12 research outputs found
DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO MINIMIZE RUNOFF THROUGH AN AUTOMATED SMART IRRIGATION SYSTEM
No full textThe study of proper water management practices is of prime importance due to the ever-
increasing population and rapid industrialization which results in shortage of portable water
supplies throughout the world. The current water supplies are not expected to meet the increasing
demand in the upcoming decades which could in result affect the socio-economic stability and have a detrimental effect on human livelihood.
About 30% of the current municipal supplies in the world are used for outdoor irrigation activities such as gardening and landscaping purposes. These numbers are on the rise due to the ever increasing human population. Due to the current inefficient landscape practices, substantial
amount of water is lost in the form of runoff. This poses a great threat to the environment with its potential for transporting fertilizers and pesticides into storm sewers and, eventually, surface waters. Thus, this study focuses on designing a Machine Learning approach which would act as a Decision Support System (DSS) to irrigate turfgrasses to minimize runoff in the plots while maintaining the quality of the turfgrasses.
For this, a robust Machine Learning approach named as Radial Basis Function - Support Vector Machine (RBF-SVM) was proposed which was trained on the synthetic data generated from the datapoints recorded during the year 2015-16 and 2016-17 at the Turfgrass Laboratory in
Texas A & M University, College Station. For each of the approaches, the target variable was changed and the number of features were varied in each case to see which gives the best results.
Among all the target variables, predicting the Soil Wetting Efficiency Index, devised by Wherley, et. al.[33] was the most applicable as it is one of the most generic approaches since it is not site-specific and gave the highest validation testing accuracy of 90%. Thus, the latter approach was used in the ASIS controller to observe the robustness of the algorithm in controlling the effectiveness of the irrigation cycle. Until now, only few irrigation cycles have been scheduled and experimental data are still
being collected from the facility. Preliminary results suggested that the Machine Learning algorithm has the potential to save water as it helped in efficient regulation of irrigation cycles and even achieved a goal of zero runoff in two of the irrigation runs. The Green Cover percentage of the plots where the proposed ASIS controller was mounted showed an increment of about 12%, thereby validating the fact that the quality of turfgrasses was also maintained. With more irrigation
cycles which would be scheduled over time, the proposed Machine Learning approach is expected to perform better with increase in observations and may nullify runoff eventually
Heavy Metal Nutrient Concentration data
No full textHeavy metal and important nutrient concentration dat
Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review
No full textAbstract Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust
Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization
No full textGlobal food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts
Machine learning-based analysis of nutrient and water uptake in hydroponically grown soybeans
No full textAbstract Recent advancements in sustainable agriculture have spurred interest in hydroponics as an alternative to conventional farming methods. However, the lack of data-driven approaches in hydroponic growth presents a significant challenge. This study addresses this gap by varying nitrogen, magnesium, and potassium concentrations in hydroponically grown soybeans and conducting essential nutrient profiling across the growth cycle. Statistical techniques like Linear Interpolation are employed to interpolate nutrient data and a feature selection pipeline consisting of chi-squared testing methods, Linear Regression with Recursive Feature Elimination (RFE) and ExtraTreesClassifier have been used to select important nutrients for predicting water uptake using non-parametric regression methods. For different nutrient growth media, i.e. for soybeans grown in Hoagland + Nitrogen and Hoagland + Magnesium media, the Random Forest regressor outperformed other methods in predicting water uptake, achieving testing Mean Squared Error (MSE) scores of 24.55 ( R 2 score 0.63) and 8.23 ( R 2 score 0.81), respectively. Similarly, for soybeans grown in Hoagland + Potassium media, Support Vector Regression demonstrated superior performance with a testing MSE of 4.37 and R 2 score of 0.85. SHapley Additive exPlanations (SHAP) values are examined in each case to elucidate the contributions of individual nutrients to water uptake predictions. This research aims to provide data-driven insights to optimize hydroponic practices for sustainable food production
Machine learning-based smart irrigation controller for runoff minimization in turfgrass irrigation
No full textInadequate turfgrass irrigation management poses a significant challenge, resulting in considerable water loss through runoff and the transport of contaminants, ultimately jeopardizing surface and groundwater quality. This study introduces a Machine Learning (ML)-based Decision Support System (DSS) designed to optimize turfgrass irrigation, concurrently minimizing runoff and preserving turfgrass quality. A robust ML classifier, specifically the Radial Basis Function - Support Vector Machine (RBF-SVM) was trained on synthetic data generated through the Monte-Carlo (MC) technique, which was then used to specify a set of irrigation rules implemented in the irrigation controller. The synthetic data were derived from observations collected from irrigation plots at the Texas A&M University Turfgrass Laboratory in Texas, United States, with Soil Wetting Efficiency Index (SWEI) serving as the target variable. When tested against a commercially available irrigation controller, the ML-based controller significantly reduced runoff by an average of 74 % while maintaining high Green Cover (GC) in turfgrass, achieving an accuracy of 87 %. These findings highlight the potential of ML-driven irrigation systems to improve water use efficiency, reduce environmental impact, and maintain turf quality. Such systems could be beneficial for urban landscapes, sports fields, and agriculture, helping users conserve water while achieving sustainable turf management
Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets
No full textWith the recent trends in urban agriculture and climate change, there is an emerging need for alternative plant culture techniques where dependence on soil can be eliminated. Hydroponic and aquaponic growth techniques have proven to be viable alternatives, but the lack of efficient and optimal practices for irrigation and nutrient supply limits its applications on a large-scale commercial basis. The main purpose of this research was to develop statistical methods and Machine Learning algorithms to regulate nutrient concentrations in aquaponic irrigation water based on plant needs, for achieving optimal plant growth and promoting broader adoption of aquaponic culture on a commercial scale. One of the key challenges to developing these algorithms is the sparsity of data which requires the use of Bolstered error estimation approaches. In this paper, several linear and non-linear algorithms trained on relatively small datasets using Bolstered error estimation techniques were evaluated, for selecting the best method in making decisions regarding the regulation of nutrients in hydroponic environments. After repeated tests on the dataset, it was decided that Semi-Bolstered Resubstitution Error estimation technique works best in our case using Linear Support Vector Machine as the classifier with the value of penalty parameter set to one. A set of recommended rules have been prescribed as a Decision Support System, using the output of the Machine Learning algorithm, which have been tested against the results of the baseline model. Further, the positive impact of the recommended nutrient concentrationson plant growth in aquaponic environments has been elaborately discussed
Modeling Sea Level Rise Using Ensemble Techniques: Impacts on Coastal Adaptation, Freshwater Ecosystems, Agriculture and Infrastructure
No full textSea level rise (SLR) is a crucial indicator of climate change, primarily driven by greenhouse gas emissions and the subsequent increase in global temperatures. The impact of SLR, however, varies regionally due to factors such as ocean bathymetry, resulting in distinct shifts across different areas compared to the global average. Understanding the complex factors influencing SLR across diverse spatial scales, along with the associated uncertainties, is essential. This study focuses on the East Coast of the United States and Gulf of Mexico, utilizing historical SLR data from 1993 to 2023. To forecast SLR trends from 2024 to 2103, a weighted ensemble model comprising SARIMAX, LSTM, and exponential smoothing models was employed. Additionally, using historical greenhouse gas data, an ensemble of LSTM models was used to predict real-time SLR values, achieving a testing loss of 0.005. Furthermore, conductance and dissolved oxygen (DO) values were assessed for the entire forecasting period, leveraging forecasted SLR trends to evaluate the impacts on marine life, agriculture, and infrastructure
A Machine-Learning-Based IoT System for Optimizing Nutrient Supply in Commercial Aquaponic Operations
No full textNutrient regulation in aquaponic environments has been a topic of research for many years. Most studies have focused on appropriate control of nutrients in an aquaponic set-up, but very little research has been conducted on commercial-scale applications. In our model, the input data were sourced on a weekly basis from three commercial aquaponic farms in Southeast Texas over the course of a year. Due to the limited number of data points, dimensionality reduction techniques such as pairwise correlation matrix were used to remove the highly correlated predictors. Feature selection techniques such as the XGBoost classifier and Recursive Feature Elimination with ExtraTreesClassifier were used to rank the features in order of their relative importance. Ammonium and calcium were found to be the top two nutrient predictors, and based on the months in which lettuce was cultivated, the median of these nutrient values from the historical dataset served as the optimal concentration to be maintained in the aquaponic solution to sustain healthy growth of tilapia fish and lettuce plants in a coupled set-up. To accomplish this, Vernier sensors were used to measure the nutrient values and actuator systems were built to dispense the appropriate nutrient into the ecosystem via a closed loop
Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops
No full textCotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This study employed unmanned aerial system (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. Long short-term memory (LSTM) models, trained using 2020 data, were subsequently applied to forecast the CC values for 2021. These models were compared with real-time auto-regressive integrated moving average (ARIMA) models to assess their effectiveness in predicting the CC values up to 14 days in advance, starting from the 28th day after crop emergence. The results showed that multiple-input multi-step output LSTM models achieved higher accuracy in predicting the in-season CC values during the early growth stages (up to the 56th day), with an average testing RMSE of 3.86, significantly lower than other single-input LSTM models. Conversely, when sufficient testing data are available, single-input stacked-LSTM models demonstrated precision in CC predictions for later stages, achieving an average RMSE of 3.06. These findings highlight the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production
