The use of UAVs in monitoring yellow sigatoka in banana
The use of UAVs in monitoring yellow sigatoka in banana
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DOI：
10.1016/j.biosystemseng.2020.02.016
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发表时间：
2020-05-01
影响因子：
5.1
通讯作者：
Rabelo de Oliveira, Marcio Regys
中科院分区：
文献类型：
--
作者：
Campos Calou, Vinicius Bitencourt;Teixeira, Adunias dos Santos;Rabelo de Oliveira, Marcio Regys
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，遥感加上机器学习技术，为监测和确定疾病、虫害、水和营养紧张等具体特征模式提供了新的前景。其目的是使用高空间分辨率航空图像监测香蕉作物中黄叶斑病的侵袭程度，遵循对表型因素进行识别、分类、量化和预测的基本假设。每月使用一架无人驾驶飞行器对商业香蕉种植园进行飞行，该飞行器配备了一台1600万像素的RGB照相机（GSD为0.016781 m像素（-1））。五个分类算法被用来识别和量化的疾病，而现场评估也按照传统的方法。结果显示，2017年9月，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，与用于估计黄色sigatoka程度的传统方法相比，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图像来处理。(C)2020年IAgRE。由爱思唯尔有限公司出版。保留所有权利。
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. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.