Implementation of Convolutional Neural Network on Farming Robots for Detecting Broccoli

Abstract

Horticultural plants are widely cultivated in Indonesia, but failure because of the wrong cultivation technique is common. In this research, we focus on the broccoli plant because it is famous and watering is very important for this plant. To help the farmer, we try to develop the technology to control the water content and give the water to broccoli. In this case, we use object recognition to detect broccoli by using a webcam. The object detection method in the broccoli image is carried out using the Convolutional Neural Network (CNN) method with the You Only Look Once (YOLO) object recognition algorithm and the shape detection method as a detector for measuring floret area. This method is used to classify and measure floret area on broccoli plants via video or real-time webcam. From the results of the shape detection experiment, we succeed in classifying small florets which have a floret area with a range of (3479 -7787) pixels, while the area of medium florets has a range (of 14321 - 16822) pixels and large florets have a range (23316 - 36790) pixels. The average accuracy that we get from the implementation of the YOLO algorithm in this research reaches 99% accuracy.