UAV-based weed detection in Chinese cabbage using deep learning
Pauline Ong, Kiat Soon Teo, Chee Kiong Sia
Abstract
Weeds are unwanted plants on agricultural soil. They always competing for sunlight, nutrient, space and water with economic crops. Uncontrolled weed growth can cause both significant economic and ecological loss. Hence, weeds should be efficiently differentiated from the crops for the smart spraying solution. In this study, the Convolutional Neural Network (CNN) was used to perform weed detection among the commercial crop of Chinese cabbage, using the acquired images by Unmanned Aerial Vehicles. The acquired images were pre-processed and subsequently segmented into the crop, soil, and weed classes using the Simple Linear Iterative Clustering Superpixel algorithm. The segmented images were then used to construct the CNN-based classifier. The Random Forest (RF) was applied to compare with the performance of CNN. The results showed that the CNN achieved a higher overall accuracy of 92.41% than the 86.18% attained by RF.