Autonomous Irrigation Research Articles

Using a soil moisture sensor-based smart controller for autonomous irrigation management of hybrid bermudagrass with recycled water in coastal Southern California

There is a lack of information on the reliability of smart soil moisture sensor-based irrigation controllers for autonomous urban landscape irrigation management using recycled water where water conservation is required, which is the main objective of this study. A three-year turfgrass irrigation research trial (2019–2021) using tertiary-treated recycled water (mean EC = 1.18 dS/m, pH = 7.5) was conducted in Irvine, California, USA. A total of 12 irrigation treatments were designed, including six soil moisture-based thresholds (for triggering and terminating irrigation events) × two irrigation frequency treatments (restricted versus on-demand). Weekly NDVI and canopy temperature data were collected during the summer irrigation season to assess the impact of irrigation treatments on the overall health and quality of 'Tifgreen 328' hybrid bermudagrass. Soil salinity and sodium adsorption ratio (SAR) were measured before and after the summer irrigation seasons. Findings revealed statistically significant effects of irrigation levels and irrigation frequency restrictions on NDVI over the three years. The temperature difference (dT) between turfgrass and air (Tc - Ta) was significantly affected by irrigation levels throughout the three years, while irrigation frequency restrictions showed a significant effect in the years 2021 and 2022. On-demand irrigation resulted in a 10% reduction in temperature difference values during the study period compared to restricted irrigation. Variations in soil salinity levels were observed during the experimental periods; however, both salinity and SAR increased as the study progressed. Results indicate that fully autonomous hybrid bermudagrass irrigation with recycled water, based on recommended soil moisture thresholds of 75% of field capacity over an extended period in a semiarid climate, may lead to unacceptable turfgrass quality.

Smart Agrobot: An Integrated Approach to Seed Sowing, Pesticide Application

Abstract: A state-of-the-art multifunctional autonomous agricultural robotic vehicle intended to transform farming methods is presented by the suggested system. This robotic vehicle is mostly operated via Bluetooth, but it also has sophisticated navigation features like GPS and object detection sensors, which allow it to navigate precisely and cautiously over fields of crops. The autonomous irrigation system with soil sensors and intelligent seeding mechanisms for different crops are just two of the robotic vehicle's many features that make it stand out. Farmers have freedom thanks to the Bluetooth control capability, which enables remote operation and monitoring. Safety features protect workers' health and prevent equipment damage, such as emergency stop buttons and collision avoidance procedures. This robotic system also performs exceptionally well in drip irrigation management in places that receive rain, optimizing fertilizing depending on soil conditions, and controlling weeds using computer vision. The suggested method, which reduces human intervention and increases total production, is a comprehensive and intelligent approach to automated and sustainable agriculture with an emphasis on energy conservation, data logging, and analytics.

Maximizing energy via solar‐powered smart irrigation: An approach utilizing a single‐axis solar tracking mechanism

AbstractThis study presents and assesses the novelty of a cutting‐edge solar‐powered automated irrigation system that incorporates a single‐axis solar tracker. The research entails the meticulous development of a prototype, followed by comprehensive experimental scrutiny spanning 3 days, from 8:00 AM to 6:00 PM. In a unique approach, we benchmark the findings against previous research endeavours, highlighting the transformative potential of our innovative design.Our innovative system harnesses a singular‐axis solar tracking mechanism alongside moisture sensors and a water pump relay module, resulting in the creation of an autonomous irrigation system perpetually powered by solar energy. The results are noteworthy, showcasing the capability of a solar panel equipped with single‐axis tracking to significantly boost photovoltaic output power. This configuration attains a remarkable 65% increase in total output power and a substantial improvement over the modest 52%–53% performance of fixed solar panels. This substantial divergence translates to a noteworthy 12%–13% difference in efficacy, underscoring the pioneering nature of our research.The zenith of power output, ranging between 3.16 and 3.68 W, transpires from noon to 2:00 PM, further illustrating the system's viability. The integrated water pump exhibits commendable efficiency, attaining levels as high as 75%. This revelation underscores the transformative potential of automated irrigation systems endowed with single‐axis solar tracking technology, auguring amplified system performance and heralding a new era of sustainable agricultural practices.

Smart high-yield tomato cultivation: precision irrigation system using the Internet of Things.

The Internet of Things (IOT)-based smart farming promises ultrafast speeds and near real-time response. Precision farming enabled by the Internet of Things has the potential to boost efficiency and output while reducing water use. Therefore, IoT devices can aid farmers in keeping track crop health and development while also automating a variety of tasks (such as moisture level prediction, irrigation system, crop development, and nutrient levels). The IoT-based autonomous irrigation technique makes exact use of farmers' time, money, and power. High crop yields can be achieved through consistent monitoring and sensing of crops utilizing a variety of IoT sensors to inform farmers of optimal harvest times. In this paper, a smart framework for growing tomatoes is developed, with influence from IoT devices or modules. With the help of IoT modules, we can forecast soil moisture levels and fine-tune the watering schedule. To further aid farmers, a smartphone app is currently in development that will provide them with crucial data on the health of their tomato crops. Large-scale experiments validate the proposed model's ability to intelligently monitor the irrigation system, which contributes to higher tomato yields.

Development and evaluation of temperature-based deep learning models to estimate reference evapotranspiration

Efficient irrigation management of urban landscapes is critical in arid/semi-arid environments and depends on the reliable estimation of reference evapotranspiration (ETo). However, the available measured climatic data in urban areas are typically insufficient to use the standard Penman-Monteith for ETo estimation. Therefore, smart landscape irrigation controllers often use temperature-based ETo models for autonomous irrigation scheduling. This study focuses on developing deep learning temperature-based ETo models and comparing their performance with widely used empirical temperature-based models, including FAO Blaney & Criddle (BC), and Hargreaves & Samani (HS). We also developed a simple free and easy-to-access tool called DeepET for ETo estimation using the best-performing deep learning models developed in this study. Four artificial neural network (ANN) models were developed using raw weather data as inputs and the reconstructed signal obtained from the wavelet transform as inputs. In addition, long short-term memory (LSTM) recurrent neural network (NN) and one-dimensional convolution neural network (CNN) models were developed. A total of 101 active California Irrigation Management Information System (CIMIS) weather stations were selected for this study, with >725,000 data points expanding from 1985 to 2019. The performance of the models was evaluated against the standard CIMIS ETo. When evaluated at the independent sites, the temperature-based DL (Deep Learning) models showed 15–20% lower mean absolute error values than the calibrated HS model. No improvement in the performance of the ANN models was observed using reconstructed signals obtained from the wavelet transform. Our study suggests that DL models offer a promising alternative for more accurate estimations of ETo in urban areas using only temperature as input. The DeepET can be accessed from the Haghverdi Water Management Group website: http://www. ucrwater.com/software-and-tools.html.

A novel autonomous irrigation system for smart agriculture using AI and 6G enabled IoT network

Agriculture plays a vital role in the growth of humankind. The total world depends upon the agricultural contribution, and wherein the agriculture purely depends upon the necessity of irrigation. This paper develops an autonomous irrigation system using Artificial Intelligence (AI) and the 6G enabled Internet of Things (6G-IoT) transforming to create a hassle-free smart agriculture model. The developed autonomous irrigation system is purely based on prediction algorithm which is mount in a microprocessor, which uses weather history data using IoT to identify and predict rainfall patterns and any climatic changes; hence it creates an intelligent system which irrigates the field depending upon the outer environment. The developed system not only checks the environmental condition, but it also measures the moisture content of the soil to supply the required amount of water to the crops. The developed autonomous AI and 6G-IoT based smart agricultural irrigation system has been tested under control environment with an accuracy of 86.34%, sensitivity of 89.28% and precision of 91%. Hence, the proposed methodology could be the possible solution for autonomous irrigation of crops in future.

Drip Irrigation Cyber-physical System with Remote Control

In today's reality, the pace of people's lives is much higher than it was 30 years ago and it is still growing. At the same time, the amount of information is also growing. This information should be processed constantly, daily, as soon as it is received. Production volumes are not also standing still. Such a lively pace of life requires process consistency and continuity and these processes must be provided by a man.
 This article describes the system of watering which should automate the process of growing plants. Also, the analysis of a new branch, that is cyber-physical systems, is carried out. The analysis of modern systems of autonomous irrigation, principles of their construction and organization of their work is conducted. A method of implementing a system that provides the possibility of constant monitoring of the growing environment and provides an opportunity to influence it is suggested. The choice of components for system construction is made. The algorithm of the system operation is described. An analysis of the relationship between system components and the user's relationship with the system is performed.

LOW-COST IRRIGATION MANAGEMENT SYSTEM: IMPROVING DATA CONFIDENCE THROUGH ARTIFICIAL INTELLIGENCE

A common scenario in the developing countries is the low income and less education of producers. Thus, the tools used for irrigation management must be cheap and easy to handle. In this work, an autonomous and low-cost network of micro-weather stations has been developed for irrigation management. Simulations were performed to evaluate the ability of intelligent systems to compute evapotranspiration with noisy and insufficient data. The network of micro-weather stations was then applied to autonomous irrigation management of a crop of bell peppers. Statistical analysis was performed on data from the developed system and a standard weather station. The results show no statistical difference between the values of evapotranspiration calculated with data from these two sources. The developed system performed with a coefficient of determination of 0.968, mean absolute error of 0.055 mm day−1, and root mean square error of 0.063 mm day−1. The study shows that low-cost intelligent systems can be used as viable tools for efficient irrigation management.

Automatic Irrigation System with Water Pumps and Sensor in the Junin Region Peru

This project presented the design and implementation of an automatic irrigation system with water pumps and sensors, powered by a photovoltaic system, in a simulation programme (Proteus). This project is designed for a harvesting site located in the district of Huancan-Junin. The project showed that it is feasible to improve the irrigation system for people who focus on agriculture. In addition, a control system was implemented based on programming, simulations, and mathematical models. Where we can perform the respective tests for the development of the project. The results of this project will be used to improve an autonomous irrigation system with pumps and sensors. Also, it will be powered by a photovoltaic system in order to reduce the physical effort of farmers and give them a better quality of life.

Developing Turfgrass Water Response Function and Assessing Visual Quality, Soil Moisture and NDVI Dynamics of Tall Fescue Under Varying Irrigation Scenarios in Inland Southern California

Highlights A turfgrass water response function was developed for 'Westcoaster' tall fescue. The Weathermatic smart controller demonstrated reliable estimations of reference evapotranspiration. Restricting irrigation frequency did not yield water savings or lead to improvements in turfgrass quality. Abstract. Efficient landscape irrigation is an important water conservation strategy and reduces strain on limited water resources in inland Southern California. A three-year field irrigation trial was conducted in Riverside, California, to evaluate the response of 'Westcoaster' tall fescue (Festuca arundinacea Schreb.) to a wide range of autonomous irrigation scenarios implemented using a Weathermatic smart controller. The response of the turfgrass was assessed using visual rating (VR) and normalized difference vegetation index (NDVI) data collected with a handheld sensor. These experimental data were then used to develop a regression-based turfgrass water response function (TWRF), which was further employed to estimate the response of tall fescue to different irrigation levels during years with varying atmospheric evaporation demands, including extreme conditions. A strong correlation was observed between NDVI and VR (r = 0.88). A minimum NDVI threshold of 0.7 was identified to maintain an acceptable level of tall fescue quality for residential areas. During the trial periods, irrigation application rates below 100% of reference evapotranspiration (ETo) were insufficient to sustain the desired quality of tall fescue. The TWRF estimations suggested that 60% ETo can be applied for short periods (7 to 43 days, depending on the atmospheric evaporative demands) during the summer before the visual quality drops below the minimum threshold. Comparing different irrigation strategies, it was observed that on-demand irrigation, which adjusts irrigation frequency based on ETo demand, resulted in higher NDVI and VR values compared to a limited watering schedule of only a few days per week. Analysis of soil moisture data revealed that the primary root water uptake occurred mainly in the topsoil layer (<33 cm), and the applied irrigation water primarily replenished the shallow soil layer (<20 cm) due to the coarse sandy loam soil characteristics. The Weathermatic controller, equipped with on-site air temperature measurements, provided reliable ETo estimations and can be used for autonomous landscape irrigation scheduling in semiarid regions. Keywords: Keywords., Evapotranspiration, Remote sensing, Smart irrigation controller, Urban irrigation, Water conservation.

Semi-supervised machine learning analysis of crop color for autonomous irrigation

Agricultural irrigation wastage, especially from inefficient infrastructure in developing nations, is among the leading causes of the global water shortage epidemic, which affects an estimated 2.8 billion people worldwide. In this project, a three-component cloudless internet-of-things (IoT)-based irrigation network is designed and constructed to affordably integrate into existing irrigation infrastructure and approach zero water wastage while maintaining crops at a perceived healthy threshold. A Raspberry Pi-based electrical circuit of sensors and drone are constructed to collect environmental data and extract brightness-adjusted HSV crop color using k-means clustering-based computer vision algorithms. A semi-supervised approach is used to train a bidirectional Recurrent Neural Network, which enables long-term and working memory analysis within hidden layers for time-sensitive outputs. The resulting predicted irrigation volume is executed by an Arduino Nano-based irrigation micropiece to dynamically adjust the water flow to each crop for individualized irrigation. Induction from water flow through irrigation pipes generates current, yielding complete and scalable power self-sufficiency at an excess margin of 25.8%. Empirical data collection reflects a 68.8% increase in irrigation effectiveness for crop growth. Model testing on various African nation crop yields and corresponding satellite images (n = 10,301) reflects an accuracy rate of 89.1% and a binary classification success of 96.0% after a single growth cycle. The ability to cost-effectively integrate into existing irrigation infrastructure and minimize irrigation resource wastage enables the constructed network to be a viable solution to mitigate the negative impacts of water scarcity on agriculture yields.

Calculation of Hydrodynamic Parameters of the Apparatus of Shock-and-Vortex Action with a Regular Tubular Packing

An analysis of the operation of gas cleaning equipment for gas purification technological schemes, including apparatuses for carrying out a single process, combined apparatuses combining several zones for carrying out various processes, and an apparatus with autonomous irrigation circuits, has allowed developing the design of a two-stage apparatus of shock-vortex action with a regular tubular packing. As a result of the studies carried out for the apparatus’ packed zone, graphical dependences of the hydraulic resistance, the retained liquid amount and the layer’s gas content on the gas velocity and irrigation density, as well as the hydraulic resistance of the tubular bundle during the coolant movement inside the pipes, on the Reynolds number are obtained. In addition, the results of the studies of the hydraulic resistance of the gas-liquid impact zone, the inclined baffle, and the total hydraulic resistance depending on the gas velocity in the inlet branch pipe and the initial liquid level are obtained. Based on the research, a method for calculating hydrodynamic parameters is proposed, including equations for calculating the hydraulic resistance of the ejection zone, the gas flow exit zone from the central pipe, the packed zone and the apparatus as a whole, the retained liquid amount and the layer’s gas content. For this design of a tubular packing, when the coolant moves inside the pipes, equations are obtained for calculating the total hydraulic resistance of the tubular bundle, resistance due to friction, and local resistances.

IoT Powered Smart Hydroponic System for Autonomous Irrigation and Crop Monitoring

Agriculture has proven to be an integral part of human history and evolution. Traditionally, farming has been a tiresome task. It demands excessive physical effort and knowledge of geographical factors. Apart from this, the yield from the conventional method is prone to dangers such as insect attacks, rotting, and exposure to unfavorable environmental conditions. Hydroponic and IoT can prove to be an effective solution to the above shortcomings. In this article, a smart, self-monitoring, and regulatory system have been proposed. It is devised to monitor and regulate the plant growth parameters to the levels optimum for plant growth. This paper proposes a smart IoT based automatic monitoring and regulating system for spinach hydroponics. Various sensors such as EC-3190 and DHT11 are separately used to monitor parameters such as water levels, temperature, humidity, and light intensity. Wi-Fi technology has been used as a communication channel that connects the user with the hydroponic system remotely.

Integration of Mechatronic and Automation Technology in Sustainable Farming for Achieving Food Security in Kenya

Advanced farming that involves modern technology, especially in large scale, can aid in attaining food security for any given country. In this study, the prospects of automation, mechatronics and the developments for modern farming are explored for sustainable agriculture in Kenya. For the purpose of technological diversification, the use of mechatronics and automation in various smart farming technological systems is presented. It is possible to step up development in realizing food security in Kenya with the use of these modern farming techniques among other similar technologies. The use Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IOT), Global System for Mobile (GSM) Communications, photovoltaic thermal solar systems, cloud data storage and radio frequency identification (RFID) technologies that are utilized in autonomous tractors, drone farming, livestock monitoring, smart poultry, dairy, irrigation, greenhouse, and farm warehouse systems are discussed. These advances can result in significant increase in production, efficiency, profits, as well as better monitoring, surveillance and tracking in the farm. Finally, the impact of these technologies on agriculture in relation to sustainable food security is explored, where it is demonstrated that mechatronic farm automation integrated with the mobile applications can offer better farm monitoring, increase yields as well as contribute towards better land utilization.

Implementation of Novel Semi-Supervised Machine Learning Model into Autonomous Irrigation Network Optimized for Power Self-Sufficiency

Implementation of Novel Semi-Supervised Machine Learning Model into Autonomous Irrigation Network Optimized for Power Self-Sufficiency

Automatsko upravljanje sistemima za navodnjavanje

Autonomous irrigation systems have been developed to optimize water use in agricultural production and reduce human labor consumption. These systems should be able to be remotely controlled and managed any times by the farmer. The use of sensors and remote reading capabilities are needed to collect real-time data on crop condition, development phase and other parameters related to weather, crops and soil to support intelligent and efficient irrigation management systems. The sensors communicate remotely with the central control unit and the main control unit, which processes a large number of input parameters, and using complex algorithms generates an output control function: when, where and how much to irrigate. The operator must also be able to access and operate the irrigation machine in real time from anywhere and ani time. Remote wireless communication can take place in several ways: Wi-Fi, radio and GSM/GPRS. The choice of communication system depends on the topography and costs. Further development of wireless sensor applications in agriculture is needed to increase the efficiency, productivity and profitability of each agricultural operation, and thus agricultural production as a whole.

Cyberphysical system of watering with remote control

In today's reality, the pace of people's lives is much higher than it was 30 years ago and it is still growing. At the same time, the amount of information also growing. This information should to be processed constantly, daily, as soon it is received. Production volumes are also not standing still. Such a lively pace of life requires process consistency and continuity. And these processes must be provided by man. This article describes the system of watering which should automate the process of growing plants. Also the analysis of a new branch, cyber-physical systems is carried out. The analysis of modern systems of autonomous irrigation, principles of their construction and organization of their work are committed. A method of implementing a system that provides the possibility of constant monitoring of the growing environment and provides an opportunity to influence it is suggested. The choice of components for system construction is made. The algorithm of the program operation is described. An analysis of the relationship between system components and the user's relationship with the system is performed.

Design Concept for Lighting Phytosystems for Public Spaces

The article presents the idea of combining the functions of phyto and lighting design using the example of developing a design concept for a lighting system with natural flowers. Experimentally selected light sources to create sufficient lighting of public space and maintain comfortable conditions for plants, as well as an autonomous irrigation system. Examples of visualization of a lighting phytosystem are presented in the interior design of the waiting room at Platov Airport, Rostov-on-Don.

Design and Implementation of an Autonomous Irrigation System using an Open-Source Internet of Things Platform

There is much interest in an autonomous precision irrigation system that can reduce labor and water use and improve productivity in agriculture. This paper explains the design and implementation process of a water pump control for autonomous irrigation by custom scheduling using an open-source Internet of Things platform. Experimental results are presented for demonstrating the proper operation of the automatic irrigation system in an apple orchard.

Collaborative Action and Social Organization in Remote Rural Regions: Autonomous Irrigation Arrangements in the Pamirs of Tajikistan

This paper proposes a bottom–up “nexus medium” perspective to examine and understand social organization and how socio-ecological challenges in remote rural regions are dealt with in communities that receive only limited external support. While “nexus mediums” constitute substances, matter, or objects that combine manifold vital meanings and can be seen as socially constructed and materialized arenas of social interaction, autonomous resource management is seen as a means of local social organization. Taking water as the nexus medium of choice allows us to generate locally informed insights about the role of this scarce resource for the everyday life and social organization of communities inhabiting arid rural areas. This reasoning will be exemplified by three local case studies conducted during empirical research in the Pamirs of Tajikistan utilizing a mix of qualitative methods. The findings reveal how many fundamental everyday-life-related aspects and activities of the studied communities are related to water, and how these communities are organized around common water use and management arrangements that are based on joint decision-making, shared benefits and responsibilities, and collaborative action. The “nexus medium” concept appears to be an appropriate approach for research that seeks to understand from a local perspective how communal living is organized and how socio-ecological challenges are addressed.