performed to adjust the orientation of the primary leaf in the image using a Fourier Transform. A Wavelet Transform is then applied to partially remove low frequency information or background in the image. Subsequently, the dis- eased regions of the primary leaf are segmented out as blobs using Otsu’s thresholding. The disease blobs are normalised and then radially partitioned into sub-regions (using a Radial Pyramid) representing radial development of many diseases. Finally, global features are computed for different pyramid layers and combined to create a feature descriptor for training a linear SVM classifier. The system is evaluated by classifying three types of wheat leaf disease: non- diseased, Yellow Rust and Septoria. The classification accuracies are slightly over 95% and 79% for images captured under controlled and uncontrolled con- ditions, respectively.
- Wheat disease recognition
- radial pyramid
- rotation using Fourier
Siricharoen, P., Bryan, S., Morrow, P., & Parr, G. (2015). Automated Wheat Disease Classification Under Controlled and Uncontrolled Image Acquisition. In Unknown Host Publication (Vol. 9164, pp. 456-464). Springer. https://doi.org/10.1007/978-3-319-20801-5_50