Optimal Irrigation Scheduling Research Articles

A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning

Optimizing irrigation water usage is crucial for sustainable agriculture, especially in the context of increasing water scarcity and climate variability. Accurate estimation of evapotranspiration (ET), a key component in determining water requirements for crops, is essential for effective irrigation management. Traditional methods of measuring and estimating ET, such as eddy-covariance systems and lysimeters, provide valuable data but often face limitations in scalability, cost, and complexity. Recent advancements in machine learning (ML) offer promising alternatives to enhance the precision and efficiency of ET estimation and smart irrigation systems. This review explores the integration of machine learning techniques in optimizing irrigation water usage, with a particular focus on ET prediction and smart irrigation technologies. We examine various ML models, that have been employed to predict ET using diverse datasets comprising meteorological, soil, and remote sensing data. In addition to ET estimation, the review highlights smart irrigation systems that optimize irrigation schedules based on real-time data inputs. Through this review, we aim to provide a comprehensive overview of the state-of-the-art in ML-based ET estimation and smart irrigation technologies, contributing to the development of more resilient and efficient agricultural water management strategies.

Enhancing water usage efficiency in agriculture through smart systems: a review

Abstract As the scarcity of clean water becomes more pronounced, significant challenges emerge in efforts to uphold food security and achieve sustainable progress, particularly on a global scale. Agriculture stands out as one of the primary sectors significantly contributing to water consumption. Thus, enhancing water usage efficiency within irrigated agricultural systems holds paramount importance to ensure the continued development of agricultural sustainability. One promising avenue lies in the advancement of intelligent irrigation systems, particularly leveraging wireless communications technology, monitoring systems, and advanced control strategies to optimize irrigation scheduling. This study adopts a qualitative methodology, grounded in a thorough examination of literature adhering to specific criteria, including publications in indexed journals, articles, or research papers published in the past ten years. It introduces diverse irrigation control and observation methodologies employed in recent times. An intriguing proposition emerges regarding the integration of soil, temperature, and humidity monitoring systems with predictive control models, potentially augmenting water usage efficiency. Through this review, researchers will be able to monitor and create control prospects related to irrigation agriculture as well as identify directions and gaps for future research activities in this field.

Impacts of Climate Changes on Spatiotemporal Variation of Cotton Water Requirement and Irrigation in Tarim Basin, Central Asia.

Evapotranspiration (ETc), crop water requirement (Dcr), irrigation (IR) and irrigation leaching amount (IRlc) play a critical role in optimizing irrigation scheduling and are also important for hydrological cycle processes and ecological environment in arid regions. This research examined the spatiotemporal variability of the ETc, Dcr and IR of cotton using data from 16 meteorological stations in the Tarim basin (TRB) of arid Northwest China during 1961-2017. The results showed that the mean annual ETc of cotton exhibited a significant decreasing trend, with a change rate of 12.965 mm·10 a-1 and 18.357 mm·10 a-1 during 1961-2017 and 1961-1990, respectively. Subsequently, it experienced a substantial increase with a change rate of 16.833 mm·10 a-1 after 1990. The Dcr of cotton followed a decreasing trend at a rate of 15.531 mm·10 a-1 and 21.99 mm·10 a-1 during 1961-2017 and 1961-1990, respectively. The Dcr of cotton provided an increasing trend at a rate of 20.164 mm·10 a-1 during 1991-2017. The IR of cotton followed a decreasing trend at a rate of 19.66 mm·10 a-1 in 1961-2017 and 24.531 mm·10 a-1 in 1961-1990, but an increasing trend at a rate of 14.437 mm·10 a-1 in 1991-2017. The IRlc of cotton decreased by 2.566 mm·10 a-1 and 3.663 mm·10 a-1 during 1961-2017 and 1961-1990, respectively. After 1990, it experienced a substantial increase by 3.331 mm·10 a-1. Wind speed exerted the greatest influence on the variability in Dcr and IR between 1961 and 1990, while shine hour played a more prominent role in explaining the variability in Dcr and precipitation may have played a more significant role in explaining the variability in IR. This study is helpful for the scientific planning for agriculture, water resource allocation and water-saving irrigation in arid regions.

Transforming Irrigated Agriculture in Semi-Arid and Dry Subhumid Mediterranean Conditions: A Case of Protected Cucumber Cultivation

Pressure from population growth and climate change stress the limited water resources in the Mediterranean region and threaten food security and social stability. Enhancing food production requires the transformation of irrigation systems and enhancement of local capacity for sustainable water and soil management in irrigated agriculture. The aim of this work is the conversion of traditional irrigation practices, by introducing the practice of optimal irrigation scheduling based on local ET estimation and soil moisture monitoring, and the use of continuous feeding by fertigation to enhance both water and nutrient use efficiency. For this, two trials were established between August and November 2023 in two different pedoclimatic zones (Serein and Sultan Yacoub) of the inner Bekaa Plain of Lebanon, characterized by semi-arid and dry subhumid conditions and different soil types. Greenhouse cucumber was tested to compare the prevailing traditional farmers’ practices with the advanced, technology-based, methods of water management. Results showed a significantly higher amount of water applied by the farmers to the protected cucumber, with a potential for average saving of 105 mm of water applied in each season by improved practices. Water input in the traditional practices revealed potential stress to plants. With more than 20% increase in cucumber yield by the transformed practices, a general trend was observed in the fertilization approach and amounts, resulting in lower nutrient recovery in the farmer’s plots. The science-based practices of water and nutrient management showed higher application and agronomic water use efficiency of full fertigation, exceeding 60%, associated with double and triple higher nitrogen use efficiency, compared to those results obtained by the traditional water and fertilizer application methods. The monitored factors can contribute to severe economic and environmental consequences from nutrient buildup or leaching in the soil–groundwater system in the Mediterranean region.

Drought tolerance and recovery capacity of two ornamental shrubs: Combining physiological and biochemical analyses with online leaf water status monitoring for the application in urban settings

When plants are transferred from nursery to urban environments, they often face drought stress due to inadequate maintenance, such as insufficient irrigation. Using drought tolerant species may help mitigate the adverse impact of drought stress in urban settings. Additionally, utilizing novel technologies for water status monitoring may help optimize irrigation schedules to prevent transplanting failures. This study investigated the physiological and biochemical responses of two ornamental shrubs, Photinia x fraseri and Viburnum tinus, subjected to water stress of increasing severity and rewatering. Water relations, gas exchanges, chlorophyll fluorescence and biochemical analyses were conducted alongside real-time monitoring of water status using leaf-water-meter sensors (LWM).The progression of water stress had a notable negative impact on leaf gas exchanges and water relations in both species. Notably, P. fraseri avoided photoinhibition by reducing chlorophyll content and actual efficiency of PSII. Adjustments in leaf phenolic compounds played a significant role in enhancing drought tolerance of both species due to their antioxidant and photoprotective properties.Upon rewatering, both species exhibited complete recovery in their physiological functions, underscoring their remarkable tolerance and resilience to drought stress. Additionally, LWM sensors efficiently tracked the dehydration levels, exhibiting a rising trend during the water stress progression and a subsequent decline after rewatering for both species. These findings confirm the reliability of LWM sensors in monitoring physiological status of plants in outdoor contexts, making them a suitable tool for use in urban settings.

Influence of soil conditioners for enhanced water retention on the accuracy and longer‐term deployment of widely used soil moisture sensors

AbstractIn precision agriculture, water content sensors are fundamental to monitor soil moisture and hence optimize irrigation scheduling. Climate‐smart agriculture has also focused on improving soil moisture retention by using soil conditioners. However, despite that ‘sensor deployment’ and ‘soil conditioners’ are both intended to improve water use efficiency, data are lacking on the responses of soil water sensors to soil conditioners for enhanced water holding capacity. We evaluated soil moisture sensor readings taken in a sandy loam soil, with added biochar (2.5% w/w), compost (5% w/w), hydrogel (0.6% w/w) and water treatment residues (WTR; 5% w/w). The soils were saturated and then subjected to wetting and drying cycles. Measurements were taken continuously using multiple commercial sensors: ML3 and SM150T soil moisture sensors (Delta‐T Devices) as well as EC‐5 soil moisture sensors (Meter). The accuracy of the tested sensors was reduced by the addition of conditioners in the following order: biochar <WTR<compost <hydrogel. Sensor accuracy was highly dependent on soil water content, with larger deviations from actual values when soil water content <0.14 m3 m−3. All sensors performed best at mid values of soil water content (0.14–0.33 m3 m−3), where ML3, SM150T and EC‐5 sensor readings did not differ significantly from the actual water content in unamended soil. The presence of conditioners did affect such accuracy, but the application of a soil + conditioner − specific calibration was effective in increasing sensor accuracy in this water content range.

A Review on the Optimization of Irrigation Schedules for Farmlands Based on a Simulation–Optimization Model

Agriculture is the most important sector that is consuming water resources. In the context of global water scarcity, how to use limited water resources to improve water use efficiency in agriculture or achieve maximum crop yield and fruit quality is of great significance for ensuring food and water security. Optimizing irrigation schedules is an effective measure to improve water use efficiency, where crop models also play an important role. However, there is little research summarizing the optimization of irrigation schedules based on crop models. This study provides a systematic review on how to optimize irrigation schedules based on crop models and simulation–optimization models. When optimizing irrigation schedules based on crop models, the selected models are usually mechanistic agro-hydrological models. Irrigation scenarios and optimization objectives are mainly focused on both crop and water aspects, such as maximizing crop yield, fruit quality, water productivity, and irrigation water productivity. Minimizing crop water consumption and total irrigation amounts serve as optimization objectives, and irrigation quantity, irrigation frequency, and irrigation interval serve as decision variables. In saline areas or low fertilizer utilization areas, the optimization objectives and decision variables also involve some indicators related to salt and nitrogen, such as the maximum desalination rate, minimum salt content, fertilizer utilization efficiency, nitrogen fertilizer productivity, nitrogen fertilizer utilization efficiency, nitrogen leaching rate, which serve as the optimization objectives, and the irrigation water salinity, or fertilization schedules serve as the decision variables. When optimizing irrigation schedules based on simulation–optimization models, the models have mainly been upgraded from water-production function to crop mechanism models. In addition, optimization algorithms have been upgraded from traditional optimization techniques to intelligent optimization algorithms. Decision-making techniques are used to make decisions on optimization results. In addition, the spatial scale for the optimization problem of irrigation schedules was developed from fields to regions, and the time scale was developed from the growth stage, beginning with months, and shortening to ten days, then to a day, and then to an hour. This study also provides a detailed introduction to widely used optimization algorithms, such as genetic algorithms, as well as decision techniques. At the same time, it is proposed that the future should focus on improving crop models and analyzing uncertainty in research on irrigation schedule optimization, which is of great significance for the precise regulation of irrigation schedules.

Analysis of the Status of Irrigation Management in North Carolina

Farmers in North Carolina are turning to irrigation to reduce the impacts of droughts and rainfall variability on agricultural production. Droughts, rainfall variability, and the increasing demand for food, feed, fiber, and fuel necessitate the urgent need to provide North Carolina farmers with tools to improve irrigation management and maximize water productivity. This is only possible by understanding the current status of irrigated agriculture in the state and investigating its potential weaknesses and opportunities. Thus, the objective of this study was to perform a comprehensive analysis of the current state of irrigation management in North Carolina based on 15-year data from the Irrigation and Water Management Survey by the United States Department of Agriculture–National Agricultural Statistics Service (USDA-NASS). The results indicated a reduction in irrigation acres in the state. Also, most farms in the state have shifted to efficient sprinkler irrigation systems from gravity-fed surface irrigation systems. However, many farms in North Carolina still rely on traditional irrigation scheduling methods, such as examining crop conditions and the feel of soil in deciding when to irrigate. Hence, there are opportunities for enhancing the adoption of advanced technologies like soil moisture sensors and weather data to optimize irrigation schedules for improving water efficiency and crop production. Precision techniques and data-based solutions empower farmers to make informed, real-time decisions, optimizing water use and resource allocation to match the changing environmental conditions. The insights from this study provide valuable information for policymakers, extension services, and farmers to make informed decisions to optimize agricultural productivity and conserve water resources.

Towards a modelling, optimization and predictive control framework for smart irrigation

Smart irrigation scheduling is a promising approach for improving the efficiency and sustainability of agricultural water use, especially in arid and semi-arid lands. In this paper, we present a design and simulation of a model predictive controller for smart irrigation scheduling. The proposed controller is based on a mathematical model of the irrigation system, which is used to predict the future states of the system and determine the optimal irrigation schedule for a given crop and field. This study further evaluates the impact of the predictive control framework on crop yield, water use efficiency (WUE), and water savings in tomato production. Three control strategies, including manual control, open-loop control, and model predictive control (MPC), were compared using a Completely Randomized Design (CRD) methodology. Results indicate that MPC outperforms the other strategies, yielding the highest average crop yield of 20 t/ha and demonstrating superior WUE at 10.4 kg/m3. Additionally, MPC significantly reduces water consumption, achieving a 29 % and 8 % savings compared to manual and open-loop control, respectively. These findings underscore the efficacy of MPC in optimizing crop yield, conserving water resources, and promoting sustainable agriculture practices. The proposed controller has the potential to address the global water scarcity challenge and contribute to the sustainability of agriculture in arid and semi-arid lands.

Drainage lysimeter based measurement of water requirement and crop coefficient of bread wheat under semi-arid climate of Melkassa, Ethiopia

In view of the changing climate and growing global food demand, efficient water management is crucial for sustainable agriculture. Accurate measurement of evapotranspiration is essential for determining crop water demand and consequently for designing and managing irrigation systems. This study, conducted at Melkassa Agricultural Research Center in Ethiopia, utilized a drainage lysimeter to investigate the water requirements and crop coefficients of the Kingbird wheat variety during the December to March cropping season in 2021/22 and 2022/23. The experiment involved planting Kingbird wheat both inside and outside the lysimeter and irrigating using a watering can. Neutron probe measurement monitored the water balance in the soil. The study determined an average crop evapotranspiration of 427.28 mm and a reference evapotranspiration of 471.30 mm indicating a water requirement of 4273 m³ to fully grow wheat on a hectare of land. The derived average crop coefficient values were 0.43, 0.93, 1.15, and 0.30 for the initial, mid-season, and end growth stages, respectively. Furthermore, a fifth-order polynomial function was developed to predict crop coefficient values based on days after sowing. The findings provide valuable insights for enhancing the design and management of irrigated wheat production in the region. The specific crop coefficient values determined for different growth stages are crucial for optimizing irrigation scheduling and improving water-use efficiency, contributing to sustainable wheat production in semi-arid environment.

Modelling Soil Moisture Content with Hydrus 2D in a Continental Climate for Effective Maize Irrigation Planning

In light of climate change and limited water resources, optimizing water usage in agriculture is crucial. This study models water productivity to help regional planners address these challenges. We integrate CROPWAT-based reference evapotranspiration (ETo) with Sentinel 2 data to calculate daily evapotranspiration and water needs for maize using soil and climate data from 2021 to 2023. The HYDRUS model predicted volumetric soil moisture content, validated against observed data. A 2D hydrodynamic model within HYDRUS simulated temporal and spatial variations in soil water distribution for maize at a non-irrigated site in Hungary. The model used soil physical properties and crop evapotranspiration rates as inputs, covering crop development stages from planting to harvest. The model showed good performance, with R² values of 0.65 (10 cm) and 0.81 (60 cm) in 2021, 0.51 (10 cm) and 0.50 (60 cm) in 2022, and 0.38 (10 cm) and 0.72 (60 cm) in 2023. RMSE and NRMSE values indicated reliability. The model revealed water deficits and proposed optimal irrigation schedules to maintain soil moisture between 32.2 and 17.51 V/V%. This integrated approach offers a reliable tool for monitoring soil moisture and developing efficient irrigation systems, aiding maize production’s adaptation to climate change.

Determination of Optimal Irrigation Scheduling for Durum Wheat in the Central Highland Vertosol of Ethiopia

The field trial was conducted for three years from 2014/15 to 2016/17 to determine optimal irrigation scheduling. There were five levels of irrigation water application; 60%, 80%, 100%, 120%, and 140% of the allowable soil moisture depletion levels (ASMDL) for each of the treatments laid out in a randomized complete block design with three replications. In the study, the combined year analysis result showed that there is a significant yield difference among the irrigation water applications at a P < 0.05 level of significance. The highest yield (5.269 tone ha<sup>-1</sup>) was obtained by applying irrigation water of 80% ASMDL followed by 120% ASMDL (4.734 tone ha<sup>-1</sup>) however the least yield (4.165 tone ha<sup>-1</sup>) was observed at irrigation water application of 60% ASMDL of the recommended level which means the application of 40% less water than the FAO recommended level. There is no significant difference in water use efficiency between the treatments, but the highest water use efficiency has been observed at 80% ASMDL. The overall result of this experiment suggests that the application of irrigation water using 20% less than the FAO recommendation (100% ASMDL) can sufficiently be used for irrigation scheduling of irrigated durum wheat under central highland vertosol conditions. Therefore, to have a higher yield of irrigated durum wheat it was recommended to flush frequently before critical depletion occurred.

Evaluation and comparison of infiltration models for estimating infiltration capacity of different textures of irrigated soils

Accurate estimation of infiltration rates is crucial for effective irrigation system design and evaluation by optimizing irrigation scheduling, preventing soil erosion, and enhancing water use efficiency. This study evaluates and compares selected infiltration models for estimating water infiltration rates in the Shillanat-iv irrigation scheme in northern Ethiopia. Soil samples were collected to determine textural classes using hydrometer soil texture analysis and the United States Department of Agriculture (USDA) textural triangle. The soil textural map of the study was created using the inverse distance weight interpolation technique in ArcGIS version 10.4. Infiltration rates were measured using the double-ring infiltrometer for five soil textures: clay loam, loam, sandy clay loam, clay, and sandy loam. Six infiltration models (Kostiakov, Modified Kostiakov, Revised Modified Kostiakov, Philip, Horton, and Novel) were employed and evaluated using statistical parameters. Model calibration and validation were conducted using data from 38 points within the study area. The parameter values of the infiltration models were optimized using SPSS statistical software using least-squares errors. The results showed that, Revised Modified Kostiakov, Modified Kostiakov, and Novel infiltration models demonstrated superior capability in estimating infiltration rates for clay loam, loam, and sandy loam soil textures, respectively. Horton's model outperformed other models in estimating infiltration rates for both sandy clay loam and clay soil textures. The appropriately fitted infiltration models can be utilized in designing the irrigation system to estimate the infiltration rate of soil textures within the selected irrigation scheme and at similar sites with comparable soil textures.

Crop Monitoring System with Water Moisture Levels Using Controller

This paper presents a Crop Monitoring System (CMS) designed to effectively manage crop water moisture levels through automated irrigation control. The system incorporates soil moisture sensors and environmental sensors for real-time data collection, which is then processed by a controller to determine optimal irrigation schedules and water application rates. By integrating predefined thresholds and algorithms, the controller ensures precise irrigation tailored to specific crop and soil conditions, thus preventing both under- watering and over-watering. Additionally, the CMS features wireless communication capabilities, enabling remote monitoring and adjustment of irrigation settings by farmers. This remote accessibility facilitates timely interventions and optimizations, even in the absence of farmers in the fields. Overall, the CMS enhances crop health, improves resource efficiency, and conserves water in agriculture. By leveraging advanced sensor technologies and intelligent control algorithms, this system offers a sustainable solution for precision agriculture, contributing to increased yields while minimizing water usage and environmental impact. This project intends to make farming as mechanized and labor-free as possible.

Can the allometric relationship between saturated water accumulation and dry mass be used to diagnose the water status of winter wheat?

ContextAgriculture is the major consumer of fresh water globally. However, climate change-induced water scarcity is challenging the freshwater resources to meet global crop water demands. Consequently, utilizing limited water resources efficiently is increasingly important in agriculture. Accurate diagnosis of crop water status and in-season demand is imperative for precision irrigation, as it allows growers to make informed decisions about fine-tuning in-season irrigation scheduling. Plant-based diagnostic approaches such as the critical saturated water accumulation (SWA) curve have been employed to diagnose the in-season crop water status in maize. However, it remains uncertain if this indicator applies to other crops including wheat. ObjectiveThe study aimed to develop and validate a model for quantifying the in-season water status of winter wheat using SWA curves and water diagnosis index (WDI). MethodsA four-year study was carried out by employing four water and two nitrogen rate treatments from 2019 to 2023 to establish the relationship between dry mass (DM) and SWA during the vegetative growth period of winter wheat. The effects of different water and nitrogen conditions on DM-SWA allometry were also analyzed. Besides, WDI was also derived as the ratio of the observed SWA value to the critical SWA value. ResultsThe allometric relationships between SWA and DM were used to construct critical SWA curves under sub-optimal and optimal nitrogen nutrition (N1: SWA = 5.34DM0.77; N2: SWA = 7.37DM0.83). The decrease in plant stem ratio and indirect soil nitrogen deficiency caused by water stress were the two main factors contributing to the decrease in plant WDI. However, uncertainties of observation and input data, model assumptions, model structure, and parameters led to a high degree of uncertainty in WDI, which affected its practical application. ConclusionsThe allometric relationships between SWA and DM can potentially be used as a diagnostic tool for assessing crop water status. Nitrogen deficiency would reduce the SWA without affecting its accumulation rate. Further research is required to employ WDI for plant water status diagnosis under field conditions. Significance/ImplicationsThe findings would assist in developing precision irrigation indicators for monitoring in-season plant water status, optimizing irrigation scheduling, selecting water-efficient cultivars, and enhancing wheat productivity.

Edge compute algorithm enabled localized crop physiology sensing system for apple (Malus domestica Borkh.) crop water stress monitoring

Elevated air temperature (>35 ℃) combined with intense solar radiation can cause heat stress related damage to apple (Malus domestica Borkh.) fruits (e.g., sunburn) and increase tree evapotranspiration demand. Current heat stress mitigation techniques (e.g., evaporative cooling and netting) may protect fruits but can skew the tree evapotranspiration rates, preventing precision under-tree irrigation. A detailed understanding of heat stress mitigation techniques on tree fruit water status is critical for optimized irrigation scheduling and reduced crop losses. This study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management. The CPSS is capable of acquiring thermal infrared and RGB images of the scene at predetermined interval. In this study, the edge compute algorithm on CPSS was amended to estimate crop water stress index (CWSI). Developed algorithm was validated for its accuracy in predicting the crop water stress under four different heat stress mitigation techniques namely: conventional overhead sprinklers, foggers, netting, and combinations of foggers and netting. A CPSS node was deployed in each treatment for acquiring thermal infrared and RGB images. Acquired imagery data were used to estimate CWSI using the modified algorithm. The algorithm-estimated CWSI showed significant negative correlation with stem water potential measurements (r = -0.8, p < 0.01). The heat stress mitigation techniques had varying effects on sensitivity of estimated CWSI. Algorithm estimated CWSI was most sensitive to changes in water stress under fogging (r = 0.76) and least sensitive under neeting (r = -0.65). Overall, the use of real-time CWSI estimates in conjunction with heat stress monitoring could help improve precision irrigation management, enabling timely actuation of the under tree drip irrigation in apple orchards.

Smart Irrigation with Intrusion Detection

This paper proposes a comprehensive approach to smart irrigation by integrating intrusion detection mechanisms. By combining the functionalities of smart irrigation systems with intrusion detection systems (IDS), the proposed framework offers enhanced security and reliability in agricultural water management. The system employs a network of sensors weather, and soil moisture levels patterns, and other relevant parameters to optimize irrigation scheduling. Concurrently, it utilizes intrusion detection algorithms to identify and respond to unauthorized access attempts or anomalous behaviors within the irrigation infrastructure. The proposed approach represents a noteworthy advancement in the direction of sustainable and secure management of water in agriculture, contributing to improved crop yields, resource conservation, and overall perseverance in the face of emerging challenges. Additionally, by incorporating machine learning algorithms into the intrusion detection system, the framework is able to change and grow over time, enhancing its capacity to identify and neutralize possible threats. Furthermore, the system can more accurately predict when irrigation is needed by utilizing real-time data analysis and predictive modeling approaches. This maximizes water usage efficiency while reducing waste. This all-encompassing strategy encourages a more ecologically sustainable method of managing water resources while also strengthening the resilience of farming operations. Furthermore, the framework enables farmers to take educated decisions in real-time, maximizing productivity and lowering risks, by giving them actionable insights and alerts about security breaches and irrigation needs.

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.

Artificial Intelligence in Water Management for Sustainable Farming: A Review

Artificial Intelligence (AI) is capable of enhancing water management for sustainable farming. The growing demand for agricultural productivity and sustainability in the context of finite water resources and climate change drives the necessity for more efficient water management practices. AI technologies, through automated and precision irrigation systems, AI-based predictive models, and AI-driven water quality monitoring, offer significant improvements in water efficiency and agricultural output. These systems optimize irrigation scheduling based on real-time data, enhance the precision of water application, and ensure water quality, thus supporting sustainable agricultural practices. However, the implementation of AI in water management is not without challenges. Technical difficulties in adapting AI to diverse agricultural environments, data privacy and security concerns, ethical considerations, and barriers to adoption among small-scale farmers are critical issues that need addressing. This study addresses both the transformative impacts and the inherent challenges of integrating AI technologies. Furthermore, the review identifies a gap in research regarding AI’s adaptability to variable climates and its integration with socio-economic data, suggesting that future studies focus on these areas. Policy recommendations are also discussed, emphasizing the need for developing standards and best practices, increasing funding and incentives for AI research, promoting training and capacity building, and establishing robust regulatory frameworks for data management. By tackling these challenges and leveraging AI’s full potential, water management in agriculture can be significantly improved, leading to enhanced global water security and sustainability in farming practices. The review concludes that while AI presents a promising future for agricultural water management, strategic and thoughtful approaches are required to overcome obstacles and fully realize the benefits of this technology.

AI ENABLED WATER CONSERVATION FOR IRRIGATION USING IOT

This project introduces a smart irrigation system that utilizes Internet of Things (IoT) technology and machine learning algorithms to enhance water management in agriculture. The system employs a series of IoT sensors distributed across the field to consistently monitor key environmental factors such as soil moisture levels, temperature, humidity, and others. The collected data is analysed using machine learning Cat Boosting algorithm. This algorithm analyze the data to determine the optimal irrigation schedule based on crop water requirements, soil conditions, weather forecasts, and historical data. The system controls irrigation equipment such as pumps, valves, and sprinklers to deliver precise amounts of water to crops at the right time. Continuous feedback from the system allows for refinement of irrigation schedules, leading to improved water conservation, increased crop yield, and cost savings for farmers. This project discusses the benefits of such a system, including water conservation, increased crop yield, cost savings, environmental sustainability, and remote monitoring and control capabilities. Overall, the integration of IoT and machine learning technologies offers a powerful solution for sustainable agriculture, enabling data-driven decision-making processes to optimize water usage and maximize crop productivity. Keywords— Smart irrigation system, Internet of Things(IOT)technology, Water conservation, Environmental parameters, Soil moisture monitoring, Increased crop yield, Machine learning algorithms