Cucumber disease detection using adaptively regularised kernel-based fuzzy C-means and probabilistic neural network

India is an agricultural country. The major position of India depends on agriculture. But due to diseases in leaves, there is a great loss to farmers. To avoid this problem, automatic disease detection of cucumber disease is proposed. The proposed methodology consists of three modules namely, segmentation, feature extraction cucumber disease detection. Initially, the cucumber diseases are segmented using adaptively regularised kernel-based fuzzy C-means (ARKFCM). Once the disease is segmented, the colour features are extracted using hue, saturation and value (HSV) technique and texture features are extracted using grey level co-occurrence matrix (GLCM) technique. After the feature extraction process, the extracted features are given to probabilistic neural network (PNN) to recognise the image as anthracnose, downy mildew and grey mould. Finally, the experimental results demonstrate that our method is efficient and powerful to recognise the cucumber diseased image and its performance is analysed in terms of accuracy, sensitivity and specificity. Copyright © 2020 Inderscience Enterprises Ltd.