Detection of Basal Stem Rot Disease Using Deep Learning

Abstract

Palm oil industry is an important economic resource for Malaysia. However, an oil palm tree disease called Basal Stem Rot has impeded the production of palm oil, which caused significant economic loss at the same time. The oil palm tree disease is caused by a fungus known as Ganoderma Boninense. Infected trees often have little to no symptoms during early stage of infection, which made early detection difficult. Early disease detection is necessary to allow early sanitization and disease control efforts. Using Terrestrial Laser Scanning technology, 88 grey-distribution canopy images of oil palm tree were obtained. The images were pre-processed and augmented before being used for training and testing of the deep learning models. The capabilities of the Convolution Neural Network deep learning models in the classification of dataset into healthy and non-healthy class were tested and the best performing model was identified based on the Macro-F1 score. Fine-tuned DenseNet121 model was the best performing model, recorded a Macro F1-score of 0.798. It was also noted that Baseline model showed a relatively remarkable macro-F1 score of 0.747, which was better than all the feature extractor models and some of the fine-tuned models. However, fine-tuned models suffered from model overfitting due to dataset limitations. For future work, it is recommended to increase the sample size and utilize other CNN architectures to improve the model performance and progress towards detecting Basal Stem Rot at the early stage of infection by classifying sample images into multiple classes.

Keywords

• Basal stem rot
• convolutional neural network
• deep learning
• Ganoderma boninense
• oil palm
• terrestrial laser scanning