An Efficient Approach for Coffee Leaf Disease Classification and Severity Prediction

Abstract

Coffee is used by about two-thirds of the population in their daily life in one way or the other. However, their significance is greatly affected by the diseases that occur in the coffee leaves. This work proposes a colourbased filtering technique to determine the diseases that arise in the coffee leaves. Initially, the image obtained from the dataset is pre-processed, and contrast enhancement takes place using top–hat transform-contrast limited adaptive histogram equalization (THT-CLAHE) to enhance the visual characteristics. Next, the background present in an image is removed and the leaf is filtered depending on its colour into healthy and diseased leaves via supremum distance-fuzzy C means (SD-FCM) machine learning technique. Then, to obtain minute details, patches are extracted from the diseased leaf. Afterward, by employing double adaptive weight strategy-mexican axolotl optimization (DAWS-MAO), feature extraction is carried out followed by the selection of crucial features. For the classification of leaf diseases, the selected features are further fed into the tuned hard sigmoidal-multi-layer perceptron neural network (Tuned HS-MLPNN). In the end, based on the area of disease prediction, the severity of the disease thus classified is predicted using machine learning. The proposed Tuned HS-MLPNN method achieves the classification accuracy of 98.65%. Finally, the superiority of the proposed model is analysed with the Support Vector Machine (SVM), Naïve Bayes (NB), and artificial neural network (ANN).