Detection of Ganoderma boninense diseases of palm oil trees using machine learning

• Main Authors: Yu, Hong Haw, Zhao, Zhen, Hum, Yan Chai, Chuah, Joon Huang, Voon, Wingates, Bejo, Siti Khairunniza, Husin, Nur Azuan, Por, Lip Yee, Lai, Khin Wee
• Format: Conference or Workshop Item
• Published: IEEE 2023
• Online Access: http://psasir.upm.edu.my/id/eprint/37542/; https://ieeexplore.ieee.org/document/10165368

About one-third of the world’s vegetable oil and fat supply is made up of palm oil, of which 75% is consumed as food. Palm oil is a vital economic resource for nations like Malaysia. The Basal Stem Rot disease of oil palm trees is one of many obstacles to the production of palm oil. The infection is brought on by a fungus called Ganoderma Boninense, which colonizes trees. Early detection is difficult since the symptoms of infection are sometimes mild to non-existent. Terrestrial laser scanning was used to collect 88 photos of the oil palm tree’s grey-distribution canopy. The photos gathered were pre-processed to enhance the performance of the deep learning model. To train and verify the effectiveness of disease detection, a deep learning model called convolution neural network is used. The performance of disease detection is trained and tested using a convolutional neural network deep learning model, which divides the data into two classes: the healthy class and the non-healthy class. The improved DenseNet121 model reports a Macro F1-score of 0.7983. The model could only separate the images into two classes rather than categorizing the images into distinct infection levels, which is a limitation of our work. In order to investigate the feasibility of early oil palm disease diagnosis, it is advised for future research to undertake multi-class or multi-level classification using deep learning.