Clarification of Water Stress in Apple Seedlings Using HSI Texture with Machine Learning Technique

Apples are known for their nutrition and economic value. Accurate and rapid diagnosis of water status in apple seedlings on an individual rootstock basis is a prerequisite for precision water management. This study presents a rapid and non-destructive approach for estimating water content in apple seedlings at leaf levels. A PIKA L system collects hyperspectral images(400-1000nm) of apple leaves. To the author's knowledge, no prior work was conducted using the spectral-texture approach in plant water stress. Our research extracts spatial information, gray-level co-occurrence matrix (GLCM), from feature wavelength images of hypercubes. Machine learning methods are applied to these spatial feature matrixs to identify apple leaves under different water stresses. In addition, differences in spectral responses were analysed using machine learning techniques for sorting apple seedlings with varying water treatments (dry, normal, and overwatering). Also, we measure chlorophyll to determine the relationship between hyperspectral characteristics and physiological changes. The achievements of the research indicate that the fusion of texture and hyperspectral imaging coupled with machine learning techniques is promising and presents a powerful potential to determine the water stress in the leaves of apple seedlings

Highlighting Water Stress in Apple Seedlings Using HSI Texture with Machine Learning Technique

Apples are known for their nutrition and economic value. Accurate and rapid diagnosis of water status in apple seedlings on an individual rootstock basis is a prerequisite for precision water management. This study presents a rapid and non-destructive approach for estimating water content in apple seedlings at leaf levels. A PIKA L system collects hyperspectral images (400-1000nm) of apple leaves. Our research extracts spatial information, gray-level co-occurrence matrix (GLCM), from feature wavelength images of hypercubes. Machine learning methods are applied to these spatial feature matrixs to identify apple leaves under different water stresses. In addition, differences in spectral responses were analysed using machine learning techniques for sorting apple seedlings with varying water treatments (dry, normal, and overwatering). Also, we measure chlorophyll to determine the relationship between hyperspectral characteristics and physiological changes. The achievements of the research indicate that the fusion of texture and hyperspectral imaging coupled with machine learning techniques is promising and presents a powerful potential to determine the water stress in the leaves of apple seedlings

Field Deployment and Integration of Wireless Communication & Operation Support System for the Landscape Irrigation Runoff Mitigation System

The study of water conservation technologies is critically important due to the rapid growth in urban population leading to a shortage in potable water supplies throughout the world. Current water supplies are not expected to meet the water demand in the coming decades; this could seriously affect human lives and socio-economic stability. About 30 percent of the current municipal supplies are being used for outdoor irrigation such as gardening and landscaping. These numbers are increasing due to the increase in urban population. Due to the current inefficient or improper landscape irrigation practices, substantial amounts of water are lost in the form of runoff or due to evaporation. Runoff occurs when the irrigation precipitation rate exceeds the infiltration rate of the soil, which depends on the soil and site characteristics such as soil type and the slope of the site. Runoff being an obvious water wastage, it also poses a great problem 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 smart operational support system for landscape irrigation that has the potential to reduce runoff and also decrease water losses in the form of evaporation. The system consists of two main units, the landscape irrigation runoff mitigation system (LIRMS) and an operational support system (OSS). The combined system is referred to as the second-generation LIRMS. The LIRMS is installed at the border of a field/lawn. The LIRMS consists of a central controller unit and a runoff sensor. Based on the feedback from the runoff sensor, the controller unit pauses and resumes irrigation as needed in order to reduce runoff. The main purpose of OSS is to automate the scheduling of the irrigation process. A multilayer perceptron based OSS was designed and implemented on a dedicated web-server. The OSS processes historical irrigation data and the environmental/weather data to choose an optimal schedule to irrigate on a given day. The OSS aims to reduce irrigation water losses due to natural environmental factors such as evaporation and rain. A wireless communication link is established between LIRMS and OSS for monitoring and analyzing irrigation events. The second-generation LIRMS was installed in the Texas A&M Turfgrass Research Field Laboratory, College Station, TX for performing irrigation tests. The preliminary results show that the average soil wetting efficiency has increased with the use of the operational support system when compared to previous tests performed without the operational support system. Also the results suggest that the second generation LIRMS has comparable runoff reductions when compared to the first-generation LIRMS. Yet, more tests are required to quantify the overall water savings

Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated

A Systematic Review of IoT Solutions for Smart Farming

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.info:eu-repo/semantics/publishedVersio

DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO MINIMIZE RUNOFF THROUGH AN AUTOMATED SMART IRRIGATION SYSTEM

The 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

CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship

This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship

Alamogordo News, 07-25-1912

https://digitalrepository.unm.edu/alamogordo_news/1446/thumbnail.jp

Proposal of architecture for IoT solution for monitoring and management of plantations

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production This work presents a systematic review of the existing literature on smart farming with IoT. The systematic review reveals an evolution in the way data are processed by IoT solutions in recent years. Traditional approaches mostly used data in a reactive manner. In contrast, recent approaches allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis. Based on the finds of the systematic review, this work proposes an architecture of an IoT solution that enables monitoring and management of crops in real time. The proposed architecture allows the usage of big data and machine learning to process the collected data. A prototype is implemented to validate the operation of the proposed architecture and a security risk assessment of the implemented prototype is carried out. The implemented prototype successfully validates the proposed architecture. The architecture presented in this work allows the implementation of IoT solutions in different scenarios of farming, such as indoor and outdoor

Strafford county 2005 annual report of the commissioners, treasurer, other county officers and the Strafford county delegation Strafford county, New Hampshire for the year ending December 31, 2005.

This is an annual report containing vital statistics for a county in the state of New Hampshire