Monitoring pests and diseases is an extremely important activity for increasing productivity in agriculture. In this scenario, remote sensing, coupled with techniques of machine learning, offer new prospects for monitoring and identifying characteristic specific patterns, such as manifestations of diseases, pests, and water and nutritional stress. The aim was to use high spatial resolution aerial images to monitor the extent of an attack of yellow sigatoka in a banana crop, following the basic assumptions of identification, classification, quantification and prediction of phenotypic factors. Monthly flights were carried out on a commercial banana plantation using an unmanned aerial vehicle, equipped with a 16-megapixel RGB camera (GSD of 0.016781 m pixel−1). Five classification algorithms were used to identify and quantify the disease while field evaluations were also made following traditional methodology. The results showed that, for September 2017, the Support Vector Machine algorithm achieved the best performance (99.28% overall accuracy and 97.13 Kappa Index), followed by the Artificial Neural Network and Minimum Distance algorithms. In quantifying the disease, the SVM algorithm was more effective than other algorithms compared to the conventional methodology used to estimate the extent of yellow sigatoka, demonstrating that the tools used for monitoring leaf spots can be handled by remote sensing, machine learning and high spatial-resolution RGB images.

中文翻译：

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无人机在香蕉黄斑病监测中的应用

监测病虫害是提高农业生产力的一项极其重要的活动。在这种情况下，遥感与机器学习技术相结合，为监测和识别特征性特定模式（例如疾病、害虫、水和营养压力的表现）提供了新的前景。目的是根据表型因素的识别、分类、量化和预测的基本假设，使用高空间分辨率航空图像来监测香蕉作物中黄斑叶斑病的侵袭程度。使用配备 16 兆像素 RGB 相机（GSD 为 0.016781 m pixel−1）的无人驾驶飞行器在商业香蕉种植园进行每月飞行。使用五种分类算法来识别和量化疾病，同时还按照传统方法进行现场评估。结果表明，2017 年 9 月，支持向量机算法取得了最佳性能（99.28% 的整体准确率和 97.13 Kappa 指数），其次是人工神经网络和最小距离算法。在量化疾病方面，与用于估计黄斑病范围的传统方法相比，SVM 算法比其他算法更有效，这表明用于监测叶斑的工具可以通过遥感、机器学习和高空间分辨率 RGB 图像。支持向量机算法实现了最佳性能（99.28% 的整体准确率和 97.13 Kappa 指数），其次是人工神经网络和最小距离算法。在量化疾病方面，与用于估计黄斑病范围的传统方法相比，SVM 算法比其他算法更有效，这表明用于监测叶斑的工具可以通过遥感、机器学习和高空间分辨率 RGB 图像。支持向量机算法实现了最佳性能（99.28% 的整体准确率和 97.13 Kappa 指数），其次是人工神经网络和最小距离算法。在量化疾病方面，与用于估计黄斑病范围的传统方法相比，SVM 算法比其他算法更有效，这表明用于监测叶斑的工具可以通过遥感、机器学习和高空间分辨率 RGB 图像。