scholarly journals Smart & Green: An Internet-of-Things Framework for Smart Irrigation

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 190 ◽  
Author(s):  
Nidia G. S. Campos ◽  
Atslands R. Rocha ◽  
Rubens Gondim ◽  
Ticiana L. Coelho da Silva ◽  
Danielo G. Gomes
Keyword(s):  
Soil Moisture ◽  
Irrigation Management ◽  
Management Plan ◽  
Matric Potential ◽  
Outlier Removal ◽  
Temporal Window ◽  
Soil Moisture Sensors ◽  
Management Scheme ◽  
Smart Agriculture ◽  
Different Sources

Irrigation is one of the most water-intensive agricultural activities in the world, which has been increasing over time. Choosing an optimal irrigation management plan depends on having available data in the monitoring field. A smart agriculture system gathers data from several sources; however, the data are not guaranteed to be free of discrepant values (i.e., outliers), which can damage the precision of irrigation management. Furthermore, data from different sources must fit into the same temporal window required for irrigation management and the data preprocessing must be dynamic and automatic to benefit users of the irrigation management plan. In this paper, we propose the Smart&Green framework to offer services for smart irrigation, such as data monitoring, preprocessing, fusion, synchronization, storage, and irrigation management enriched by the prediction of soil moisture. Outlier removal techniques allow for more precise irrigation management. For fields without soil moisture sensors, the prediction model estimates the matric potential using weather, crop, and irrigation information. We apply the predicted matric potential approach to the Van Genutchen model to determine the moisture used in an irrigation management scheme. We can save, on average, between 56.4% and 90% of the irrigation water needed by applying the Zscore, MZscore and Chauvenet outlier removal techniques to the predicted data.

scholarly journals Performance Assessment of Five Different Soil Moisture Sensors under Irrigated Field Conditions in Oklahoma

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3786 ◽  
Author(s):  
Sumon Datta ◽  
Saleh Taghvaeian ◽  
Tyson Ochsner ◽  
Daniel Moriasi ◽  
Prasanna Gowda ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Cropping Systems ◽  
Irrigation Management ◽  
Clay Content ◽  
Field Capacity ◽  
Irrigation Scheduling ◽  
Irrigated Agriculture ◽  
Undisturbed Soil ◽  
Soil Moisture Sensors ◽  
Rosetta Model

Meeting the ever-increasing global food, feed, and fiber demands while conserving the quantity and quality of limited agricultural water resources and maintaining the sustainability of irrigated agriculture requires optimizing irrigation management using advanced technologies such as soil moisture sensors. In this study, the performance of five different soil moisture sensors was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma, with variable levels of soil salinity and clay content. With factory calibrations, three of the sensors had sufficient accuracies at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. The study also investigated the performance of different approaches (laboratory, sensor-based, and the Rosetta model) to determine soil moisture thresholds required for irrigation scheduling, i.e., field capacity (FC) and wilting point (WP). The estimated FC and WP by the Rosetta model were closest to the laboratory-measured data using undisturbed soil cores, regardless of the type and number of input parameters used in the Rosetta model. The sensor-based method of ranking the readings resulted in overestimation of FC and WP. Finally, soil moisture depletion, a critical parameter in effective irrigation scheduling, was calculated by combining sensor readings and FC estimates. Ranking-based FC resulted in overestimation of soil moisture depletion, even for accurate sensors at the site with lower levels of salinity and clay.

scholarly journals Common Questions When Using Soil Moisture Sensors for Citrus and Other Fruit Trees

EDIS ◽  
2021 ◽  
Vol 2021 (2) ◽  
pp. 4
Author(s):  
Eric Herrera ◽  
Sandra M. Guzmán ◽  
Eduart Murcia
Keyword(s):  
Soil Moisture ◽  
Transmission Line ◽  
Time Domain ◽  
Irrigation Management ◽  
Fruit Trees ◽  
Fruit Tree ◽  
Biological Engineering ◽  
Soil Moisture Sensors ◽  
Tree Production ◽  
Extension Personnel

This guide is for Extension personnel who may encounter questions from growers about the functioning and accuracy of soil moisture sensors (SMSs) for fruit tree production. The 4-page publication focuses on two types of handheld sensors currently used in Florida for irrigation management of citrus and other trees: the transmission line oscillator (TLO) and time-domain transmissometer (TDT). Written by Eric Herrera, Sandra M. Guzmán, and Eduart Murcia, and published by the UF/IFAS Department of Agricultural and Biological Engineering, February 2021.

scholarly journals A smart Agriculture Irrigation System using sensor array based IOT

2021 ◽  
Vol 2062 (1) ◽  
pp. 012010
Author(s):  
Kola Murali ◽  
B. Sridhar
Keyword(s):  
Soil Moisture ◽  
Irrigation System ◽  
Sensor Data ◽  
Irrigation Systems ◽  
Soil Moisture Sensors ◽  
Agriculture Irrigation ◽  
Smart Agriculture ◽  
Intelligent Irrigation ◽  
Control Water

Abstract The role of Agriculture is important to build a nation, since more than 58% of the population in our country is dependent on agriculture that means half of the population is investing in agriculture. However, many farmers are unfamiliar with intelligent irrigation systems designed to improve the water used for their crops. The proposed system is to precisely monitor the distribution of the water to crops. This IOT based system has a distributed wireless network of soil moisture sensors to monitor soil moisture. Other sensors such as temperature, humidity, rain, IR, LDR, foot. The gateway device also processes the detector’s information and transmits the data to the farmer. An algorithm was developed using threshold values for soil moisture and nutrients, and these values were programmed into a node com-based gateway to control water for irrigation. Complete sensor data is sent to the free cloud using NODEMCU and displayed on websites and apps. This proposed work presents extensive research on irrigation systems in smart agriculture.

Smart Sensors for Smart Agriculture

2021 ◽  
pp. 175-191
Author(s):  
Puja Priya ◽  
Gurjit Kaur
Keyword(s):  
Soil Moisture ◽  
Sensor Nodes ◽  
New Technique ◽  
Smart Sensors ◽  
Internet Service ◽  
Agriculture Sector ◽  
Soil Moisture Sensors ◽  
The People ◽  
A New Technique ◽  
Smart Agriculture

Agriculture is the primitive and crucial occupation for the people. Urbanization, which indirectly affected the lives of people in the agricultural sector by increasing level of environmental pollution, climate change, degradation of soil and water quality, increasing population, decreasing income from the farming industry, etc. come as a new challenge and makes mass migration of rural people to the cities. To overcome this problem, new technologies are emerging that play a pivotal role in developing smart agriculture based on IoT technology by using smart sensors. Smart agriculture helps improve crop yield, livestock tracking, soil moisture monitoring, remote water tank level monitoring, temperature, and humidity sensing, the security of farmland, monitoring the environmental conditions, and equipment tracking. This helps farmers protect and monitor their property remotely, etc. Internet of things (IoT)-based smart sensors is the new technique for the smart agriculture system. IoT-based smart agriculture system consists of various sensor nodes placed in different locations, internet service, smart remote devices, or computer systems with the internet that monitor the operation of sensor nodes, WiFi, a camera with a microcontroller, and different interfacing sensing nodes for service. Some of the examples of such sensors are temperature sensors for temperature sensing, soil moisture sensors to check the moisture content in the soil, PIR sensors used in the detection of animals, people and other objects present in the farm field, GPS-based remote control robots that perform spraying, weeding, security, moisture sensing, etc. This chapter will have the following sequence introduction of the agriculture sector with the problems it is facing now and a new technique to overcome the current issues, need of IoT in the agriculture sector, the link of IoT technique with wireless sensor network in full detail study of IoT-based system, IoT-based applications, benefits of IoT technique in the agriculture sector, and future scope.

scholarly journals The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields

2018 ◽  
Author(s):  
Mireia Fontanet ◽  
Daniel Fernández-Garcia ◽  
Francesc Ferrer
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Spatial Scales ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Rainfall Events ◽  
Soil Moisture Sensors ◽  
Gravimetric Measurements ◽  
Agricultural Applications ◽  
Remote Sensing Techniques

Abstract. Soil moisture measurements are needed in a large number of applications such as climate change, watershed water balance and irrigation management. One of the main characteristics of this property is that soil moisture is highly variable with both space and time, hindering the estimation of a representative value. Deciding how to measure soil moisture before undertaking any type of study is therefore an important issue that needs to be addressed correctly. Nowadays, different kinds of methodologies exist for measuring soil moisture; Remote Sensing, soil moisture sensors or gravimetric measurements. This work is focused on how to measure soil moisture for irrigation scheduling, where soil moisture sensors are the main methodology for monitoring soil moisture. One of its disadvantages, however, is that soil moisture sensors measure a small volume of soil, and do not take into account the existing variability in the field. In contrast, Remote Sensing techniques are able to estimate soil moisture with a low spatial resolution, and thus it is not possible to apply these estimations to agricultural applications. In order to solve this problem, different kinds of algorithms have been developed for downscaling these estimations from low to high resolution. The DISPATCH algorithm downscales soil moisture estimations from 40 km to 1 km resolution using SMOS satellite soil moisture, NDVI and LST from MODIS sensor estimations. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in two different hydrologic scenarios; (1) when wet conditions are maintained around the field for rainfall events, and (2) when it is local irrigation that maintains wet conditions. Results show that the DISPATCH algorithm is sensitive when soil moisture is homogenized during general rainfall events, but not when local irrigation generates occasional heterogeneity. In order to explain these different behaviours, we have examined the spatial variability scales of NDVI and LST data, which are the variables involved in the downscaling process provided by the MODIS sensor. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average water content at the site, and this could be a reason for why the DISPATCH algorithm is unable to detect soil moisture increments caused by local irrigation.

scholarly journals Designing Low-Cost Capacitive-Based Soil Moisture Sensor and Smart Monitoring Unit Operated by Solar Cells for Greenhouse Irrigation Management

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5387
Author(s):  
Abdelaziz M. Okasha ◽  
Hasnaa G. Ibrahim ◽  
Adel H. Elmetwalli ◽  
Khaled Mohamed Khedher ◽  
Zaher Mundher Yaseen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Root Zone ◽  
Low Cost ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
Soil Moisture Sensors

Precise and quick estimates of soil moisture content for the purpose of irrigation scheduling are fundamentally important. They can be accomplished through the continuous monitoring of moisture content in the root zone area, which can be accomplished through automatic soil moisture sensors. Commercial soil moisture sensors are still expensive to be used by famers, particularly in developing countries, such as Egypt. This research aimed to design and calibrate a locally manufactured low-cost soil moisture sensor attached to a smart monitoring unit operated by Solar Photo Voltaic Cells (SPVC). The designed sensor was evaluated on clay textured soils in both lab and controlled greenhouse environments. The calibration results demonstrated a strong correlation between sensor readings and soil volumetric water content (θV). Higher soil moisture content was associated with decreased sensor output voltage with an average determination coefficient (R2) of 0.967 and a root-mean-square error (RMSE) of 0.014. A sensor-to-sensor variability test was performed yielding a 0.045 coefficient of variation. The results obtained from the real conditions demonstrated that the monitoring system for real-time sensing of soil moisture and environmental conditions inside the greenhouse could be a robust, accurate, and cost-effective tool for irrigation management.

scholarly journals Influence of pH and Matric Potential on Germination of Cephalosporium gramineum Conidia

Plant Disease ◽  
1998 ◽  
Vol 82 (9) ◽  
pp. 975-978 ◽  
Author(s):  
Cynthia A. Blank ◽  
Timothy D. Murray
Keyword(s):  
Soil Moisture ◽  
Soil Ph ◽  
Significant Influence ◽  
Phosphate Buffer ◽  
Mineral Salts ◽  
Matric Potential ◽  
High Soil Moisture ◽  
Soil Fungistasis ◽  
Cephalosporium Gramineum ◽  
Influence Of Ph

Germination of Cephalosporium gramineum conidia in soil was up to twofold greater at -0.064 MPa than at -0.037 and -0.007 MPa when incubated at 5°C for 2 days. Soil pH from 4.7 to 7.5 did not have a significant influence on germination of conidia and the interaction between soil pH and matric potential on germination was not significant. Soil fungistasis, which was previously observed for conidia of C. gramineum, was not observed in these studies. Germination of conidia on mineral salts agar containing phosphate buffer was significantly less at pH 4.5 than at 5.5, 6.5, or 7.5 at 5°C in one of two experiments; however, pH had no influence on germination at 10 or 20°C in two experiments. Although Cephalosporium stripe is more severe under conditions of high soil moisture and low soil pH, increased germination of conidia in response to these factors does not explain the observed increase in disease.

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