A decision support system for managing irrigation in agriculture

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Answer

What is smart irrigation decision support system?

Smart Irrigation Decision Support System The decision support system is the component in charge of taking the final decision on the amount of water to be irrigated, or equivalently, the number of minutes to irrigate considering constant water flow.

What is the purpose of the irrigation safety system?

The system was applied to field-scale irrigation management and aimed at assisting users in identifying safe modes of irrigation when applying low-quality water.

Is human-led irrigation management effective?

Human expertise has been proved effective to assist irrigation management but it is not scalable and available to every field, farm and crop and it is slow in the analysis of the data and real time processing.

What is the impact of cross validation on irrigation system performance?

A lower performance can be expected in comparison to what could be achieved by retraining the system with information of the plantation (scenario 1), which is sacrificed for the benefit of not having to generate manual irrigation report for new plantations. Cross validation, specifically leave-one_plantation-out is applied in validation.

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What is decision support system in agriculture?

Decision support systems (DSSs) are used in agriculture to collect and analyze data from a variety of sources with the ultimate goal of providing end users with insight into their critical decision-making process.


What are the 3 system of irrigation?

There are three commonly used methods: surface irrigation, sprinkler irrigation and drip irrigation.


What are 4 types of irrigation techniques?

The four methods of irrigation are:Surface.Sprinkler.Drip/trickle.Subsurface.


Which type of irrigation is best for agriculture?

Micro Irrigation Micro-irrigation or drip irrigation is the most efficient type of irrigation system for agriculture. Using a complex network of soaker hoses, pipes, timers, and even sensors, water is applied directly to the soil where the roots of the plants would benefit most.


What is irrigation system in agriculture?

Irrigation is the artificial application of water to the soil through various systems of tubes, pumps, and sprays. Irrigation is usually used in areas where rainfall is irregular or dry times or drought is expected. There are many types of irrigation systems, in which water is supplied to the entire field uniformly.


What is the most effective irrigation system?

Drip irrigation is the most water-efficient way to irrigate many different plantings. It is an ideal way to water in clay soils because the water is applied slowly, allowing the soil to absorb the water and avoid runoff.


What are the 5 different types of irrigation systems?

The different types of irrigation include- sprinkler irrigation, surface irrigation, drip irrigation, sub-irrigation and manual irrigation.


What are the irrigation system?

Definition of ‘irrigation system’ 1. a system of supplying (land) with water by means of artificial canals, ditches, etc, esp to promote the growth of food crops. a sophisticated irrigation system. 2. a system used to clean the stool out of the colon.


What is the importance of irrigation in agriculture?

Irrigation in arid areas of the world provides two essential agricultural requirements: (1) a moisture supply for plant growth which also transports essential nutrients; and (2) a flow of water to leach or dilute salts in the soil.


How do I choose an irrigation system?

5 Factors to Consider When Choosing an Irrigation SystemSoil type. The type of soil in an area can affect not only the type irrigation method used but also the irrigation run times. … Land topography. In particular, hilly or sloping land can be a challenge. … Local weather patterns. … Type of crops grown. … Water quality.


What are the advantages of having irrigation system in your farm?

One of the most obvious benefits of an irrigation system is protecting your yard, plants and trees from inefficient watering and drought. A well-designed system ensures that your grass and plants are getting the proper amount of water.


What is a decision support system?

A decision support system (DSS) is an interactive software-based system used to help decision-makers compile useful information from a combination of raw data, documents, and personal knowledge; to identify and solve problems; and to make an optimized decision. The DSS architecture consists of the database (or knowledge base), the model (i.e., the decision context and user criteria), and the user interface. The main advantages of using a DSS include examination of multiple alternatives, better understanding of the processes, identification of unpredicted situations, enhanced communication, cost effectiveness, and better use of data and resources. The application DSS in agriculture and environment has been rapidly increased in the past decade, which allows rapid assessment of agricultural production systems around the world and decision-making at both farm and district levels, though constraints exist for successful adoption of this technology in agriculture. One of the important applications of DSS in agriculture is water management at both field and district levels. Agriculture is facing more severe and growing competition with other sectors for freshwater. The water resources are becoming increasingly insufficient to meet the demand in developing countries and their quality is declining due to pollution and inadequate management. Irrigation is an effective means to enhance crop productions, but water needs to be supplied accurately, taking into account its availability, crop requirement and land size, irrigation systems, and crop productivity and feasibility. This chapter attempts to present the state-of-art principles, design, and application of DSS in agriculture, particularly irrigation practices, and to identify emerging approaches and future direction of research in this field.


When did irrigation DSS start?

DSS application in irrigation management applications began in the early 1990s, and currently developed countries do better in standardization of irrigation DSSs with a larger use of software for irrigation districts.


What are the criteria for irrigation scheduling?

A large number of criteria in irrigation scheduling can be found implemented in DSS. The time and amount of irrigation water can be decided by the users as functions of the following:#N#1.#N#Soil moisture: fixing a soil water content threshold or a percentage of crop available water in a fixed soil depth#N#2.#N#ET threshold: fixing an ET threshold and irrigating when the cumulative daily ETc minus effective rainfall reaches this threshold#N#3.#N#Time interval: a fixed time interval (e.g., in scheme where irrigation water is available only every n days)#N#4.#N#Phenology: supplemental irrigation at critical crop stages


What is consumptive irrigation water?

The consumptive use of irrigation water, defined as the water that is no longer available for use because it evaporated, transpired, or incorporated into crops, is transferred to the atmosphere as ET, and therefore, the spatial and temporal quantification of ET is essential in agricultural water management, at both field and district scales.


What information does the DSS provide?

The DSS usually can provide a plenty of information: from a simple alert of when–how much and where to irrigate to information about soil and plant water status, crop growth, soil water deficit, daily and seasonal water use and irrigation requirement, climatic report, and so on.


What is the primary driving force for water transpiration?

However, the same solar radiation is also the primary driving force for water transpiration. Almost all growth engines of the different crop models can be grouped into three main categories, depending on the hierarchy of processes and scales involved: (i) carbon-, (ii) solar-, and (iii) water-driven growth engines.


What is DSS used for?

The applications of DSS in agriculture have been increasing in the last decades, especially for land use planning, pest control, and fertilization. In this chapter, the major focus is on irrigation.


What are irrigation decision systems?

They improve the efficiency of crop yields, provide an appropriate use of water on the earth and so, prevent the water scarcity in some regions. In this paper, a comprehensive survey on water need models depending on crop growth and irrigation decision systems has been conducted based on machine learning and advanced control theory. The following outcomes and solutions are the main contributions. First, crop growth models and correspondingly water need models are suffer from un-modeled dynamics of the environment and lack of sensory devices. Second, irrigation decision systems based on the controller design are not fully efficient due to the imprecise crop growth models and time-varying environments. Third, water need models are depending on the inaccurate weather forecasts that also causes inefficient irrigation control. The relevant literature basis to these outcomes are surveyed in detail. Then, available and foreseen solutions are discussed and presented on a time-line. Consequently, literature review with the latest developments on water need models, irrigation decision systems, applied control methods and discussions are expected to be useful for the future strategies.


What is smart irrigation?

Knowledge-driven “smart” irrigation proposes to achieve explicitly targeted crop yield and/or irrigation water use efficiency (WUE). A coupled crop growth and soil water transport model was established and applied to schedule irrigation for drip-irrigated and film-mulched maize through numerical simulation. By designing various scenarios with either a constant or variable threshold of plant water deficit index (PWDI) to initiate irrigation, the quantitative relationship between PWDI threshold and the corresponding yield and WUE was investigated with acceptable errors between the measured and simulated values (R2 > 0.85). The model allowed determination of PWDI thresholds designed to reach specific combinations of yield and WUE to consider actual conditions such as availability and cost of water resources. Regulated deficit irrigation with a variable threshold, considering variability of physiological response to water stress, was superior to a constant PWDI threshold in improving WUE. A constant PWDI threshold of 0.54 and 45 threshold combinations among various growth stages were suggested to obtain same relative values of yield and WUE. Numerical simulation has the potential to provide reliable dynamic information regarding soil water and crop growth, necessary for smart irrigation scheduling, due to its ability in integrating the effects of environmental conditions and economic considerations and, as such, should be further studied to enhance simulation accuracy and subsequently to optimize irrigation scheduling under complex situations.


What is the seventh sense?

The authors introduce their vision of modern world understanding by the managers and engineering staff of agricultural enterprises in the era of total communications and digitalization of agriculture, referring to the “seventh sense” as a fundamental instinct for comprehension of reality and existence which places human at the centre. In the technical age, engineering marketing is crucial in terms of perception of innovation technology advantages for development of a new entrepreneurial way of thinking, especially for engineers, given their significant participation in commercial projects of agro-industrial complex. The development of integrated hybrid production in agro-industrial complex for innovative agricultural production taking into account different business lines of various enterprises of the complex is suggested to achieve synergy and build a socially-oriented system. The proposals for business development are also introduced.


What is smart agriculture?

Smart agriculture integrates a set of technologies, devices, protocols, and computational paradigms to improve agricultural processes. Big data, artificial intelligence, cloud, and edge computing provide capabilities and solutions to keep, store, and analyze the massive data generated by components. However, smart agriculture is still emerging and has a low level of security features. Future solutions will demand data availability and accuracy as key points to help farmers, and security is crucial to building robust and efficient systems. Since smart agriculture comprises a wide variety and quantity of resources, security addresses issues such as compatibility, constrained resources, and massive data. Conventional protection schemes used in the traditional Internet or Internet of Things may not be useful for agricultural systems, creating extra demands and opportunities. This paper aims at reviewing the state-of-the art of smart agriculture security, particularly in open-field agriculture, discussing its architecture, describing security issues, presenting the major challenges and future directions.


What is DSS in maize?

A Decision Support System (DSS) for efficient utilization of maize inbred line germplasm has been developed using the latest version of Drupal (7.34); Hypertext Preprocessor (PHP) was used for frontend development and database (backend) was developed in My Structured Query Language (MySql). The DSS houses a database with information on 12 traits viz. days to 50% anthesis, days to 50% silk emergence, density of spikelets, number of kernel rows, grain type, plant height, ear placement height, anthocyanin colouration at base of glume, anthocyanin colouration of anthers, anthocyanin colouration of silks, kernel row arrangement and 1000-kernel weight against each inbred lines in addition it has Image Library feature which displays ear and tassel images of the maize inbred lines. The system was designed as per the requirements and available at http://wnciimr.org. The DSS is first of its kind developed for maize in India. It helps in taking informed decision to accelerate the utilization of germplasm. It significantly reduces the duplicity of experiments to generate information on simply inherited traits and thus, enhances the scientific productivity to a great extent.


What is artificial intelligence in agriculture?

Artificial Intelligence (AI) is considered a key element to address the current challenges facing the agricultural sector related to food production and climate change. Since AI is successfully helping to optimize human processes or tasks in several sectors. In this study, we present a scientometric analysis to answer the question, what is the academic overview of the application of artificial intelligence in agriculture? We use references indexed in the Scopus, a scientometric methodology and software tools to perform the research. We identify that the countries with the highest number of publications are China, the United States, India, and Australia through document analysis. The United States is a country with more authors and institutions collaboration. The institution with the highest published number of papers was China Agricultural University, and also that Gerrit Hoogenboom, from the University of Florida, has leadership in publications. Finally, we identified that precision agriculture, smart farming, and smart sustainable agriculture refers to apply artificial intelligence and information technologies in agriculture. Also, we identify that the Internet of Things (IoT) is an emergent topic and that decision support systems and machine learning are the transversal topics.


What is agriculture 4.0?

Agriculture 4.0, as the future of farming technology, includes several key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. In this paper, we present in detail the subsystems and the architecture of an intelligent irrigation system for precision agriculture, the AREThOU5A IoT platform. We describe the operation of the IoT node that is utilized in the platform. Moreover, we apply the radiofrequency energy harvesting technique to the presented IoT platform, as an alternative technique to deliver power to the IoT node of the platform. To this end, we fabricate and validate a rectenna module for radiofrequency energy harvesting. Experimental results of the fabricated rectenna exhibit a satisfactory performance as a harvester of ambient sources in an outdoor environment.


What is a decision support system?

A decision support system (DSS) is an interactive software-based system used to help decision-makers compile useful information from a combination of raw data, documents, and personal knowledge; to identify and solve problems; and to make an optimized decision. The DSS architecture consists of the database (or knowledge base), the model (i.e., the decision context and user criteria), and the user interface. The main advantages of using a DSS include examination of multiple alternatives, better understanding of the processes, identification of unpredicted situations, enhanced communication, cost effectiveness, and better use of data and resources. The application DSS in agriculture and environment has been rapidly increased in the past decade, which allows rapid assessment of agricultural production systems around the world and decision-making at both farm and district levels, though constraints exist for successful adoption of this technology in agriculture. One of the important applications of DSS in agriculture is water management at both field and district levels. Agriculture is facing more severe and growing competition with other sectors for freshwater. The water resources are becoming increasingly insufficient to meet the demand in developing countries and their quality is declining due to pollution and inadequate management. Irrigation is an effective means to enhance crop productions, but water needs to be supplied accurately, taking into account its availability, crop requirement and land size, irrigation systems, and crop productivity and feasibility. This chapter attempts to present the state-of-art principles, design, and application of DSS in agriculture, particularly irrigation practices, and to identify emerging approaches and future direction of research in this field.


How does intelligent irrigation work?

Intelligent irrigation is one sustainable solution to reduce demands on water resources and adverse environmental impacts from irrigation. Specific case studies have quantified water savings with intelligent irrigation, however, water savings have not yet been quantified for urban agriculture or compared across climates. Before urban agriculture implements intelligent irrigation, requiring an added cost and knowledge requirements of the control system, the effects of the system must first be estimated for a broad range of climatic conditions. We hypothesized that an intelligent irrigation system will decrease water use without reducing crop yield. With CROPWAT, we modeled an urban tomato garden irrigated conventionally to one irrigated intelligently in each of the nine climatic regions of the United States. Tomatoes were selected because they are sensitive to water stress. The intelligent irrigation system included a wireless sensor network and controllable valves. In addition, we created the Conventional-Scenario Intelligent-Scenario Index to compare the overall performance of an intelligent irrigation strategy to a conventional one. Our simulations showed that the intelligent irrigation scenario decreased water use on average by 59% in all sub-humid climates while maintaining yield (0% reduction). All sub-humid climates (7 of 9 total zones) fell within the “fair” to “good” index categories. Based on these results, urban agricultural sites should consider installing intelligent irrigation systems if they are in sub-humid climates. In the two semi-arid climates, our intelligent irrigation scenario eliminated the 6–10% crop yield reductions of the conventional scenario but did not reduce water consumption. Both locations fell within the “fair” index category. The minor improvements in the semi-arid climates may not outweigh the added system costs.


What is a soil moisture sensor?

A soil moisture sensor-based variable rate irrigation (VRI) decision support system was developed and tested to quantify the potential of integrated VRI with advanced irrigation scheduling driven by soil moisture sensor data. The decision support system consists of a wireless soil moisture sensing array, a web-based user interface and a VRI-enabled center pivot irrigation system. The soil moisture sensing array was installed to monitor soil moisture within delineated irrigation management zones. At the interface, the soil moisture data were used by an irrigation scheduling tool running in the background to develop irrigation scheduling recommendations by zone. The recommendations were then downloaded to the VRI controller on the center pivot as a precision irrigation prescription.


How does Gescon work?

The work illustrates the theoretical basis, the methodological approach and the structure of a new decision support system program (GesCoN) designed for the management of N fertigation in vegetable crops. The methodological approach is based on daily water and N balance, considering the water lost by evapotranspiration and the N uptake by the crop as output and irrigation, net rainfall, N fertilization, N mineralization from soil organic matter as main inputs. The software calculates on a daily basis the availability of water and N into the root zone, and assesses when to start a fertigation event and the amount of irrigation water and N fertiliser that has to be applied in order to fulfill the water and N-crop requirements. The models used by GesCoN for the prediction of plant growth, including root apparatus geometry and its interaction with wet soil zones, N uptake as well as the approaches used for predicting N mineralization and the dynamics of N and water into the soil are also described. Water balance is done by estimating ET0 through Penman-Montheit or a calibrated Hargreaves model. ETc can be estimated by using the single or dual Kc approach. The flow-chart of the program and the basic information for its functioning are described. The application of the DSS in a processing tomato crop including its parametrization in a Souther Italy environment, are also reported.


What is DSS in agriculture?

DSS are interactive software-based systems used to compile useful information from multiple raw data sources and provide optimized solutions to support farmers in the decision-making process. The architecture of these systems usually consists of a database for data repository, a core engine for computation of crop water requirements and irrigation scheduling, and a graphical interface for farmers’ access to outputs (Rinaldi and He, 2014). The success of these systems has been gaining momentum with the development of online features and smartphone applications (Goap et al., 2018;Abi Saab et al., 2019;Nawandar and Satpute, 2019), offering continuous interaction between users and tools, increased operational flexibility, further allowing the update of data inputs in real-time. …


How is geomatics used in the Mujib Basin?

Geomatics was used in this paper to provide efficient tools to create maps and to perform all the necessary analysis about crops (computation of areas , discrimination of crop types etc.) in Mujib Basin (MB ), in a timely and cost-effective manner which can’t be delivered using the conventional surveying technology . The use of these techniques allowed the identification and the delineation of agricultural parcels and then the computation of their areas in this basin. This leads to the production of a digital crop map. The obtained crop map beside FAO and climatic data permitted the evaluation of water requirements for crops during the intended period. This study showed the efficiency of geomatics techniques to reveal the illegal use of the underground water resources in comparing with the well production in this basin and to estimate the water requirement to irrigate all parcels during the year 2015. Significant difference between well production and water requirements for the parcels irrigated from underground water is found. This proves the use of illegal water resources for example by digging wells without permission or taking water from main pipelines before metring. This metring helps the control of the consumed water from underground reservoirs.


What is cropwat model?

CROPWAT is an empirical decision support system developed by the Food and Agriculture Organization (FAO) for the calculation of crop water and irrigation requirements using user defined crop, soil, and climate data. This model has been shown to be a reliable tool to estimate the crop water demand (Aldaya and Hoekstra, 2010;Bouraima et al., 2015;Knezevic et al., 2013; Rinaldi and He, 2013; Surendran et al., 2015;Tibebe et al., 2016). CROPWAT has been used to estimate crop performance under various irrigation practices for a variety of crops and climates (Banerjee et al., 2016;Bouraima et al., 2015;Ekwue et al., 2015;Kizza et al., 2016;Laouisset and Dellal, 2016;Surendran et al., 2017). …


What are irrigation decision systems?

They improve the efficiency of crop yields, provide an appropriate use of water on the earth and so, prevent the water scarcity in some regions. In this paper, a comprehensive survey on water need models depending on crop growth and irrigation decision systems has been conducted based on machine learning and advanced control theory. The following outcomes and solutions are the main contributions. First, crop growth models and correspondingly water need models are suffer from un-modeled dynamics of the environment and lack of sensory devices. Second, irrigation decision systems based on the controller design are not fully efficient due to the imprecise crop growth models and time-varying environments. Third, water need models are depending on the inaccurate weather forecasts that also causes inefficient irrigation control. The relevant literature basis to these outcomes are surveyed in detail. Then, available and foreseen solutions are discussed and presented on a time-line. Consequently, literature review with the latest developments on water need models, irrigation decision systems, applied control methods and discussions are expected to be useful for the future strategies.


What is automatic irrigation scheduling?

Automatic irrigation scheduling systems are highly demanded in the agricultural sector due to their ability to both save water and manage deficit irrigation strategies. Elaborating a functional and efficient automatic irrigation system is a very complex task due to the high number of factors that the technician considers when managing irrigation in an optimal way. Automatic learning systems propose an alternative to traditional irrigation management by means of the automatic elaboration of predictions based on the learning of an agronomist (DSS). The aim of this paper is the study of several learning techniques in order to determine the goodness and error relative to expert decision. Nine orchards were tested during 2018 using linear regression (LR), random forest regression (RFR), and support vector regression (SVR) methods as engines of the irrigation decision support system (IDSS) proposed. The results obtained by the learning methods in three of these orchards have been compared with the decisions made by the agronomist over an entire year. The prediction model errors determined the best fitting regression model. The results obtained lead to the conclusion that these methods are valid engines to develop automatic irrigation scheduling systems.


What is IoT in agriculture?

Initially developed for smart cities which face the same challenges caused by an increasing population, Internet of Things (IoT) technologies have evolved rapidly over the last few years and are now moving successfully to agriculture. Wireless Sensor Networks (WSNs) have been reported to be used in the agri-food sector and could answer the call for a more optimized agricultural management. This paper investigates a PCB-made interdigited capacitive (IDC) soil humidity sensor as a low-price alternative to the existing ones on the market. An in-depth comparative study is performed on 30 design variations, part of them also manufactured for further investigations. By measurements and simulations, the influence of the aspect ratio and dielectric thickness on the sensitivity and capacitance of the sensor are studied. In the end, a Humidity and Temperature Measurement Wireless Equipment (HTMWE) for IoT agriculture applications is implemented with this type of sensor.

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