KaleCare: Smart Farm for Kale with Pests Detection System using Machine Learning

Abstract

Kale is a popular ingredient in Thai cuisine and can be grown year-round. However, kale requires particular care, especially pests. Therefore, this study applies the Internet of Things to propose the KaleCare, a smart farm management system for kale with four main functions including automatic watering based on weather forecasting, automatic fertilizing, reporting, and pest detection for cutworms, and aphids. There are three processes to create the pest classification models for pest detection function. Firstly, the raw images were applied to the GrabCut to remove the background. Secondly, data augmentation was applied to generate images due to the small amount of raw data. Finally, the modified GoogLeNet reduced the original GoogLeNet structure is proposed to classify both types of pests. The experimental results show that the proposed model outperforms with 0.8903 and 0.7959 in average classification rate and 0.886 and 0.7965 in average F1-score to classify cutworm and aphid, respectively.