Image-based Detection of Coconut Scale (aspiodotus rigidus) Infestation using Image Data Preprocessing and Augmentation Techniques and Convolutional Neural Networks - Maderazo, Andrea Marie (Quezon City University of the Philippines, 2023)
ABSTRACT

Coconut scale insect "aspiodotus rigidus" infestation is a threat to coconut trees and livelihood associated with it. Early detection and monitoring are suitable preventative pest control methods for this. The main objective of this study is to create a deep learning classification model using transfer learning with image data pre-processing and augmentation methodologies. Image pre-processing was used to increase the dataset, reduce noise and enhance the image features like bilateral filtering, dilation, morphological gradient, and normalization. Image augmentation increases the variability of the images with techniques like zooming, rotations, and flipping.

A Convolutional Neural Network architecture that uses transfer learning was created. The following classification base models were selected: Visual Geometric Group (VGG), Inception, ResNet, EfficientNet, and XCeption.

The ResNet model was the most suitable and was optimized by hyper turning the parameters using random search. The hypertuned parameters had a slightly lower performance so the initial model was selected as the final model. The coconut scale insect infestation detection model has a prediction accuracy of 0.98, precision of 0.97, and an AUC ROC score of 0.98. It should be noted that the false negative predicted by the model is associated with image data during late stages of infestation.
Keywords: Image Classification Models; Convolutional Neural Network; Transfer Learning; Image Pre-Processing.; Image Augmentation; Coconut Scale Insect Infestation