Internet of Things (IoT) for Soil Moisture Tensiometer Automation

Abdelmoneim, Ahmed A.; Khadra, Roula; Derardja, Bilal; Dragonetti, Giovanna

doi:10.3390/mi14020263

Cited by 4 publications

(8 citation statements)

References 28 publications

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“…Figure 6 shows a high correlation between the three tested prototypes and their accompanied mechanical manometers, with an average R 2 of 0.8 while the root mean square error (RMSE) was insignificant, ranging from = 2.87 to 5.38. As the prototype was previously lab validated using bare soil pots [33], it is interesting to discuss how repeating the validation process in the open field impacted the prototype's accuracy. Compared to the lab validation, R 2 is reduced from 0.99 to 0.8, while the RMSE range increased from 0.7-1.1 Kpa in the lab to 2.87-5.38 Kpa in the open field.…”

Section: Resultsmentioning

confidence: 99%

“…Soil moisture tensiometers are one of the earliest and most widely adopted devices for measuring soil water potential. They consist of a porous cup and a vacuum gauge for measuring the equivalent negative pressure or water tension in unsaturated soils [33]. Unlike soil water content sensors, tensiometers are not sensitive to variations in soil texture [34], so they do not require prior calibration to be used for determining the matric potential at the current moment.…”

Section: Soil Moisture Based Schedulingmentioning

confidence: 99%

“…On the other hand, the second plot was irrigated on demand using the data feed from three IoT soil moisture tensiometers developed by CIHEAMs Bari digital agriculture lab [33]. They measure the soil moisture and plot it on a cloud service platform (ThingSpeak™).…”

Section: Experimental Layoutmentioning

confidence: 99%

“…Serving as the prototype's central processing unit is the ESP32-WROOM MCU, a cost-effective and potent microcontroller module fea-turing integrated WiFi and dual-mode Bluetooth capabilities. The detailed blueprints and description of the prototype can be found in [33].…”

Section: Design and Development Of The Iot Soil Moisture Tensiometer ...mentioning

confidence: 99%

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Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce

Abdelmoneim,

Khadra,

Elkamouh

et al. 2023

Sustainability

Self Cite

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Predictive weather-based models are widely used to schedule irrigation through the estimation of crop evapotranspiration. However, perceiving real-time crop water requirements remains a challenge. This research aims at field validating and exploiting a low-cost IoT soil moisture tensiometer prototype to consequently compare weather-based irrigation to soil water moisture-based irrigation in terms of yield and crop water productivity. The prototype is based on the ESP32 microcontroller and BMP180 barometric sensor. When compared to a mechanical tensiometer, the IoT prototype proved its accuracy, registering an average R2 equal to 0.8 and an RMSE range of 4.25–7.1 kPa. In a second step, the irrigation of a Romaine lettuce field (Lactuca sativa L.) cultivated under a drip system was managed according to two different scenarios: (1) using the data feed from the IoT tensiometers, irrigation was performed to keep the soil water potential between −15 and −25 kPa; (2) using the data provided by the in-situ weather station to estimate the crop water requirements. When comparing the yield, no significant difference was registered between the two scenarios. However, the water productivity was significantly higher, registering a 36.44% increment in scenario 1. The experiment highlights the water-saving potential achievable through real-time monitoring of soil moisture conditions. Since it is a low-cost device (82.20 USD), the introduced prototype facilitates deploying and managing a fleet of sensors for soil water potential live mapping.

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Section: Resultsmentioning

confidence: 99%

Section: Soil Moisture Based Schedulingmentioning

confidence: 99%

Section: Experimental Layoutmentioning

confidence: 99%

Section: Design and Development Of The Iot Soil Moisture Tensiometer ...mentioning

confidence: 99%

See 2 more Smart Citations

Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce

Abdelmoneim,

Khadra,

Elkamouh

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…The resistance soil moisture sensor is influenced by factors such as air gap, soil salinity, temperature, and bulk density, and even requires specific calibration [5]. Tensiometers have limitations such as lag and susceptibility to soil temperature and salinity, and they also require regular manual monitoring and maintenance [6]. Furthermore, these traditional methods are limited to point-scale measurements and do not provide spatially representative results, making it challenging to meet the requirements of real-time, large-scale, dynamic moisture estimation for precision agriculture.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Water Content Based on Hyperspectral Reflectance Combined with Competitive Adaptive Reweighted Sampling and Random Frog Feature Extraction and the Back-Propagation Artificial Neural Network Method

Chen

Lou

Tuo

et al. 2023

Water

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The soil water content (SWC) is a critical factor in agricultural production. To achieve real-time and nondestructive monitoring of the SWC, an experiment was conducted to measure the hyperspectral reflectance of soil samples with varying levels of water content. The soil samples were divided into two parts, SWC higher than field capacity (super-θf) and SWC lower than field capacity (sub-θf), and the outliers were detected by Monte Carlo cross-validation (MCCV). The raw spectra were processed using Savitzky–Golay (SG) smoothing and then the spectral feature variable of SWC was extracted by using a combination of competitive adaptive reweighted sampling (CARS) and random frog (Rfrog). Based on the extracted feature variables, an extreme learning machine (ELM), a back-propagation artificial neural network (BPANN), and a support vector machine (SVM) were used to establish the prediction model. The results showed that the accuracy of retrieving the SWC using the same model was poor, under two conditions, i.e., SWC above and below θf, mainly due to the influence of the lower accuracy of the super-θf part. The number of feature variables extracted by the sub-θf and super-θf datasets were 25 and 18, respectively, accounting for 1.85% and 1.33% of the raw spectra, and the variables were widely distributed in the NIR range. Among the models, the best results were achieved by the BPANN model for both the sub-θf and the super-θf datasets; the R2p, RMSEp, and RRMSE of the sub-θf samples were 0.941, 1.570%, and 6.685%, respectively. The R2p, RMSEp, and RRMSE of the super-θf samples were 0.764, 1.479%, and 4.205%, respectively. This study demonstrates that the CARS–Rfrog–BPANN method was reliable for the prediction of SWC.

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Broad Scope of Site‐Specific Crop Management and Specific Role of Remote Sensing Technologies Within It—A Review

Ali,

Hassan,

Kaul

2024

J Agronomy Crop Science

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Precision agriculture (PA) has great potential to increase agricultural productivity and profitability while reducing input costs and environmental impacts. Within PA, site‐specific crop management (SSCM) is considered the main premise, in which tillage operations and precise crop inputs (such as seed, fertiliser, water, pesticide and agrochemical) are applied according to field variability. The main aim of this review was to highlight the methods and tools used for spatial crop monitoring, soil and weather data influencing crop productivity and to support the adoption of SSCM technology. To achieve this goal: we discussed the main five components of SSCM, methods for monitoring crop and soil data, delineating field management zones (FMZs) and variable rate technologies (VRT) such as precision planting and digital smart sensors used for SSCM application. The review summarised that recent advances in plant and soil sensing systems, artificial intelligence (AI) and machine learning should be used in retrieving and analysing GIS big data for optimised crop inputs supply. Within VRT, light‐bar systems, automatic controllers and sensors are user‐friendly technologies that should be employed in SSCM solution. The authors highlight that adoption of PA can be increased through proper training and education of the farmers, and developing simple, affordable and efficient PA technologies. The review suggests five criteria that should be strictly adopted to get maximum benefits from SSCM: (i) all factors influencing crop yields can be identified; (ii) their effects on crop yields can be determined by using appropriate digital tools and crop modelling; (iii) variable rate crop inputs (VRCIs) should be calculated based on accurate information obtained from plant, soil and environment; (iv) targeted crop inputs should be exercised through global positioning system (GPS) enabled automatic controllers or wireless sensors network (WSN); and (v) right doses of crop inputs (e.g., nitrogen and irrigation) must be applied at the right time and place.

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Internet of Things (IoT) for Soil Moisture Tensiometer Automation

Cited by 4 publications

References 28 publications

Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce

Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce

Prediction of Soil Water Content Based on Hyperspectral Reflectance Combined with Competitive Adaptive Reweighted Sampling and Random Frog Feature Extraction and the Back-Propagation Artificial Neural Network Method

Broad Scope of Site‐Specific Crop Management and Specific Role of Remote Sensing Technologies Within It—A Review

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