Recognition of diseased Pinus trees in unmanned aerial vehicle (UAV) images is beneficial to the dynamic monitoring and control of Pinus tree diseases in large areas. However, the low resolution and complex backgrounds of UAV images limit the accuracy of traditional machine learning methods in recognising diseased Pinus trees. This study presents a method for recognising diseased Pinus trees that combines deep convolutional neural networks (DCNNs), deep convolutional generative adversarial networks (DCGANs), and an AdaBoost classifier. DCGANs can expand the number of samples of diseased Pinus trees to solve the problem of insufficient training samples. DCNNs are used to remove fields, soils, roads, and rocks in images to reduce the impact of complex backgrounds on target recognition. The AdaBoost classifier distinguishes diseased Pinus trees from healthy Pinus trees and identifies shadows in background removal images. Experimental results show that the proposed method has better recognition performance than K-means clustering, support vector machine, AdaBoost classifier, backpropagation neural networks, Alexnet, VGG, and Inception_v3 networks.

中文翻译：

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使用深度学习和 AdaBoost 分类器识别无人机图像中的患病松树

无人机图像中病害松树的识别有利于大面积松树病害的动态监测和控制。然而，无人机图像的低分辨率和复杂背景限制了传统机器学习方法在识别患病松树方面的准确性。本研究提出了一种识别患病松树的方法，该方法结合了深度卷积神经网络 (DCNN)、深度卷积生成对抗网络 (DCGAN) 和 AdaBoost 分类器。DCGANs 可以扩大患病松树的样本数量，解决训练样本不足的问题。DCNNs 用于去除图像中的田地、土壤、道路和岩石，以减少复杂背景对目标识别的影响。AdaBoost 分类器将患病的松树与健康的松树区分开来，并识别背景去除图像中的阴影。实验结果表明，该方法比K-means聚类、支持向量机、AdaBoost分类器、反向传播神经网络、Alexnet、VGG和Inception_v3网络具有更好的识别性能。