Plant Curl Disease Detection And Classification Using Active Contour And Fourier Descriptor
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 5, Pages 1088-1105
AbstractAutomatic plant leaf curl detection is an important step towards the development of Computer-aided crop damage analysis systems. It helps in analyzing the health condition of the plants through leaf images. Image processing techniques are recently being used to analyze the condition of the leaf and identify the disease that inflicted the crop. Leaf curl disease can be identified by analyzing the edges of the leaf. This paper presents a procedure to identify the curl disease occurring in plant leaves using active contour, Fourier feature descriptor, and deep learning. Active contour is used to identify the shape of the leaf. The edge contour of the leaf is then given to the Fourier feature descriptor. The feature extracted using the Fourier descriptor is invariant to the angle and size of the leaf. The same feature vector is produced in any given angle and size of the leaf in the image. The features are trained using 1D CNN. The model can then be used to classify new images and automatically identify the leaf have curl disease or not. The experimental results prove that the proposed algorithm produces good results in identifying the leaf curl disease.
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