Development and Automation of a Photovoltaic-Powered Soil Moisture Sensor for Water Management

IF 3.1 Q2 WATER RESOURCES Hydrology Pub Date : 2023-08-10 DOI:10.3390/hydrology10080166

Denilson Alves de Melo, P. C. Silva, Adriana Rodolfo Da Costa, J. Delmond, Ana Flávia Alves Ferreira, Johnny Alves de Souza, José Francisco de Oliveira-Júnior, Jhon Lennon Bezerra da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, P. R. Giongo, Maria Beatriz Ferreira, Abelardo Antônio de Assunção Montenegro, H. F. E. de Oliveira, Thieres George Freire da Silva, Marcos Vinícius da Silva

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引用次数: 1

Abstract

The objective of this study was to develop and calibrate a photovoltaic-powered soil moisture sensor (SMS) for irrigation management. Soil moisture readings obtained from the sensor were compared with gravimetric measurements. An automated SMS was used in two trials: (i) okra crop (Abelmoschus esculentus) and (ii) chili pepper (Capsicum frutescens). All sensors were calibrated and automated using an Arduino Mega board with C++. The soil moisture data were subjected to descriptive statistical analysis. The data recorded by the equipment was correlated with the gravimetric method. The determination coefficient (R2), Pearson correlation (r), and root mean square error (RMSE) were adopted as criteria for equipment validation. The results show that our SMS achieved an R2 value of 0.70 and an r value of 0.84. Notably, there was a striking similarity observed between SMS and gravimetric data, with RMSE values of 3.95 and 4.01, respectively. The global model developed exhibited highly efficient outcomes with R2 (0.98) and r (0.99) values. The applicability of the developed SMS facilitates irrigation management with accuracy and real-time monitoring using digital data. The automation of the SMS emerges as a real-time and precise alternative for performing irrigation at the right moment and in the correct amount, thus avoiding water losses.

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用于水管理的光伏土壤湿度传感器的开发与自动化

本研究的目的是开发和校准用于灌溉管理的光伏驱动土壤湿度传感器(SMS)。从传感器获得的土壤湿度读数与重力测量结果进行了比较。自动SMS用于两个试验:(i)秋葵作物(Abelmoschus esculentus)和(ii)辣椒(Capsicum frutescens)。所有传感器都使用Arduino Mega板和c++进行校准和自动化。土壤湿度数据进行描述性统计分析。仪器记录的数据与重量法进行了对比。采用决定系数(R2)、Pearson相关系数(r)和均方根误差(RMSE)作为设备验证的标准。结果表明，SMS的R2值为0.70,r值为0.84。值得注意的是，SMS数据与重力数据具有惊人的相似性，RMSE值分别为3.95和4.01。建立的全局模型显示出非常有效的结果，R2(0.98)和r(0.99)值。开发的SMS的适用性促进了灌溉管理的准确性和实时监测，利用数字数据。SMS的自动化成为一种实时和精确的替代方案，可以在正确的时间和正确的数量进行灌溉，从而避免水分流失。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Hydrology Earth and Planetary Sciences-Earth-Surface Processes

CiteScore

4.90

自引率

21.90%

发文量

192

审稿时长

6 weeks

期刊介绍： Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences, including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology, hydrogeology and hydrogeophysics. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, ecohydrology, geomorphology, soil science, instrumentation and remote sensing, data and information sciences, civil and environmental engineering are within scope. Social science perspectives on hydrological problems such as resource and ecological economics, sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site. Studies focused on urban hydrological issues are included.