DEVELOPMENT OF A RESISTANCE-BASED SENSOR FOR DETECTION OF WETNESS AT THE SOIL–AIR INTERFACE
DEVELOPMENT OF A RESISTANCE-BASED SENSOR FOR DETECTION OF WETNESS AT THE SOIL–AIR INTERFACE
复制标题
开发用于检测土壤-空气界面湿度的电阻传感器
DOI：
--
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Yue Jin
中科院分区：
文献类型：
--
作者：
L. Osborne;Yue Jin
许多微生物，包括几种真菌植物病原体，往往居住在或非常接近土壤表面。这些病原体的生存、繁殖和发育受环境中水分的影响。目前没有有效的手段来连续监测土壤-空气界面处的湿度条件。已开始实施一个项目，开发一种传感器，用于连续监测土壤表面湿度，并与数据记录设备一起使用。通过在合成海绵和薄土层上进行重复试验，开发并测试了传感器的一致性和耐用性。然后进行现场试验以测试传感器的耐久性和对现场环境的响应。在温室条件下，传感器校准对触觉估计的湿度在一系列已知的水分含量的三种土壤质地（桑迪壤土，粘壤土，粉砂壤土）的薄层。在实验室测试中，对传感器的响应均匀性进行了评估。传感器被证明是统一的实验室和现场条件下的响应。它们很好地指示了田间的湿润事件，并允许确定湿润持续时间，这是植物病理学家非常感兴趣的参数。传感器与自动数据记录装置结合，可以提供潮湿持续时间的估计，以纳入疾病预测模型。
Many microbes, including several fungal plant pathogens, often reside at or very near the soil surface. Survival, reproduction, and development of these pathogens are influenced by moisture in the environment. There are currently no efficient means to continuously monitor wetness conditions at the soil-air interface. A project was initiated to develop a sensor for continuous monitoring of soil-surface wetness and to be used in conjunction with data-logging equipment. Sensors were developed and tested for consistency and durability through replicate trials conducted on synthetic sponges and on thin soil layers. Field trials were then conducted to test sensor durability and response to field environments. Under greenhouse conditions, sensors were calibrated against tactile estimates of wetness on thin layers of three soil textures (sandy loam, clay loam, and silt loam) over a range of known moisture levels. In laboratory tests, sensors were evaluated for uniformity of response. Sensors were shown to be uniform in response under laboratory and field conditions. They worked well to indicate wetting events in the field and allowed for determination of wetness duration, a parameter of great interest to plant pathologists. The sensors, in conjunction with automatic data-logging devices, may be able to provide estimates of wetness duration for incorporation into disease predictive models.