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Development and field evaluation of a machine vision based in-season weed detection system for wild blueberry

机译:基于野生蓝莓季节杂草检测系统的机器视觉的开发与现场评价

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The wild blueberry crop requires the heavy application of agrochemicals for the proper crop yield and weed control. An integrated machine vision based weed detection system was developed to target goldenrod weed spot-specifically. Color co-occurrence matrices and statistical classifiers were used for the goldenrod detection. The linear and quadratic classifiers were developed using different reduced sub-sets of textural features. The classifiers were evaluated using accuracy, specificity, sensitivity, and false negative ratio at Laboratory scale. Performance of the developed weed detection system was also evaluated in two wild blueberry fields. The results indicated that quadratic classifier with DM-HSISD model showed the best performance at laboratory scale with the classification accuracies of 94.98% and 93.80% for training and testing datasets, respectively. Another quadratic classifier (with DM-HSI model) containing all 39 textural features was found to be the second best with the accuracies of 94.29% and 91.46% on training and test datasets, respectively. The linear classifiers didn't perform well compare to their respective quadratic counter-parts. Field performance of the developed system equipped with the quadratic DM-HSISD, classifier indicated no significant difference between variable rate (VR) and uniform application (UA) in terms of mean percentage area coverage (PAC) for the targeted goldenrod spots in both fields. However, a significant difference was observed between mean PAC of VR and UA applications for the non-targeted wild blueberry spots. The potential and actual chemical savings were in ranges between 46.71% and 74.83% and 30.12% and 60.58% depending on the weed and sprayed area, respectively. These results demonstrated that the developed weed detection system has potential for targeted application of agrochemicals to control goldenrod in wild blueberry fields.
机译:野生蓝莓作物需要沉重的农业化学品应用于适当的作物产量和杂草控制。基于集成机器视觉的杂草检测系统是为了瞄准GoldenRod杂草现货。颜色共发生矩阵和统计分类器用于GoldenRod检测。使用不同的减少的纹理特征开发线性和二次分类器。使用实验室规模的精度,特异性,灵敏度和假阴性比评估分类器。在两个野生蓝莓领域中也评估了发育杂草检测系统的性能。结果表明,具有DM-HSISD模型的二次分类器在实验室规模的最佳性能下,分别为培训和测试数据集的分类精度为94.98%和93.80%。另一个包含所有39个纹理特征的另一个二次分类器(具有DM-HSI模型)分别是第二个最佳培训和测试数据集的准确度为94.29%和91.46%。线性分类器并没有与其各自的二次反击相比顺序。在具有二次DM-HSISD的开发系统的现场性能,分类器在两个字段中针对目标GoldenRod斑点的平均百分比面积覆盖(PAC)之间的可变速率(VR)和均匀应用(UA)之间没有显着差异。然而,在非靶向野生蓝莓斑点的VR和UA应用的平均PAC之间观察到显着差异。潜力和实际化学储蓄分别在46.71%和74.83%和30.12%和60.58%之间,分别取决于杂草和喷涂区域。这些结果表明,发达的杂草检测系统具有针对农业化学物质的目标应用来控制野生蓝莓领域的Goldenrod。

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