Identification of Mangrove Invasive Plant Derris Trifoliate Using UAV Images and Deep Learning Algorithms

Abstract

Derris trifoliate, one of the most notorious invaders of mangroves in South China, seriously threatens the growth of mangroves and the stability of local ecosystem. In order to effectively control the spread of Derris trifoliate and stabilize the mangrove ecosystem, it is necessary to identify the distribution and simulate the occurrence of Derris trifoliate in mangrove forests. While previous methods for invasive plant mapping based on satellite images were limited by low temporal and spatial resolution, the newly born unmanned aerial vehicle (UAV) images have provided a fast, fine-grained, and low-cost alternative for rapid invasive plant monitoring. However, extracting Derris trifoliate in mangrove forests based on UAV images is still a challenge. For one thing, it is difficult to collect enough data for model training, since the Derris trifoliate is hard to be distinguished in mangroves. For another thing, few existing methods can meet the requirements of high computing efficiency and low memory consumption of UAVs. Therefore, we proposed two lightweight deep learning networks based on DenseNet and VGG, namely, the lightweight DenseNet (LDN) and the lightweight VGG (LVG), and investigated the capability of LDN and LVG in identifying Derris trifoliate from mangrove forests with small amounts of data. This study has verified the effect of the lightweight deep learning algorithms for the accurate detection of mangrove invasive plants Derris trifoliate from UAV images.