Effect of directional augmentation using supervised machine learning technologies: A case study of strawberry powdery mildew detection

Abstract

The study extracts representative features to train a model with supervised machine learning (ML) to detect powdery mildew (Sphaerotheca macularis f. sp. fragariae) on the strawberry leaves. Powdery mildew (PM) is a fungal disease that greatly affects the production of strawberry and usually infects under conditions of warming temperatures and high humidity. In this research, we report robust models to detect PM using image processing and ML technologies. Three feature extraction techniques (histogram of oriented gradients; HOG, speeded-up robust features; SURF, and gray level co-occurrence matrix; GLCM) and two supervised ML (artificial neural network; ANN and support vector machine; SVM) were implemented using MATLAB. Images were augmented to 1016 images using a four different angle rotation technique to simulate strawberry leaf bundles in the real field. The classification accuracy (CA) to detect PM was highest at 94.34% with a combination of ANN and SURF with 908 × 908 image resolution and with SVM and GLCM at 88.98% with 908 × 908 image resolution. In terms of the extraction time for real-time processing, HOG takes the shortest time to extract features in both ANN and SVM.

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