An Experimental Comparison of IoT-Based and Traditional Irrigation Scheduling on a Flood-Irrigated Subtropical Lemon Farm

Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman–Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.

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A proactive decision making framework for condition-based maintenance

Industrial Management & Data Systems ◽

10.1108/imds-03-2015-0071 ◽

2015 ◽

Vol 115 (7) ◽

pp. 1225-1250 ◽

Cited By ~ 35

Author(s):

Alexandros Bousdekis ◽

Babis Magoutas ◽

Dimitris Apostolou ◽

Gregoris Mentzas

Keyword(s):

Decision Making ◽

Decision Support ◽

Literature Review ◽

Real Time ◽

Condition Based Maintenance ◽

Sensor Data ◽

Extensive Literature ◽

Decision Space ◽

Content Type ◽

Extensive Literature Review

Purpose – The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the context of CBM. Design/methodology/approach – Starting with the manufacturing challenges and the main principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision making are analysed and their potential relevance to CBM is identified. Then, an extensive literature review of methods and techniques for the various steps of CBM is provided, especially for prognosis and decision support. Based on these, limitations and gaps are identified and a framework for proactive decision making in the context of CBM is proposed. Findings – In the proposed framework for proactive decision making, the CBM concept is enriched in the sense that it is structured into two components: the information space and the decision space. Moreover, it is extended in a way that decision space is further analyzed according to the types of recommendations that can be provided. Moreover, possible inputs and outputs of each step are identified. Practical implications – The paper provides a framework for CBM representing the steps that need to be followed for proactive recommendations as well as the types of recommendations that can be given. The framework can be used by maintenance management of a company in order to conduct CBM by utilizing real-time sensor data depending on the type of decision required. Originality/value – The results of the work presented in this paper form the basis for the development and implementation of proactive Decision Support System (DSS) in the context of maintenance.

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A Methodology for Heterogeneous Sensor Data Organization and Near Real-Time Data Sharing by Adopting OGC SWE Standards

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8040167 ◽

2019 ◽

Vol 8 (4) ◽

pp. 167 ◽

Cited By ~ 1

Author(s):

Bartolomeo Ventura ◽

Andrea Vianello ◽

Daniel Frisinghelli ◽

Mattia Rossi ◽

Roberto Monsorno ◽

...

Keyword(s):

Real Time ◽

Sensor Data ◽

Weather Data ◽

Time Data ◽

Heterogeneous Sensors ◽

Heterogeneous Datasets ◽

Real Time Data ◽

Sensor Registration ◽

Set Up ◽

Minimum Interaction

Finding a solution to collect, analyze, and share, in near real-time, data acquired by heterogeneous sensors, such as traffic, air pollution, soil moisture, or weather data, represents a great challenge. This paper describes the solution developed at Eurac Research to automatically upload data, in near real-time, by adopting Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to guarantee interoperability. We set up a methodology capable of ingesting heterogeneous datasets to automatize observation uploading and sensor registration, with minimum interaction required of the user. This solution has been successfully tested and applied in the Long Term (Socio-)Ecological Research (LT(S)ER) Matsch-Mazia initiative, and the code is accessible under the CC BY 4.0 license.

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Decision support for agricultural water management

Global NEST Journal ◽

10.30955/gnj.000887 ◽

2013 ◽

Vol 14 (3) ◽

pp. 255-263 ◽

Cited By ~ 1

Keyword(s):

Decision Support ◽

Water Use ◽

Irrigation Water ◽

Agricultural Land ◽

Crop Yields ◽

Swat Model ◽

Irrigation Scheduling ◽

Agricultural Landscapes ◽

Agricultural Water ◽

Matlab Programming

Irrigation water use is the major pressure limiting the availability of fresh water resources in the Mediterranean. Efficient irrigation scheduling programs (IRSPs) are able to reduce water consumption; however, their selection and placement in large agricultural landscapes depend on location specific characteristics and economic indicators. Towards this end, a novel and efficient Decision Support Tool (DST) is developed in MATLAB-programming, able to assess the effectiveness of different IRSPs in reducing total agricultural water use at the catchment scale along with their impact on crop yields. The DST integrates a look-up table with data on irrigation water amounts and crop yields at different locations within a catchment, populated by a hydrological and crop growth estimator: the process-based SWAT model, into a multi-objective Genetic Algorithm, which serves as the optimization engine for the allocation of measures across the agricultural land. The optimization scheme leads rapidly to the optimal trade-off frontier between the conflicting objectives providing spatial allocations of IRSPs. The tool was implemented in the Ali Efenti catchment demonstrating optimal solutions that could save more than 10% of water by reducing cotton yields less than 5% from the baseline. The study highlights the potential of the tool to assist in the development of cost-effective water saving plans at the catchment level in order to reduce the risk of desertification in intensively cultivated areas.

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A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity

Agronomy ◽

10.3390/agronomy9110686 ◽

2019 ◽

Vol 9 (11) ◽

pp. 686 ◽

Cited By ~ 7

Author(s):

Chen ◽

Qi ◽

Gui ◽

Gu ◽

Ma ◽

...

Keyword(s):

Decision Support ◽

Decision Support System ◽

Real Time ◽

Support System ◽

Deficit Irrigation ◽

Water Productivity ◽

Irrigation Scheduling ◽

Cotton Field ◽

Crop Water ◽

Significant Difference

A precisely timed irrigation schedule to match crop water demand is vital to improving water use efficiency in arid farmland. In this study, a real-time irrigation-scheduling infrastructure, Decision Support System for Irrigation Scheduling (DSSIS), based on water stresses predicted by an agro-hydrological model, was constructed and evaluated. The DSSIS employed the Root Zone Water Quality Model (RZWQM2) to predict crop water stresses and soil water content, which were used to trigger irrigation and calculate irrigation amount, respectively, along with forecasted rainfall. The new DSSIS was evaluated through a cotton field experiment in Xinjiang, China in 2016 and 2017. Three irrigation scheduling methods (DSSIS-based (D), soil moisture sensor-based (S), and conventional experience-based (E)), factorially combined with two irrigation rates (full irrigation (FI), and deficit irrigation (DI, 75% of FI)) were compared. The DSSIS significantly increased water productivity (WP) by 26% and 65.7%, compared to sensor-based and experience-based irrigation scheduling methods (p < 0.05), respectively. No significant difference was observed in WP between full and deficit irrigation treatments. In addition, the DSSIS showed economic advantage over sensor- and experience-based methods. Our results suggested that DSSIS is a promising tool for irrigation scheduling.

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Evaluation of strategies for increasing irrigation water productivity of maize in southern New South Wales using the MaizeMan model

Australian Journal of Experimental Agriculture ◽

10.1071/ea06092 ◽

2008 ◽

Vol 48 (3) ◽

pp. 304 ◽

Cited By ~ 4

Author(s):

E. Humphreys ◽

R. J. G. White ◽

D. J. Smith ◽

D. C. Godwin

Keyword(s):

Soil Water ◽

Real Time ◽

Irrigation Water ◽

New South ◽

Water Productivity ◽

New South Wales ◽

Irrigation Scheduling ◽

Weather Data ◽

South Wales ◽

Irrigation Water Productivity

MaizeMan is Windows-based decision support software, derived from CERES Maize and SWAGMAN Destiny, which can be used for real-time irrigation scheduling or strategic analysis. Evaluation of MaizeMan for sprinkler and furrow-irrigated maize (Pioneer 3153) showed good predictive ability for yield, biomass, runoff and soil water depletion between sowing and harvest. MaizeMan simulations using 43 years of weather data from Griffith, New South Wales, suggested that the biggest influence on yield, irrigation requirement and irrigation water productivity is seasonal weather conditions. For example, yield of October-sown 3153 irrigated frequently to avoid soil water deficit varied from about 8 to 16 t/ha, while net irrigation and net irrigation water productivity varied from 7 to 11 ML/ha and 0.8 to 1.6 t/ML, respectively. The optimum sowing window for maximising yield and irrigation water productivity is wide, from late September to mid November. Delaying sowing beyond this may result in higher yield and irrigation water productivity; however, delayed maturity would lead to problems for grain drying and harvesting in winter and increased insect pressure. The simplest management strategy for maximising yield and irrigation water productivity is irrigation scheduling tailored to soil type. Irrigation scheduling can be assisted by real-time scheduling using MaizeMan, provided soil hydraulic properties are accurately characterised. One to two irrigations can also be saved by growing shorter duration hybrids, but the tradeoff is lower yield, while irrigation water productivity is maintained. Simulated sprinkler irrigation increased yield and net irrigation water productivity by small amounts (averages of 0.5 t/ha and 0.2 t/ML, respectively) relative to well-scheduled flood irrigation, through improved soil water and aeration status and reduced deep drainage loss.

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LCIS DSS—An Irrigation Supporting System for Efficient Water Use in Precision Agriculture

Proceedings ◽

10.3390/proceedings2019030021 ◽

2019 ◽

Vol 30 (1) ◽

pp. 21

Author(s):

Anna Brook ◽

Keren Salinas ◽

Eugenia Monaco ◽

Antonello Bonfante

Keyword(s):

Remote Sensing ◽

Decision Support ◽

Real Time ◽

Water Use ◽

Crop Yield ◽

Low Cost ◽

Irrigation Management ◽

Imaging Spectroscopy ◽

Irrigation Scheduling ◽

Cost System

The sustainable management of water resources is one of the most important topics to face future climate change and food security. Many countries facing a serious water crisis, due to both natural and artificial causes. The efficient use of water in agriculture is one of the most significant agricultural challenges that modern technologies. These last are considered powerful management instruments able to help farmers achieve the best efficiency in irrigation water use and to increase their incomes by obtaining the highest possible crop yield. In this context, within the project “An advanced low cost system for farm irrigation support—LCIS” (a joint Italian Israeli R&D project), a fully transferable Decision Support Systems (DSS) for irrigation support, based on three different methodologies representative of the state of the art in irrigation management tools (W-Tens, in situ soil sensor; IRRISAT®, remote sensing; W-Mod, simulation modelling of water balance in the soil-plant and atmosphere system), has been developed. These three LCIS-DSS tools have been evaluated, in terms of their ability to support the farmer in irrigation management, in a real applicative case study in Italy and Israel. The main challenge of a new DSS for irrigation is attributed to the uncertain factors during the growing season such as weather uncertainty, and crop monitoring platform. For encounter this challenge, we developed during two years the LCIS, a web-based real-time DSS for irrigation scheduling using low-cost imaging spectroscopy for state estimation of the agriculture system and probabilistic short- and medium-term climate forecasts. While the majority of the existing DSS models are incorporated directly into the optimization framework, we propose to integrate continuous feedback from the field (e.g., soil moisture, crop water-stress, plant stage, LAI, and biomass) estimated based on remote sensing information. These field data will be collected by the point-based spectrometer and hyperspectral imaging system. Then a low-cost camera will be designed for specific spectral/spatial parameters (bound to the required feedbacks). The main objectives were: developing real-time Decision Support System (DSS) for optimal irrigation scheduling at farm scale for crop yield improvement, reducing irrigation cost, and water saving; developing a low-cost imaging spectroscopy framework to support the irrigation scheduling DSS above and facilitates its use in countries/places where expensive imaging spectroscopy is not available; examining the developed framework in real-life application, the framework will be calibrated evaluated using high resolution devices and tested using a low-cost system in Israel and Italy farms.

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Design, Development and Implementation of Real Time Canal and Weather Monitoring Devices

Journal of Experimental Agriculture International ◽

10.9734/jeai/2021/v43i1130752 ◽

2021 ◽

pp. 1-13

Author(s):

N. V. Gowtham Deekshithulu ◽

Joyita Mali ◽

V. Vamsee Krishna ◽

D. Surekha

Keyword(s):

Real Time ◽

Sensor Data ◽

Weather Data ◽

Design Development ◽

Wireless Data ◽

Depth Sensor ◽

Velocity Sensor ◽

Wireless Data Transmission ◽

Ir Sensors ◽

Monitoring Devices

In the present study, canal depth, velocity and weather monitoring sensors are designed and implemented in the field irrigation laboratory, Aditya Engineering College, Surampalem, Andhra Pradesh, India. The depth sensor which is used in this project is HC-SR04 sensor and the velocity sensor is YF-S403. A method of data acquisition and transmission based on ThingSpeak IOT is proposed. To record weather data (i.e., temperature, humidity, rainfall depth and wind speed) DHT11 sensor, ultrasonic sensor and IR sensors are used. The purpose of this project is to evaluate the performance of real time canal and weather monitoring devices. A structure of real time weather monitoring devices based on sensors and ThingSpeak IOT, a design was developed to realize the independent operation of sensors and wireless data transmission can help in minimizing the error in data collection. Arduino UNO is connected with canal depth and velocity sensor to generate the output, similarly NodeMCU is connected with weather monitoring device. The results revealed that observed sensor data showed good results when compared/calibrated with the existing conventional measurement system. In order to decrease the time and to get accurate value, it is recommended to consider the sensors for the proper use and to access weather data easily. The developed device worked satisfactorily with minimum or no errors.

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Towards agricultural soil carbon monitoring, reporting and verification through Field Observatory Network (FiON)

10.5194/gi-2021-21 ◽

2021 ◽

Author(s):

Olli Nevalainen ◽

Olli Niemitalo ◽

Istem Fer ◽

Antti Juntunen ◽

Tuomas Mattila ◽

...

Keyword(s):

Decision Support ◽

Carbon Sequestration ◽

Soil Carbon ◽

Real Time ◽

Agricultural Soil ◽

Weather Data ◽

Farming Practices ◽

Time Model ◽

Climate Conditions ◽

Reporting And Verification

Abstract. Better monitoring, reporting and verification (MRV) of the amount, additionality and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost-efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near real-time field measurements, weather data, satellite imagery, modeling and computing networks. FiON’s first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called Field Observatory (v1.0, fieldobservatory.org). Field Observatory is designed as an online service for near real-time model-data synthesis, forecasting and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices, and provide an MRV tool for decision-support.

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AI Driven Planetary Bot for Future Vision

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst2183120 ◽

2021 ◽

pp. 736-740

Author(s):

Seema J ◽

Kunal Kumar Gupta ◽

Challapalli Balaram ◽

Akshay Puttu Shetty ◽

Kamjula Vasudeva Reddy

Keyword(s):

Machine Learning ◽

Light Intensity ◽

Real Time ◽

Sensor Data ◽

Weather Data ◽

Time Data ◽

Model Training ◽

Sensor Temperature ◽

Future Vision ◽

Future Condition

These days focus is more on technologies like Artificial Intelligence, Machine Learning and IoT. There is lots of platforms available for IOT implementation. ESP8266 chip is among them Here the implementation is about prediction of different aspects of weather data that can be used in many ways like predicting the future condition of different region of earth or predicting future condition of different planets and their different regions. To implement this system, we need different sensors like pressure sensor humidity sensor, temperature sensor and a light intensity sensor i.e DHT11 is utilize for temperature and humidity data together and LDR. Is for light intensity. The data which is sensed by different sensors are than uploaded to Thingspeak which is an API for cloud server by the help of NodeMCU and then converted to csv format. The data can be used for monitoring the real time values too. Machine Learning Environment can be setup by the help of a CNN model. Training of model can be done by recorded values of sensor data. After recording data from sensors to NodeMCU like temperature, pressure, humidity and light intensity and after these values are sent to python environment that is Jupyter notebook for further analysis. Here the data which is used is real time data to predict the particular value and test the model.

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