Classification and detection of chili and its flower using deep learning approach

Abstract

This study presents an application of using Deep Neural Network (DNN) based detector to detect chili and its flower in the chili plant image. Detecting chili on its plant is important for the development of a robotic vision to automatically picking the chili. Only one type of local chili variety is used in this study from the species of Capsicum frustecens. Five hundred of chili plant images were captured from multiple angles and each of images was marked and labelled for any present of chili and its flower. These images were divided into 70-30 per cent proportion for training with validation and testing purposes accordingly. This project uses Faster Regions with Convolutional Neural Networks (R-CNN) as a deep learning model for training that contains around 177 numbers of layers including input and output layers. The classifier network was trained to optimize all parameters involved in chili and its flower classification and detection. The classification and detection accuracy are measured on the tested images. The result shows very good accuracy in validation and testing for classification and detection especially with the image of chili plan is upright position.