Recognition of Abnormal Individuals Based on Lightweight Deep Learning Using Aerial Images in Complex Forest Landscapes: A Case Study of Pine Wood Nematode

Abstract

Individuals with abnormalities are key drivers of subtle stress changes in forest ecosystems. Although remote sensing monitoring and deep learning have been developed for forest ecosystems, they are faced with the complexity of forest landscapes, multiple sources of remote sensing data, high monitoring costs, and complex terrain, which pose significant challenges to automatic identification. Therefore, taking pine nematode disease as an example, this paper proposes D-SCNet, an intelligent monitoring network for abnormal individuals applicable to UAV visible images. In this method, the convolutional block attention model and simplified dense block are introduced to enhance the semantic analysis ability of abnormal individual identification, use multi-level information of abnormal individuals well, enhance feature transfer as well as feature weights between network layers, and selectively focus on abnormal features of individuals while reducing feature redundancy and parameter and improving monitoring accuracy and efficiency. This method uses lightweight deep learning models through weak information sources to achieve rapid monitoring of a large range of abnormal individuals in complex environments. With the advantages of low cost, high efficiency, and simple data sources, it is expected to further enhance the practicality and universality of intelligent monitoring of anomalous individuals by UAV remote sensing.