| Summary: | Over the years, numerous studies have been conducted on the
integration of computer vision and machine learning in plant disease
detection. However, these conventional machine learning methods
often require the contour segmentation of the infected region from the
entire leaf region and the manual extraction of different discriminative
features before the classification models can be developed. In this
study, deep learning models, specifically, the AlexNet convolutional
neural network (CNN) and the combination of AlexNet and support
vector machine (AlexNet-SVM), which overcome the limitation
of handcrafting of feature representation were implemented for oil
palm leaf disease identification. The images of healthy and infected
leaf samples were collected, resized, and renamed before the model
training. These images were directly used to fit the classification models, without the need for segmentation and feature extraction as in
the conventional machine learning methods. The optimal architecture
of AlexNet CNN and AlexNet-SVM models were then determined
and subsequently applied for the oil palm leaf disease identification.
Comparative studies showed that the overall performance of the
AlexNet CNN model outperformed AlexNet-SVM-based classifier.
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