Assessing the Water-Energy-Food-Ecosystems Nexus in Smart Irrigation: A Potato Farming Case Study in Lebanon

This study examines the Water-Energy-Food-Ecosystems (WEFE) nexus in Lebanese agriculture, with a focus on the shift from conventional surface irrigation techniques to advanced smart irrigation systems in the Bekaa region, specifically targeting potato cultivation. The study quantitatively analyzes the interaction among water, energy, and agricultural outputs at the farm scale using the WEFE Nexus framework for scenario analysis. It evaluates variations in water productivity, environmental effects, and economic outcomes, offering a detailed view of existing practices and their sustainable improvement potential. The WEFE Nexus assessment demonstrates that smart irrigation integration significantly decreased resource usage: water consumption was reduced by 58%, diesel fuel use for irrigation dropped by 57%, and the demand for labor and fertilizers decreased by 47% and 49%, respectively. This change led to enhanced crop yields and increased resource efficiency, demonstrating the potential of smart irrigation as a transformative strategy for sustainable agriculture in Lebanon and other arid areas. Economic analysis showed that farmers could recover the costs of installing the smart irrigation system within 3 months. The findings highlight the need for further research on integrating smart irrigation with renewable energy, showing potential for sustainable agricultural development. .

IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System

In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation process.Since the conventional irrigation system needs massive quantity of waterutilization, a smart irrigation system can be designed with the help of recenttechnologies such as machine learning (ML) and the Internet of Things (IoT).With this motivation, this paper designs a novel IoT enabled deep learningenabled smart irrigation system (IoTDL-SIS) technique. The goal of theIoTDL-SIS technique focuses on the design of smart irrigation techniquesfor effectual water utilization with less human interventions. The proposedIoTDL-SIS technique involves distinct sensors namely soil moisture, temperature, air temperature, and humidity for data acquisition purposes. The sensordata are transmitted to the Arduino module which then transmits the sensordata to the cloud server for further process. The cloud server performs the dataanalysis process using three distinct processes namely regression, clustering,and binary classification. Firstly, deep support vector machine (DSVM) basedregression is employed was utilized for predicting the soil and environmentalparameters in advances such as atmospheric pressure, precipitation, solarradiation, and wind speed. Secondly, these estimated outcomes are fed intothe clustering technique to minimize the predicted error. Thirdly, ArtificialImmune Optimization Algorithm (AIOA) with deep belief network (DBN)model receives the clustering data with the estimated weather data as inputand performs classification process. A detailed experimental results analysisdemonstrated the promising performance of the presented technique over theother recent state of art techniques with the higher accuracy of 0.971.

IoT-Solar Energy Powered Smart Farm Irrigation System

As the Internet of things(IoT)technology is evolving,distributed solar energy resources can be operated,monitored,and controlled remotely.The design of an IoT based solar energy system for smart irrigation is essential for regions around the world,which face water scarcity and power shortage.Thus,such a system is designed in this paper.The proposed system utilizes a single board system-on-a-chip controller(the controller hereafter),which has built-in WiFi connectivity,and connections to a solar cell to provide the required operating power.The controller reads the field soil moisture,humidity,and temperature sensors,and outputs appropriate actuation command signals to operate irrigation pumps.The controller also monitors the underground water level,which is essential to prevent the pump motors from burning due to the level in the water well.The proposed system has three modes of operations,i.e.the local control mode,mobile monitoring-control mode,and fuzzy logic-based control mode.For the purpose of the proposed system validation,a prototype was designed,built,and tested.

Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water shortages.This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers.The proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and bean.The proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real time.This helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop failure.Our dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research centers.The experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,respectively.The new smart greenhouse automation system was also evaluated on four crops with a high turnover rate.The system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

Effects of Intelligent Irrigation on Photosynthetic Characteristics of Citrus Leaves and Fruit Quality

[Objectives]This study was conducted to improve the water use efficiency and fruit quality of citrus,and realize the automatic irrigation and standardized production in citrus orchards.[Methods]With Orah as the research object,the effects of intelligent irrigation management in citrus orchards on citrus leaf chlorophyll content,photosynthetic characteristics and fruit quality were investigated by comparing with conventional farmer management.[Results]The smart irrigation measure in citrus orchards significantly increased the SPAD value of leaves at the maturation stage of citrus,and simultaneously significantly improved the photosynthetic rate,transpiration rate and stomatal conductance at the flower bud differentiation stage,as well as the photosynthetic rate,transpiration rate,stomatal conductance and intercellular CO_(2) concentration at the maturation stage.However,the effects on the photosynthetic characteristic indexes in the rainy season were not significant.Compared with conventional experience management,the smart irrigation management measure of citrus orchards increased the edible rate and juice rate of citrus by 4.53 and 3.69 percentage points,respectively,and increased soluble solids,total sugar,vitamin C and sugar-acid ratio by 16.75%,20.86%,24.10%and 13.17%,respectively.[Conclusions]The smart irrigation management fully met the water demand for citrus growth due to timely irrigation,significantly improved the photosynthesis indicators of citrus leaves during drought,and significantly improved the quality of citrus.

Effect of smart sprinkler irrigation utilization on water use efficiency for wheat crops in arid regions

The smart irrigation system(SIS)developed in this research is a valuable tool for scheduling irrigation and quantifying water required by plants.SIS was implemented and tested under sprinkler irrigation system to irrigate wheat crops(YecoraRojo).Results obtained from this system were compared with the control irrigation system(CIS),whose scheduling method was based on data from an automatic weather station.Results indicated significant savings in applied water using the SIS.In addition,the use of the SIS conserved 12%of irrigation water compared to CIS and obtained an economical yield.The water use efficiency(WUE)under SIS had generally higher values(1.64 kg/m^(3))compared to CIS(1.46 kg/m^(3)).Hence,the application of SIS technology provides significant advantages on WUE and irrigation water use efficiency(IWUE).Relatively high WUE and IWUE were found for the irrigation treatment(80%of evapotranspiration under SIS).Results showed that the irrigation requirements of wheat increased(100%of ETc under CIS)with increasing evapotranspiration(ETc)but excessive irrigation could decrease WUE and IWUE.These results indicated that extreme irrigation might not produce higher yield or optimal economic benefit,thus,suitable irrigation schedules by using SIS must be established and extendable to other agricultural crops.