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首页> 外文期刊>Journal of computer sciences >Irrigation System Using Hyperspectral Data and Machnie Learning Techniques for Smart Agriculture
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Irrigation System Using Hyperspectral Data and Machnie Learning Techniques for Smart Agriculture

机译:使用超光谱数据和机器学习技术进行智能农业的灌溉系统

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摘要

Water is the main resource for agriculture. Management of water in agricultural field is a challenging process. To manage the water content in the agricultural field, smart irrigation system has been proposed by using fuzzy based decision support system on Hyperspectral Image benchmark dataset. Hyperspectral images are the process of collected and processed the images from electromagnetic spectrum. Recent studies show that hyperspectral images are very accurate in collecting the soil moistures value. Dataset is collected in five-day field of campaign the soil is the type of clayey slit and it is non vegetation. Hyperspectral datasets which consist of range value between 454 to 598 nm. Value is gathered from the 285 hyperspectral snapshot camera recording images with 125 spectral bands with the spectral resolution of 4 nm. Experimental results of this method achieve the accuracy of 0.98. Hence the proposed method reduces the water wastage to an extent.
机译:水是农业的主要资源。农业领域水管理是一个具有挑战性的过程。为了管理农业领域的含水量,已经通过在高光谱图像基准数据集上使用模糊的决策支持系统提出了智能灌溉系统。高光谱图像是从电磁谱收集和处理图像的过程。最近的研究表明,高光谱图像在收集土壤湿度值方面非常准确。数据集在五天的活动领域收集土壤是粘土狭缝的类型,它是非植被。高光谱数据集,其范围值在454至598nm之间。从带有125个光谱带的285高光谱快照相机录制图像收集价值,具有4nm的光谱分辨率。该方法的实验结果达到0.98的精度。因此,所提出的方法将水浪费降低到程度。

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