A Deep Learning-Based Generalized System for Detecting Pine Wilt Disease Using RGB-Based UAV Images

Abstract

Pine wilt is a devastating disease that typically kills affected pine trees within a few months. In this paper, we confront the problem of detecting pine wilt disease. In the image samples that have been used for pine wilt disease detection, there is high ambiguity due to poor image resolution and the presence of “disease-like” objects. We therefore created a new dataset using large-sized orthophotographs collected from 32 cities, 167 regions, and 6121 pine wilt disease hotspots in South Korea. In our system, pine wilt disease was detected in two stages: n the first stage, the disease and hard negative samples were collected using a convolutional neural network. Because the diseased areas varied in size and color, and as the disease manifests differently from the early stage to the late stage, hard negative samples were further categorized into six different classes to simplify the complexity of the dataset. Then, in the second stage, we used an object detection model to localize the disease and “disease-like” hard negative samples. We used several image augmentation methods to boost system performance and avoid overfitting. The test process was divided into two phases: a patch-based test and a real-world test. During the patch-based test, we used the test-time augmentation method to obtain the average prediction of our system across multiple augmented samples of data, and the prediction results showed a mean average precision of 89.44% in five-fold cross validation, thus representing an increase of around 5% over the alternative system. In the real-world test, we collected 10 orthophotographs in various resolutions and areas, and our system successfully detected 711 out of 730 potential disease spots.

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

“…As an essential component of low-altitude remote sensing (Zhang et al, 2021), UAV remote sensing platforms have unique advantages for crop pest and disease monitoring, which considerably expands the scope of remote sensing use in crop monitoring (Dehkordi et al, 2020;Xu et al, 2022). Satellite remote sensing is primarily used for monitoring broad areas, but it cannot provide images with sufficient spatial resolution and the images are susceptible to weather conditions (Bendig et al, 2015;You et al, 2022). In addition, the progressive improvement of UAV technology has made feasible its combination with hyperspectral and multispectral technology for agricultural disease monitoring, providing a reference for accurate crop disease monitoring and to guide remedial management (Adao et al, 2017;Li et al, 2021).…”

Artificial Intelligence-Based Learning Approaches for Remote Sensing

“…Pine wilt is an overwhelming disease that kills a vast number of affected pine trees within a short time. In the contribution by You et al [16], the authors challenged this issue by detecting pine wilt disease. The main issue in detecting this disease is that data with low resolutions are used; therefore, there is high vagueness due to poor image resolution.…”

Pine Wilt Disease Segmentation with Deep Metric Learning Species Classification for Early-Stage Disease and Potential False Positive Identification

“…To manage PWD, quick tracking and containment is essential, which involves the removal of the infected trees through cutting, burning, or chemical and biological methods. While ground surveys have traditionally been used for detection, they require trained professionals familiar with the specific forest of interest, which makes assessments time-consuming, limited in range, and potentially costly depending on the terrain and road conditions [1,2].…”

“…Unmanned Aerial Vehicle (UAV) images have emerged as a primary method for early disease diagnosis. In this approach, a highquality orthophotograph is typically divided into smaller segments, and a conventional supervised classifier is employed to identify disease locations within a confined area [1].…”