Recalibration and performance comparison of soil moisture sensors using regression and neural network characteristic models

Author(s):  
Sagar Verma ◽  
Roop Pahuja
Soil Science ◽  
2003 ◽  
Vol 168 (6) ◽  
pp. 396-408 ◽  
Author(s):  
Brian G. Leib ◽  
Jay D. Jabro ◽  
Gary R. Matthews

2019 ◽  
Vol 12 (1) ◽  
pp. 321 ◽  
Author(s):  
Meetpal S. Kukal ◽  
Suat Irmak ◽  
Kiran Sharma

Soil moisture sensors can be effective and promising decision-making tools for diverse applications and audiences, including agricultural managers, irrigation practitioners, and researchers. Nevertheless, there exists immense adoption potential in the United States, with only 1.2 in 10 farms nationally using soil moisture sensors to decide when to irrigate. This number is much lower in the global scale. Increased adoption is likely hindered by lack of scientific support in need assessment, selection, suitability and use of these sensors. Here, through extensive field research, we address the operational feasibility of soil moisture sensors, an aspect which has been overlooked in the past, and integrate it with their performance accuracy, in order to develop a quantitative framework to guide users in the selection of best-suited sensors for varying applications. These evaluations were conducted for nine commercially available sensors under silt loam and loamy sand soils in irrigated cropland and rainfed grassland for two different installation orientations [sensing component parallel (horizontal) and perpendicular (vertical) to the ground surface] typically used. All the sensors were assessed for their aptness in terms of cost, ease of operation, convenience of telemetry, and performance accuracy. Best sensors under each soil condition, sensor orientation, and user applications (research versus agricultural production) were identified. The step-by-step guide presented here will serve as an unprecedented and holistic adoption-assisting resource and can be extended to other sensors as well.


2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.


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