Sugarcane Classification for On-Site Assessment Using Computer Vision

Abstract

In this paper, we present a machine intelligent system that can automatically classify sugarcane images into predefined categories. This system is developed in order to facilitate the operation in sugar manufacturing factories and can be beneficial to the sugar industry as a whole. The software system consists of the core computer vision module and other compounds, such as user interfaces and database management. To develop the core module, we apply deep learning models based on convolutional neural networks, which are currently state-of-the-art models for computer vision. The best models trained and evaluated on our sugarcane datasets achieve more than 90% multi-class accuracy in almost all settings. We have incorporated the trained model into the prototype system and successfully installed the system to test operating at one of the major sugar manufacturing facilities in the previous sugarcane harvesting season.