Discovering the <italic>Ganoderma Boninense</italic> Detection Methods Using Machine Learning: A Review of Manual, Laboratory, and Remote Approaches

Abstract

Ganoderma disease is a kind of infection that actuates oil palm death. Early detection of Ganoderma disease is the most recommended strategy for proper treatment and disease control plan to be taken promptly. In this paper, the detection methods for Ganoderma disease were reviewed and categorized accordingly. It was found that the combination of remote sensors and machine learning techniques could identify the disease up to four severity levels, including the early stage of infection. It also significantly reduced the labor and time costs compared to the traditional visual inspection and lab-based approaches. In terms of machine learning, support vector machine (SVM) using the idea of finding a hyperplane was suggested as the best classifier in several studies. Despite only one research was done on ANN and no research evaluating CNN and GAN in Ganoderma disease detection; ANN, CNN and GAN were recognized as the potential machine learning techniques that could enhance the detection system.

Keywords

• Basal stem rot
• Ganoderma%22%2C%22default_operator%22%3A%22AND%22%7D%7D%7D"> <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Ganoderma</italic>
• machine learning
• oil palm
• remote sensors