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Application of image technology to simulate optimal frequency of automatic collection of volumetric soil water content data
Agricultural Water Management ( IF 6.7 ) Pub Date : 2022-04-26 , DOI: 10.1016/j.agwat.2022.107674
Jian Wang , Xin Li , Zhenggui Zhang , Xiaofei Li , Yingchun Han , Lu Feng , Beifang Yang , Guoping Wang , Yaping Lei , Shiwu Xiong , Minghua Xin , Zhanbiao Wang , Yabing Li

Volumetric soil water content (VSWC) monitoring is an important aspect of environmental monitoring of farmland. Accurate and real-time determination of the VSWC is important for crop drought stress diagnosis and smart irrigation. Image technology is commonly used in agricultural information technology. Based on image technology, we simulated the optimal frequency of the sensors to automatically collect VSWC data, thereby solving the problems of data redundancy and data analysis difficulties in real-time monitoring. In this study, a cotton field under mulched drip irrigation in 2018 was utilised as the research subject, 5TE sensors were arranged on the soil profile of the cotton field using the "grid method", and Voxler and Surfer software was used to model the VSWC data and draw contour maps. Image processing technology (image greyscale and image similarity comparison) was employed to determine the image algorithm suitable for contour map pre-processing and the best time period for VSWC monitoring. These results indicated that the contrast-limited adaptive histogram equalisation (CLAHE) greyscale algorithm is a suitable pre-processing algorithm for processing contour maps using image processing technology, and the best 5TE sensor data monitoring time period is every 10 h. This conclusion provides a theoretical reference for VSWC monitoring and water management in production.



中文翻译:

图像技术在模拟土壤体积含水量数据自动采集最优频率中的应用

体积土壤含水量(VSWC)监测是农田环境监测的一个重要方面。VSWC的准确和实时测定对于作物干旱胁迫诊断和智能灌溉具有重要意义。图像技术是农业信息技术中常用的技术。基于图像技术,模拟传感器的最优频率,自动采集VSWC数据,解决实时监测数据冗余和数据分析难的问题。本研究以2018年覆盖滴灌的棉田为研究对象,采用“网格法”在棉田土壤剖面上布置5TE传感器,利用Voxler和Surfer软件对VSWC进行建模。数据和绘图等高线图。采用图像处理技术(图像灰度和图像相似度比较)确定适合等高线图预处理的图像算法和VSWC监测的最佳时间段。这些结果表明,对比度限制自适应直方图均衡(CLAHE)灰度算法是一种适用于利用图像处理技术处理等高线图的预处理算法,最佳的5TE传感器数据监测时间段为每10 h。该结论为生产中VSWC监测和水资源管理提供了理论参考。

更新日期:2022-04-29
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