scholarly journals Greenhouse Automation Using Wireless Sensors and IoT Instruments Integrated with Artificial Intelligence

2021 ◽  
Author(s):  
Redmond R. Shamshiri ◽  
Ibrahim A. Hameed ◽  
Kelly R. Thorp ◽  
Siva K. Balasundram ◽  
Sanaz Shafian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Large Scale ◽  
Fruits And Vegetables ◽  
Growth Models ◽  
Climate Control ◽  
Time Data ◽  
Multiple Sensors ◽  
Knowledge Based ◽  
Crop Growth Models ◽  
Streaming Systems

Automation of greenhouse environment using simple timer-based actuators or by means of conventional control algorithms that require feedbacks from offline sensors for switching devices are not efficient solutions in large-scale modern greenhouses. Wireless instruments that are integrated with artificial intelligence (AI) algorithms and knowledge-based decision support systems have attracted growers’ attention due to their implementation flexibility, contribution to energy reduction, and yield predictability. Sustainable production of fruits and vegetables under greenhouse environments with reduced energy inputs entails proper integration of the existing climate control systems with IoT automation in order to incorporate real-time data transfer from multiple sensors into AI algorithms and crop growth models using cloud-based streaming systems. This chapter provides an overview of such an automation workflow in greenhouse environments by means of distributed wireless nodes that are custom-designed based on the powerful dual-core 32-bit microcontroller with LoRa modulation at 868 MHz. Sample results from commercial and research greenhouse experiments with the IoT hardware and software have been provided to show connection stability, robustness, and reliability. The presented setup allows deployment of AI on embedded hardware units such as CPUs and GPUs, or on cloud-based streaming systems that collect precise measurements from multiple sensors in different locations inside greenhouse environments.

Crop growth models for decision support systems

1996 ◽  
Vol 76 (1) ◽  
pp. 9-19 ◽  
Author(s):  
Y. W. Jame ◽  
H. W. Cutforth
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Crop Production ◽  
Systems Approach ◽  
Support Systems ◽  
Growth Models ◽  
Crop Growth ◽  
Agricultural Management ◽  
Knowledge Based ◽  
Crop Growth Models

Studies on crop production are traditionally carried out by using conventional experience-based agronomic research, in which crop production functions were derived from statistical analysis without referring to the underlying biological or physical principles involved. The weaknesses and disadvantages of this approach and the need for greater in-depth analysis have long been recognized. Recently, application of the knowledge-based systems approach to agricultural management has been gaining popularity because of our expanding knowledge of processes that are involved in the growth of plants, coupled with the availability of inexpensive and powerful computers. The systems approach makes use of dynamic simulation models of crop growth and of cropping systems. In the most satisfactory crop growth models, current knowledge of plant growth and development from various disciplines, such as crop physiology, agrometeorology, soil science and agronomy, is integrated in a consistent, quantitative and process-oriented manner. After proper validation, the models are used to predict crop responses to different environments that are either the result of global change or induced by agricultural management and to test alternative crop management options.Computerized decision support systems for field-level crop management are now available. The decision support systems for agrotechnology transfer (DSSAT) allows users to combine the technical knowledge contained in crop growth models with economic considerations and environmental impact evaluations to facilitate economic analysis and risk assessment of farming enterprises. Thus, DSSAT is a valuable tool to aid the development of a viable and sustainable agricultural industry. The development and validation of crop models can improve our understanding of the underlying processes, pinpoint where our understanding is inadequate, and, hence, support strategic agricultural research. The knowledge-based systems approach offers great potential to expand our ability to make good agricultural management decisions, not only for the current climatic variability, but for the anticipated climatic changes of the future. Key words: Simulation, crop growth, development, management strategy

scholarly journals Intersection of Artificial Intelligence, Machine Learning, and Internet of Things – An Economic Overview

2020 ◽  
Vol 9 (2) ◽  
pp. 119-128
Author(s):  
Mani Manavalan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Large Scale ◽  
Smart Devices ◽  
Time Data ◽  
Business World ◽  
Course Of Action ◽  
To Come ◽  
Set Up

Internet of Things (IoT) has become one of the mainstream advancements and a supreme domain of research for the technical as well as the scientific world, and financially appealing for the business world. It supports the interconnection of different gadgets and the connection of gadgets to people. IoT requires a distributed computing set up to deal with the rigorous data processing and training; and simultaneously, it requires artificial intelligence (AI) and machine learning (ML) to analyze the information stored on various cloud frameworks and make extremely quick and smart decisions w.r.t to data. Moreover, the continuous developments in these three areas of IT present a strong opportunity to collect real-time data about every activity of a business. Artificial Intelligence (AI) and Machine Learning are assuming a supportive part in applications and use cases offered by the Internet of Things, a shift evident in the behavior of enterprises trying to adopt this paradigm shift around the world. Small as well as large-scale organizations across the globe are leveraging these applications to develop the latest offers of services and products that will present a new set of business opportunities and direct new developments in the technical landscape. The following transformation will also present another opportunity for various industries to run their operations and connect with their users through the power of AI, ML, and IoT combined. Moreover, there is still huge scope for those who can convert raw information into valuable business insights, and the way ahead to do as such lies in viable data analytics. Organizations are presently looking further into the data streams to identify new and inventive approaches to elevate proficiency and effectiveness in the technical as well as business landscape. Organizations are taking on bigger, more exhaustive research approaches with the assistance of continuous progress being made in science and technology, especially in machine learning and artificial intelligence. If companies want to understand the valuable capacity of this innovation, they are required to integrate their IoT frameworks with persuasive AI and ML algorithms that allow ’smart devices/gadgets’ to imitate behavioral patterns of humans and be able to take wise decisions just like humans without much of an intervention. Integrating both artificial intelligence and machine learning with IoT networks is proving to be a challenging task for the accomplishment of the present IoT-based digital ecosystems. Hence, organizations should direct the necessary course of action to identify how they will drive value from intersecting AI, ML, and IoT to maintain a satisfactory position in the business in years to come. In this review, we will also discuss the progress of IoT so far and what role AI and ML can play in accomplishing new heights for businesses in the future. Later the paper will discuss the opportunities and challenges faced during the implementation of this hybrid model.

scholarly journals Digital Therapeutics: Virtual Coaching Powered by Artificial Intelligence on Real-World Data

2021 ◽  
Vol 3 ◽  
Author(s):  
Harm op den Akker ◽  
Miriam Cabrita ◽  
Aristodemos Pnevmatikakis
Keyword(s):  
Artificial Intelligence ◽  
Conversational Agents ◽  
Time Data ◽  
Real World Data ◽  
Virtual Coaching ◽  
Knowledge Based ◽  
Iot Devices ◽  
Main Components ◽  
New Generation

An ever-increasing number of people need to cope with one or more chronic conditions for a significant portion of their life. Digital Therapeutics (DTx) focused on the prevention, management, or treatment of chronic diseases are promising in alleviating the personal socio-economic burden caused. In this paper we describe a proposed DTx methodology covering three main components: observation (which data is collected), understanding (how to acquire knowledge based on the data collected), and coaching (how to communicate the acquired knowledge to the user). We focus on an emerging form of automated virtual coaching, delivered through conversational agents allowing interaction with end-users using natural language. Our methodology will be applied in the new generation of the Healthentia platform, an eClinical solution that captures clinical outcomes from mobile, medical and Internet of Things (IoT) devices, using a patient-centric mobile application and offers Artificial Intelligence (AI) driven smart services. While we are unable to provide data to prove its effectiveness, we illustrate the potential of the proposed architecture to deliver DTx by describing how the methodology can be applied to a use-case consisting of a clinical trial for treatment of a chronic condition, combining testing of a new medication and a lifestyle intervention, which will be partly implemented and evaluated in the context of the European research project RE-SAMPLE (REal-time data monitoring for Shared, Adaptive, Multi-domain and Personalised prediction, and decision making for Long-term Pulmonary care Ecosystems).

scholarly journals PARADIGM SHIFT: ENGINEERING ARTIFICIAL INTELLIGENCE AND MANAGEMENT STRATEGIES FUSION

2016 ◽  
Vol 4 (2) ◽  
pp. 1-16
Author(s):  
A. H. Harb ◽  
AbdAlhameedAbdAlhameed Alsayyid
Keyword(s):  
Artificial Intelligence ◽  
Strategic Management ◽  
Large Scale ◽  
Qualitative Methodology ◽  
Management Strategies ◽  
Global Market ◽  
Ai Planning ◽  
Time Data ◽  
Test Procedures ◽  
Organizational Goals

The purpose of this paper is to investigate management strategies that use Artificial Intelligence to perceive, capture, and process real-time data to predict and direct the performance of an enterprise.  AI systems can account for errors in human judgment through computational processes that supersede the capabilities of human intelligence alone.  Qualitative methodology was used to assess components such as fuzzy logic that can produce answers determined by multiple factors that can be integrated into a determinant solution. Quantifying the neurocircuitry of strategic management processes in tacticians will serve as the foundation for AI and management strategies engineering fusion.  When AI is programmed to motivate, interact, and make judgements based upon statistical measurements, the fusion of AI and management engineering can increase efficiency and effectiveness in the attainment of organizational goals. Results demonstrated the application of AI planning can range from directing large-scale machinery overhaul procedures, spacecraft mission planning, emergency response, assembling test procedures for rocket launchers, and delivery truck scheduling. A thorough understanding AI for strategic management engineering fusion and its underlying concepts is a prerequisite to competitive advantage in a global market.

scholarly journals Implementation of artificial intelligence in the construction industry and analysis of existing technologies

2021 ◽  
Vol 2 (2(58)) ◽  
pp. 12-15
Author(s):  
Kateryna Kyivska ◽  
Svitlana Tsiutsiura
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Technology ◽  
Fuzzy Logic ◽  
Construction Industry ◽  
Construction Projects ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Time Data ◽  
Knowledge Based

The object of research is the process of using information technology in the construction industry. One of the most problematic areas is increasing the efficiency of the construction industry through the introduction of digital technologies. The research carried out is based on the application of an approach that is implemented using artificial intelligence. The study used machine learning and fuzzy logic methods to mark visual data and analyze it for potential threats, as well as to reduce all possible risks. The main feature of this approach is that using machine learning technology, it is possible to reduce the risks of a project before they affect its profit. So, using artificial intelligence in combination with BIM technologies, it is possible to predict work on construction projects based on real-time data, past activities and other factors in such a way as to optimize construction processes. The benefits to be gained from implementing digital processes will become even more evident in future projects as AI continues to analyze company data. This is due to the fact that the proposed approach using fuzzy logic has a number of features, in particular, the more information machine learning algorithms process, the more complex they become. As a result, they provide even more useful information and allow to make even better decisions. This provides an opportunity to minimize risks and efficiently allocate resources when working on projects. Compared to conventional information technology, artificial intelligence can be used to build a knowledge-based security management system and combine statistical probabilities to help mitigate security risks in construction projects.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2146
Author(s):  
Manuel Andrés Vélez-Guerrero ◽  
Mauro Callejas-Cuervo ◽  
Stefano Mazzoleni
Keyword(s):  
Artificial Intelligence ◽  
Upper Limb ◽  
Main Trend ◽  
Motor Rehabilitation ◽  
Rehabilitation Process ◽  
Upper Limb Rehabilitation ◽  
Multiple Sensors ◽  
Meta Analyses ◽  
Limb Rehabilitation ◽  
And Control

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

scholarly journals An Agent-Based Crop Model Framework for Heterogeneous Soils

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 85
Author(s):  
Jorge Lopez-Jimenez ◽  
Nicanor Quijano ◽  
Alain Vande Wouwer
Keyword(s):  
Growth Models ◽  
Control Strategies ◽  
Low Cost ◽  
Soil Heterogeneity ◽  
Crop Growth ◽  
Negative Effects ◽  
Model Framework ◽  
Agent Based ◽  
Crop Growth Models ◽  
Definition Of

Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilities to enhance the representation of production scenarios. In this work, an agent-based model (ABM) that considers the soil heterogeneity and water exchanges is proposed. Soil heterogeneity is associated to the combination of individual behaviours of uniform portions of land (agents), while water fluxes are related to the topography. Each agent is characterized by an individual dynamic model, which describes the local crop growth. Moreover, this model considers positive and negative effects of water level, i.e., drought and waterlogging, on the biomass production. The development of the global ABM is oriented to the future use of control strategies and optimal irrigation policies. The model is built bottom-up starting with the definition of agents, and the Python environment Mesa is chosen for the implementation. The validation is carried out using three topographic scenarios in Colombia. Results of potential production cases are discussed, and some practical recommendations on the implementation are presented.

scholarly journals Evaluation of a Low-Cost Commercial Actigraph and Its Potential Use in Detecting Cultural Variations in Physical Activity and Sleep

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3774
Author(s):  
Pavlos Topalidis ◽  
Cristina Florea ◽  
Esther-Sevil Eigl ◽  
Anton Kurapov ◽  
Carlos Alberto Beltran Leon ◽  
...  
Keyword(s):  
Physical Activity ◽  
Low Income ◽  
Large Scale ◽  
Low Cost ◽  
Low Income Countries ◽  
Time Data ◽  
Cultural Variations ◽  
Step Counts ◽  
Scale Step ◽  
Potential Use

The purpose of the present study was to evaluate the performance of a low-cost commercial smartwatch, the Xiaomi Mi Band (MB), in extracting physical activity and sleep-related measures and show its potential use in addressing questions that require large-scale real-time data and/or intercultural data including low-income countries. We evaluated physical activity and sleep-related measures and discussed the potential application of such devices for large-scale step and sleep data acquisition. To that end, we conducted two separate studies. In Study 1, we evaluated the performance of MB by comparing it to the GT3X (ActiGraph, wGT3X-BT), a scientific actigraph used in research, as well as subjective sleep reports. In Study 2, we distributed the MB across four countries (Austria, Germany, Cuba, and Ukraine) and investigated physical activity and sleep among these countries. The results of Study 1 indicated that MB step counts correlated highly with the scientific GT3X device, but did display biases. In addition, the MB-derived wake-up and total-sleep-times showed high agreement with subjective reports, but partly deviated from GT3X predictions. Study 2 revealed similar MB step counts across countries, but significant later wake-up and bedtimes for Ukraine than the other countries. We hope that our studies will stimulate future large-scale sensor-based physical activity and sleep research studies, including various cultures.

Impact of the clinical use of artificial intelligence-assisted neoplasia detection for colonoscopy: a large-scale prospective, propensity score-matched study (with video)

2021 ◽  
Author(s):  
Misaki Ishiyama ◽  
Shin-ei Kudo ◽  
Masashi Misawa ◽  
Yuichi Mori ◽  
Maeda Yasuharu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Propensity Score ◽  
Large Scale ◽  
Clinical Use ◽  
Matched Study
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