Pest Detection in Plants Using Google Inception-v3 Architecture as Image Embedding

Many people need to see the pest with their own eyes to know what species the pest is to know if it could destroy the harvest or not. However, there are so many pests out there that are unknown to many people, so people need to look up the pest which cost more effort and time. Technology development in the machine learning field could help to detect pests in real time. The research method proposed in this study is the classification of pests on plants using a convolutional neural network with Google Inception-v3 architecture as image embedding. This study classifies aphids, armyworms, beetles, bollworms, grasshoppers, mites, mosquitos, sawflies, and stem borers with image input. This research also compares Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) as the final classifier for the architecture. The results of this study show that the best model can detect with 99.87% accuracy.