Plant discrimination by Support Vector Machine classifier based on spectral reflectance
Plant discrimination by Support Vector Machine classifier based on spectral reflectance
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DOI：
10.1016/j.compag.2018.03.026
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发表时间：
2018-05-01
影响因子：
8.3
通讯作者：
Alameh, Kamal
中科院分区：
文献类型：
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作者：
Akbarzadeh, Saman;Paap, Arie;Alameh, Kamal
支持向量机 (SVM) 算法是为杂草作物区分而开发的，其准确性与基于两个不同波长下离散归一化植被指数 (NDVI) 评估的传统数据聚合方法进行了比较。专门建立了一个测试台，用于收集玉米（作为农作物）和银甜菜（作为杂草）在 635 nm、685 nm 和 785 mn、7.2 km/h 速度下的光谱反射特性。结果表明，使用高斯核 SVM 方法并结合原始反射强度或 NDVI 值作为输入，可提供比使用基于离散 NDVI 的聚合算法更好的辨别精度。在实验室条件下进行的实验结果表明，所开发的高斯支持向量机算法可以对玉米和银甜菜进行分类，玉米/银甜菜的判别准确度为 97%，而使用传统的基于 NDVI 的方法获得的最大准确度不超过 70%。
Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different wavelengths. A testbed is especially built to collect the spectral reflectance properties of corn (as a crop) and silver beet (as a weed) at 635 run, 685 nm, and 785 mn, at a speed of 7.2 km/h. Results show that the use of the Gaussian-kernel SVM method, in conjunction with either raw reflected intensities or NDVI values as inputs, provides better discrimination accuracy than that attained using the discrete NDVI-based aggregation algorithm. Experimental results carried out in laboratory conditions demonstrate that the developed Gaussian SVM algorithms can classify corn and silver beet with corn/silver-beet discrimination accuracies of 97%, whereas the maximum accuracy attained using the conventional NDVI-based method does not exceed 70%.