Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images

Abstract

Urban greenery is an essential characteristic of the urban ecosystem, which offers various advantages, such as improved air quality, human health facilities, storm-water run-off control, carbon reduction, and an increase in property values. Therefore, identification and continuous monitoring of the vegetation (trees) is of vital importance for our urban lifestyle. This paper proposes a deep learning-based network, Siamese convolutional neural network (SCNN), combined with a modified brute-force-based line-of-bearing (LOB) algorithm that evaluates the health of Eucalyptus trees as healthy or unhealthy and identifies their geolocation in real time from Google Street View (GSV) and ground truth images. Our dataset represents Eucalyptus trees’ various details from multiple viewpoints, scales and different shapes to texture. The experiments were carried out in the Wyndham city council area in the state of Victoria, Australia. Our approach obtained an average accuracy of 93.2% in identifying healthy and unhealthy trees after training on around 4500 images and testing on 500 images. This study helps in identifying the Eucalyptus tree with health issues or dead trees in an automated way that can facilitate urban green management and assist the local council to make decisions about plantation and improvements in looking after trees. Overall, this study shows that even in a complex background, most healthy and unhealthy Eucalyptus trees can be detected by our deep learning algorithm in real time.

A deep semantic vegetation health monitoring platform for citizen science imaging data

“…Then the transformed image information goes through the fully connected layers and turns into a classification or prediction. CNNs have been shown to be quite effective in a wide range of applications, including object detection [ 28 ], plant segmentation [ 10 ], classification [ 29 – 33 ], plant disease identification and classification [ 34 , 35 ] and semantic segmentation [ 36 – 38 ].…”