...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin
【24h】

Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin

机译：通过空中图像和机器学习方法检测香蕉植物及其主要疾病 - 以刚果博士和贝宁共和国为例

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Front-line remote sensing tools, coupled with machine learning (ML), have a significant role in crop monitoring and disease surveillance. Crop type classification and a disease early warning system are some of these remote sensing applications that provide precise, timely, and cost-effective information at different spatial, temporal, and spectral resolutions. To our knowledge, most disease surveillance systems focus on a single-sensor based solutions and lagging the integration of multiple information sources. Moreover, monitoring larger landscapes using unmanned aerial vehicles (UAV) are challenging, and, therefore combining high resolution satellite imagery data with advanced machine learning (ML) models through the use of mobile apps could help detect and classify banana plants and provide more information on its overall health status. In this study, we classified banana under mixed-complex African landscapes through pixel-based classifications and ML models derived from multi-level satellite images (Sentinel 2, PlanetScope and WorldView-2) and UAV (MicaSense RedEdge) platforms. Our pixel-based classification from random forest (RF) model using combined features of vegetation indices (VIs) and principal component analysis (PCA) showed up to 97% overall accuracy (OA) with less than 10% omission and commission errors (OE and CE) and Kappa coefficient of 0.96 in high resolution multispectral images. We used UAV-RGB aerial images from DR Congo and Republic of Benin fields to develop a mixed-model system combining object detection model (RetinaNet) and a custom classifier for simultaneous banana localization and disease classification. Their accuracies were tested using different performance metrics. Our UAV-RGB mixed-model revealed that the developed object detection and classification model successfully classified healthy and diseased plants with 99.4%, 92.8%, 93.3% and 90.8% accuracy for the four classes: banana bunchy top disease (BBTD), Xanthomonas Wilt of Banana (BXW), healthy banana cluster and individual banana plants, respectively. These approaches of aerial image-based ML models have high potential to provide a decision support system for major banana diseases in Africa.
机译：前线遥感工具，加上机器学习（ML），在作物监测和疾病监测中具有重要作用。作物型分类和疾病预警系统是一些这些遥感应用中的一些，可在不同的空间，时间和光谱分辨率下提供精确，及时，经济高效的信息。据我们所知，大多数疾病监控系统专注于基于单传感器的解决方案并滞留多个信息源的集成。此外，使用无人驾驶航空公司（UAV）监测较大的景观是具有挑战性的，因此通过使用移动应用程序将高分辨率卫星图像数据与高级机器学习（ML）模型组合有助于检测和分类香蕉工厂并提供更多信息它的整体健康状况。在这项研究中，我们通过基于像素的分类和来自多级卫星图像（Sentinel 2，PlanetsCope和WorldView-2）和UAV（MICASENSE REDEDGE）平台的基于像素的分类和ML型号的基于像素的分类和ML模型来分类香蕉。我们的基于像素的随机森林（RF）模型的分类，使用植被指数的组合特征（VIS）和主成分分析（PCA）显示出高达97％的总体精度（OA），省略和佣金误差低于10％（OE和CE）和Kappa系数为0.96在高分辨率多光谱图像中。我们使用了来自刚果博士和贝宁共和国领域的UAV-RGB航空图像，以开发一个混合模型系统，组合对象检测模型（RetinAnet）和定制分类器，用于同时香蕉本地化和疾病分类。使用不同的性能指标测试它们的准确性。我们的UAV-RGB混合模式显示，发达的对象检测和分类模型成功地分类为99.4％，92.8％，92.8％，93.3％和90.8％的四类精度：香蕉束顶级疾病（BBTD），Xanthomonas Wilt香蕉（BXW），健康的香蕉群和单独的香蕉植物。这些基于航空图像的ML模型的方法具有很高的潜力，以提供非洲的主要香蕉疾病的决策支持系统。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号