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.

一线遥感工具与机器学习（ML）结合，在作物监测和疾病监测中发挥着重要作用。作物类型分类和疾病预警系统是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，及时且具有成本效益的信息。据我们所知，大多数疾病监测系统都专注于基于单传感器的解决方案，而落后于多个信息源的集成。此外，使用无人飞行器（UAV）监视较大的景观具有挑战性，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）模型相结合，可以帮助检测和分类香蕉植物，并提供有关香蕉植物的更多信息。其整体健康状况。在这项研究中，我们通过基于像素的分类和从多层卫星图像（Sentinel 2，PlanetScope和WorldView-2）和UAV（MicaSense RedEdge）平台衍生的ML模型，在混合复杂的非洲景观下对香蕉进行了分类。我们基于随机森林（RF）模型的像素分类方法，结合了植被指数（VIs）和主成分分析（PCA）的特征，显示出高达97％的总体准确度（OA），且遗漏和委托误差小于10％（OE和CE），高分辨率多光谱图像中的Kappa系数为0.96。我们使用来自刚果民主共和国和贝宁共和国田野的UAV-RGB航拍图像来开发混合模型系统，该系统结合了目标检测模型（RetinaNet）和自定义分类器，用于同时进行香蕉定位和疾病分类。使用不同的性能指标测试了他们的准确性。我们的UAV-RGB混合模型显示，开发的对象检测和分类模型成功地对健康和病态植物进行了分类，四个类别的准确度分别为99.4％，92.8％，93.3％和90.8％：香蕉束顶病（BBTD），黄单胞菌威尔特香蕉（BXW），健康香蕉簇和单个香蕉植物。这些基于航空影像的机器学习模型的方法具有很大的潜力，可以为非洲的主要香蕉疾病提供决策支持系统。健康香蕉簇和单个香蕉植物。这些基于航空影像的机器学习模型的方法具有很大的潜力，可以为非洲的主要香蕉疾病提供决策支持系统。健康香蕉簇和单个香蕉植物。这些基于航空影像的机器学习模型的方法具有很大的潜力，可以为非洲的主要香蕉疾病提供决策支持系统。