Ibrahim A. Hameed | Norwegian University of Science and Technology (original) (raw)

Books by Ibrahim A. Hameed

Environmental control for commercial plant production affects the productivity and the quality of... more Environmental control for commercial plant production affects the productivity and the quality of the crop. The efficiency of plant production in greenhouses depends significantly on the adjustment of several components particularly, the greenhouse interior temperature, relative humidity and Co2 concentration. In warm climates, the greenhouse air temperature and relative humidity are controlled by means of a simultaneous ventilation and humidification. Humidification usually requires some sort of evaporative devices such as misters, fog units or sprinklers, all of which cool and add water vapour to the greenhouse air. Dehumidifiers are very expensive, thereby in warm countries the only available solution for dehumidification is ventilation. Ventilation is required during most of the day to exchange the moist air with drier outside air. Moreover, it is very important in all greenhouses even if they are not controlled since it decreases the so-called “Greenhouse effect” which is mainly due to the confining of the air in the greenhouse enclosure and less to the radiative properties of the cover. Conventional controllers (e.g. Pseudo-Derivative Feedback Controller) are employed to maintain, at any time, optimal temperature and relative humidity inside the greenhouse, and to overcome the load effect of the outdoor undesirable climatic conditions. Since greenhouses are continually exposed to changing conditions, e.g. the outside climate and the thermal effect of the growing plant inside it, the greenhouse moves between different operating points within the whole growing season. That leads to a complex control problem requiring effective intelligent controllers. In practice, conventional controllers were used to control the system however their parameters are empirically adjusted. Besides, the operation of these controllers relies on the measurements provided by sensors located inside and near the greenhouse. If the information provided by one or several of these sensors is erroneous, the controllers will not operate properly. Similarly, failure of one or several of the actuators to function properly will impair the greenhouse operation. Therefore, an automatic diagnosis system of failures in greenhouses is proposed. The diagnosis system is based on deviations observed between measurements performed in the system and the predictions of a model of the failure-free system. This comparison is done through a bank of fuzzy observers, where each observer becomes active to a specific failure signature and inactive to the other failures. Neural networks are used to develop a model for the failure-free greenhouse. The main objective of this thesis is to explore and develop intelligent control schemes for adjusting the climate inside a greenhouse. The thesis employs the conventional Pseudo- Derivative Feedback (PDF) Controller. It develops the fuzzy PDF controller (FPDF). The thesis also, develops two genetic algorithm (GA) based climatic control schemes, one is genetic PDF (GPDF) and the other is genetic FPDF (GFPDF). The former uses GA to adjust the gains of the Pseudo-Derivative Feedback Controller (GPDF) and the later uses genetic algorithm to optimize the FPDF controller parameters (i.e., scale factors and/or parameters of the membership functions). Finally, the thesis develops a fuzzy neural fault detection and isolation system (FNFDIS), in which a bank of fuzzy observers are designed to detect faults that may occur in the greenhouse end items (e.g.., sensors and actuators). Simulation experiments are performed to test the soundness and capabilities of the developed control schemes for controlling the greenhouse climate. The proposed schemes are tested through two experiments, setpoint tracking test and regulatory control test. Also, the proposed diagnostic system was tested through four experiments. Compared with the results obtained using the conventional controllers, best results have been achieved using the proposed control schemes.

Fuzzy Logic (FL) is a particular area of interest in the study of Artificial intelligence (AI) ba... more Fuzzy Logic (FL) is a particular area of interest in the study of Artificial intelligence (AI) based on the idea that in fuzzy sets each element in the set can assume a value from 0 to 1, not just 0 or 1, as in classic or crisp set theory. The gradation in the extent to which an element is belonging to the relevant sets is called the degree of membership. This degree of membership is a measure of the element’s belonging to the set, and thus of the precision with which it explains the phenomenon being evaluated. A linguistic expression is given to each fuzzy set. The information contents of the fuzzy rules are then used to infer the output using a suitable inference engine. The key contribution of fuzzy logic in computation of information described in natural language made it applicable to a variety of applications and problem domains; from simple control systems to human decision support systems. Yet, despite its long-standing origins, it is a relatively new field, and as such leaves much room for development. The thesis presents two novel applications of fuzzy systems; a human decision support system to help teachers to fairly evaluate students and two hybrid intelligent fuzzy systems; a type-2 fuzzy logic system and a combined type-1 fuzzy logic system and extended Kalamn filter for controlling systems operating under high levels of uncertainties due to various sources of measurement and modeling errors. The combination of fuzzy logic and the classical student evaluation approach produces easy to understand transparent decision model that can be easily understood by students and teachers alike. The developed architecture overcomes the problem of ranking students with the same score. It also incorporated different dimensions of evaluation by considering subjective factors such as difficulty, complexity and importance of the questions. Although we discuss this approach with an example from the area of student evaluation, this method evidently has wide applications in other areas of decision making including student’s project evaluation, learning management systems evaluation, as well as, other assessment applications. Uncertainty is an attribute of information. For systems being controlled using the mentioned above type-1 fuzzy logic systems, such uncertainty leads to fuzzy rules whose antecedents or consequents are uncertain, which translates into uncertain antecedent or consequent membership functions. Type-1 fuzzy systems, whose membership functions are type-1 fuzzy sets, are unable to directly handle such uncertainties. Type-2 fuzzy systems in which the antecedent or consequent membership functions are type-2 fuzzy sets. Such sets are fuzzy sets whose membership grades themselves are type-1 fuzzy sets, are very useful in circumstances where it is difficult to determine an exact membership function for a fuzzy set. By combining type-2 fuzzy logic with traditional soft computing techniques such as genetic algorithms, we build a powerful hybrid intelligent control system that can use the advantages that each technique offers. Due to the complexity of implementing type-2 systems, a simplified approach for building type-2 fuzzy system using the well known type-1 is developed. A genetic algorithm is used to give adaptability to the fuzzy system to adapt to changing situations. In addition, it provides the system with an aid to show how much uncertainty is incorporated in the system. The system is applied to a nonlinear multi-input multi-output system equipped with almost all types of uncertainty and it shows very stable response even under very high levels of uncertainties. A novel approach for controlling systems equipped with high levels and different sources of uncertainties due to measurement and modeling errors is developed by combining a type-1 fuzzy system with the well known extended Kalman filter (EKF). The addition of an EKF in the feedback loop improved the system response by blocking possible effects of measurement error through the use of the estimated states instead of the measured states. The developed type-1 fuzzy-Kalamn filter scheme is applied to a complex, nonlinear multi-input multi-output system exposed to high levels of noise. Surprisingly, the new scheme decreased the power consumption while keeping system states very close to the desired states. In addition, the output response becomes very smooth which could help to increase the life time of the system actuators. The filtering effect is also expected to result in less number of false alarms when fault detection and isolation system is applied and hence increase system robustness and reliability.

Papers by Ibrahim A. Hameed

devoted to a global view on ambient computing, services, applications, technologies and their int... more devoted to a global view on ambient computing, services, applications, technologies and their integration. On the way for a full digital society, ambient, sentient and ubiquitous paradigms lead the torch. There is a need for behavioral changes for users to understand, accept, handle, and feel helped within the surrounding digital environments. Ambient comes as a digital storm bringing new facets of computing, services and applications. Smart phones and sentient offices, wearable devices, domotics, and ambient interfaces are only a few of such personalized aspects. The advent of social and mobile networks along with context-driven tracking and localization paved the way for ambient assisted living, intelligent homes, social games, and telemedicine. The conference had the following tracks:  Ambient services, technology and platforms  Ambient devices, applications and systems  Ambient computing environments, sensors and hardware We take here the opportunity to warmly thank all the m...

Applied Sciences

Soft growing robots, which mimic the biological growth of plants, have demonstrated excellent per... more Soft growing robots, which mimic the biological growth of plants, have demonstrated excellent performance in navigating tight and distant environments due to their flexibility and extendable lengths of several tens of meters. However, controlling the position of the tip of these robots can be challenging due to the lack of precise methods for measuring the robots’ Cartesian position in their working environments. Moreover, classical control techniques are not suitable for these robots because they involve the irreversible addition of materials, which introduces process constraints. In this paper, we propose two optimization-based approaches, combining Moving Horizon Estimation (MHE) with Nonlinear Model Predictive Control (NMPC), to achieve superior performance in point stabilization, trajectory tracking, and obstacle avoidance for these robots. MHE is used to estimate the entire state of the robot, including its unknown Cartesian position, based on available configuration measureme...

This study investigates the potential of a simple artificial neural network for the prediction of... more This study investigates the potential of a simple artificial neural network for the prediction of COVID-19 New Confirmed Cases in Algeria (CNCC).Four different ANN models were built (GRNN, RBFNN, ELM, and MLP). The performance of the predictive models is evaluated based on four numerical parameters, namely root mean squared error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and Pearson correlation coefficient (R). Taylor diagram was also used to examine the similarities and differences between the observed and predicted values obtained from the proposed models.The results showed the potential of the multi-layer perceptron neural network (MLPNN) which exhibited a high level of accuracy in comparison to the other models.

Journal of Clinical Medicine

Globally, coal remains one of the natural resources that provide power to the world. Thousands of... more Globally, coal remains one of the natural resources that provide power to the world. Thousands of people are involved in coal collection, processing, and transportation. Particulate coal dust is produced during these processes, which can crush the lung structure of workers and cause pneumoconiosis. There is no automated system for detecting and monitoring diseases in coal miners, except for specialist radiologists. This paper proposes ensemble learning techniques for detecting pneumoconiosis disease in chest X-ray radiographs (CXRs) using multiple deep learning models. Three ensemble learning techniques (simple averaging, multi-weighted averaging, and majority voting (MVOT)) were proposed to investigate performances using randomised cross-folds and leave-one-out cross-validations datasets. Five statistical measurements were used to compare the outcomes of the three investigations on the proposed integrated approach with state-of-the-art approaches from the literature for the same da...

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Accurate prediction of soil erosion rate is a quite important issue for a wise and sustainable us... more Accurate prediction of soil erosion rate is a quite important issue for a wise and sustainable use of soil resources. In this study, an adaptive neuro-fuzzy inference system (ANFIS) approach is used to construct a prediction model. The objectives of this study is to develop fuzzy logic models that predict soil erosion in a relatively large watershed using a limited number of input variables, compare the predictions of soil erosion using ANFIS model with those of the Revised Universal Soil Loss Equation RUSLE.With the incorporation of Geographical Information System (GIS), it is possible to analyse satellite data, which gives required information like land use and cover, slope, distribution of rainfall, flow direction etc. of study watershed. The capabilities of these technologies increase when they are integrated with ANFIS model for erosion prediction.ANFIS model and GIS integrated erosion prediction models do not only estimate soil loss but also provide the spatial distributions of the erosion. Generating accurate erosion risk maps in GIS environment is very important to locate the areas with high erosion risks for prioritization and to develop adequate conservation techniques for a better sustainable management of Wadi Sahel watershed (Algeria).

Digital Agriculture, Methods and Applications [Working Title]

Adopting new information and communication technology (ICT) as a solution to achieve food securit... more Adopting new information and communication technology (ICT) as a solution to achieve food security becomes more urgent than before, particularly with the demographical explosion. In this survey, we analyze the literature in the last decade to examine the existing fog/edge computing architectures adapted for the smart farming domain and identify the most relevant challenges resulting from the integration of IoT and fog/edge computing platforms. On the other hand, we describe the status of Blockchain usage in intelligent farming as well as the most challenges this promising topic is facing. The relevant recommendations and researches needed in Blockchain topic to enhance intelligent farming sustainability are also highlighted. It is found through the examination that the adoption of ICT in the various farming processes helps to increase productivity with low efforts and costs. Several challenges are faced when implementing such solutions, they are mainly related to the technological d...

Next-Generation Greenhouses for Food Security

Automation of greenhouse environment using simple timer-based actuators or by means of convention... more 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 cu...

Agricultural Robots - Fundamentals and Applications, 2019

In this research, an extreme learning machine (ELM) is proposed to predict the new COVID-19 cases... more In this research, an extreme learning machine (ELM) is proposed to predict the new COVID-19 cases in Algeria. In the present study, public health database from Algeria health ministry has been used to train and test the ELM models.The input parameters for the predictive models include Cumulative Confirmed COVID-19 Cases (CCCC), Calculated COVID-19 New Cases (CCNC), and Index Day (ID).The predictive accuracy of the seven models has been assessed via several statistical parameters. The results showed that the proposed ELM model achieved an adequate level of prediction accuracy with smallest errors (MSE= 0.16, RMSE=0.4114, and MAE= 0.2912), and highest performance’s (NSE = 0.9999, IO = 0.9988, R2 = 0.9999). Hence, the ELM model could be utilized as a reliable and accurate modeling approach for predicting the new COVIS-19 cases in Algeria.The proposed ELM model, it can be used as a decision support tool to manage public health medical efforts and facilities against the COVID-19 pandemic...

ABSTRACT Fuzzy systems are based on fuzzy set theory and fuzzy logic. They can be applied to a va... more ABSTRACT Fuzzy systems are based on fuzzy set theory and fuzzy logic. They can be applied to a variety of problem domains, from simple industrial control to systems that support human decision making. Fuzzy systems are an alternative to traditional notions of set membership and logic that has its origins in ancient Greek philosophy, and have applications at the leading edge of Artificial Intelligence. Yet, despite its long-standing origins, it is a relatively new field, and as such leaves much room for development. The thesis will present two applications of fuzzy systems; in human decision making and in industrial applications. In the first part, the thesis will present an application of fuzzy systems in Educational Evaluation (EE), along with some of the more advantages of its use, with an example drawn from current research in that field. Ultimately, it will be demonstrated that the use of fuzzy systems makes a viable addition to the field of Artificial Intelligence, and perhaps more generally to EE as a whole. In the second part, the thesis will present an application of fuzzy systems in industrial applications through the design and implementation of a more sophisticated control system which is able to detect and to handle uncertainties. Measurement and modeling uncertainties can be considered as a chronic disease which can not be avoided and control systems have to find a way to live with. Type-2 fuzzy logic system (T2FLS) is very well known medicine in handling the large amount of uncertainty and the incomplete information which is an inherent part of control systems used in real world application. However, it does not gain the expected attention it deserves because of its implementation complexity. So far, there is no or very few real applications of T2FLS in control system design and therefore I decided to find away to implement it using our basic knowledge of T1FLS. In Section A of the second part of the thesis, a simple architecture of T2FLS using four embedded type-1 fuzzy logic systems (T1FLSs) will be presented. The performance of the proposed architecture is assessed by applying it to a multi-input multi-output (MIMO) greenhouse climate control system. A genetic algorithm (GA) will be used to tune the controller parameters. The system will provide a way to show the amount of uncertainty in the system in terms of the width of the T2 fuzzy membership functions. If the system has no uncertainty, the control system will behave as a normal T1FLS, otherwise, the width of the membership functions will increase according to the amount of uncertainties in the system. In Section B of part 2 of the thesis, the problem of measurement and modeling uncertainty will be treated by using different way. In this section, the very well known Extended Kalman Filter is proposed to be an integral part of any control system. The objective is to improve the performance of the control system by using the estimated states by the EKF as the controlled variables instead of the noisy measured states. This approach is assessed by applying it to the MIMO greenhouse climate control problem and by comparing the results obtained by estimation of the states using the EKF and the results obtained by regular state feedback in terms of the Signal-to-Noise Ratio (SNR) and Mean Squares of Error (MSE). Simulation results show good performance and more economic savings; moreover, the Fault Detection and Isolation (FDI) systems became more robust against false alarms.

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2020

A throughput is a measure of successful data transmission through a terminal in a given time. The... more A throughput is a measure of successful data transmission through a terminal in a given time. There is a significant number of surveys that have been published to measure the performance of MAC protocols and each MAC channel. There is a substantial need to perform a comparative analysis of achieved results that can provide a comprehensible result for the end-users. In our work, we have applied different methods to obtain the throughput of contention-free and contention-based protocols. We have provided a brief comparison between different protocols of the same class to accentuate the efficient protocols. Our paper incorporates collision avoidance MAC channels, as the collision avoidance techniques provide better usability of protocols than the collision detection techniques. We have also proposed an efficient way to increase the life span of the Low-energy Adaptive Clustering Hierarchy (LEACH) protocol using Time Division Multiple Access (TDMA). The goal to obtain the maximum effectiveness of a network can be achieved by maximal utilization of the throughput. In this paper, we have provided a brief comparison of different MAC protocols by using the throughput parameter, and we have also proposed a new method to increase the efficiency of LEACH protocol.

ECMS 2020 Proceedings edited by Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther, 2020

Process Safety and Environmental Protection, 2022

Agricultural Robots - Fundamentals and Applications, 2019

2018 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2018

Driving heavy tractors and agricultural machinery on soil for growing plants causes more permanen... more Driving heavy tractors and agricultural machinery on soil for growing plants causes more permanent damage to the soil than was previously believed. Compacting the soil severely deteriorates soil fertility leading to poor crop yields and increased population from agricultural lands. In addition, compacted soils require many years of expensive treatment to recover their fertility. Soil compaction problem can be solved by replacing heavy tractors and machinery with a number of smaller and lighter vehicles that is capable of treating crop fields as well as heavy ones without compacting the soil. However, this scenario is impractical because of the high operating cost to secure a human operator/driver for each vehicle. A practical solution is to replace heavy tractors and machinery by a set of autonomous vehicles that require minimum or no human intervention. In this paper, the technology required to enable a single farmer to supervise and operate a team of automated vehicles, is proposed. This will include the development of a Mission Control Center and Intelligent Coverage Path Planning algorithms to enable team/swarm of autonomous vehicles to communicate and cooperate and solve a range of agricultural tasks in a safe and efficient way.

We have presented in this paper the design, control and implementation of a versatile low cost ma... more We have presented in this paper the design, control and implementation of a versatile low cost manipulator with arm gripper mounted on an existing unmanned ground vehicle. The major development in this work is the robust and efficient stable design of manipulator having four degrees of freedom capable of lifting up to 1.5 kg weight for various industrial and nonindustrial applications. The communication link is established using two human supervisory controlled wireless four channel 2.4GHz remote controllers, which are separately used for UGV and manipulator for effective maneuvering and control. The controlling of RC servo motors is made using Arduino Uno controller board. An on board solar panel is used for charging batteries run time during the day. A 50W, 18V standard solar panel is used to enhance the maneuvering time of UGV and manipulator. The unique feature of our UGV is its two rotating head on flippers capable of controlled maneuvering especially in uneven terrain surfaces...

In this paper, we propose the use of a robot platform for helping teachers and students in classr... more In this paper, we propose the use of a robot platform for helping teachers and students in classrooms and at homes without degrading the academic achievement. We hypothesized that the robot can teach the same contents without degrading the academic achievement. In addition, it can improve the quality of teaching, mixing teaching with fun to attract students’ attention, repeat the contents without fatigue or anger and also providing pupils with special needs with the right contents in the right time. In this paper a 24 degrees of freedom (DOF) NAO robot is used for teaching an introduction to robotics to elementary school pupils. A GUI tool is designed to help teachers to easily upload slides of the subject’s contents. The robot presents the slides by reading it while generating random movements that resemble the body language of human presenters and teachers. The robot uses its camera to count the number of faces heading to it and use it as a measure of attention. When the number go...

Applied Sciences

Journal of Clinical Medicine

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Digital Agriculture, Methods and Applications [Working Title]

Next-Generation Greenhouses for Food Security

Agricultural Robots - Fundamentals and Applications, 2019

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2020

ECMS 2020 Proceedings edited by Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther, 2020

Process Safety and Environmental Protection, 2022

Agricultural Robots - Fundamentals and Applications, 2019

2018 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2018

Communications in Computer and Information Science, 2021

Aust. J. Intell. Inf. Process. Syst., 2019

Violence usually spread online, as it has spread in the past. With the increasing use of social m... more Violence usually spread online, as it has spread in the past. With the increasing use of social media, the violence attributed to online hate speech has increased worldwide resulting rise in number of attacks on immigrants and other minorities. Analysis of such short text posts (e.g. tweets etc.) is valuable for identification of abusive language and hate speech. In this paper, we present Deep Context-Aware Embedding for the detection of Hate speech and abusive language on twitter. To improve the classification performance, we have enhanced the quality of the tweets by considering polsemy, syntax, semantic, OOV words as well as sentiment knowledge and concatenated to form input vector. We have used BiLSTM with attention modeling to identify tweet with hate speech. Experimental results showed significant improvement in the classification of tweets.

Examination is one of the common assessment methods to assess the level of knowledge of students.... more Examination is one of the common assessment methods to assess the level of knowledge of students. Assessment methods probably have a greater influence on how and what students learn than any other factor. Assessment is used to discriminate not only between different students but also between different levels of thinking. Due to the increasing trends in class sizes and limited resources for teaching, the need arises for exploring other assessment methods. Multiple-Choice Questions (MCQs) have been highlighted as the main way of coping with the large group teaching, ease of use, testing large number of students on a wide range of course material, in a short time and with low grading costs. MCQs have been criticised for encouraging surface learning and its unfairness. MCQs have a variety of scoring options; the most widely used method is to compute the score by only focusing on the responses that the student made. In this case, the number of correct responses is counted, the number of incorrect answers is counted and a final score is reported as either the number of the correct answers or the number of correct answers minus the number of incorrect answers. The disadvantages of this approach are that other dimensions such as importance and complexity of questions are not considered, and in addition, it cannot discriminate between students with equal total score. In this paper, a method to automatically evaluate MCQs considering importance and complexity of each question and providing a fairer way to discriminating between students with equal total scores is presented.

Digital farming is the practice of modern technologies such as sensors, robotics, and data analys... more Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from tedious operations to continuously automated processes. This paper reviews some of the latest achievements in agricultural robotics, specifically those that are used for autonomous weed control, field scouting, and harvesting. Object identification, task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information. For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators. While robots are becoming the inseparable parts of the modern farms, our conclusion is that it is not realistic to expect an entirely automated farming system in the future.

International Journal of Agricultural and Biological Engineering, 2018

Research efforts for development of agricultural robots that can effectively perform tedious fiel... more Research efforts for development of agricultural robots that can effectively perform tedious field tasks have grown significantly in the past decade. Agricultural robots are complex systems that require interdisciplinary collaborations between different research groups for effective task delivery in unstructured crops and plants environments. With the exception of milking robots, the extensive research works that have been carried out in the past two decades for adaptation of robotics in agriculture have not yielded a commercial product to date. To accelerate this pace, simulation approach and evaluation methods in virtual environments can provide an affordable and reliable framework for experimenting with different sensing and acting mechanisms in order to verify the performance functionality of the robot in dynamic scenarios. This paper reviews several professional simulators and custom-built virtual environments that have been used for agricultural robotic applications. The key features and performance efficiency of three selected simulators were also compared. A simulation case study was demonstrated to highlight some of the powerful functionalities of the Virtual Robot Experimentation Platform. Details of the objects and scenes were presented as the proof-of-concept for using a completely simulated robotic platform and sensing systems in a virtual citrus orchard. It was shown that the simulated workspace can provide a configurable and modular prototype robotic system that is capable of adapting to several field conditions and tasks through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. This review suggests that an open-source software platform for agricultural robotics will significantly accelerate effective collaborations between different research groups for sharing existing workspaces, algorithms, and reusing the materials.

In modern agriculture, there is a high demand to move from tedious manual harvesting to a continu... more In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose output were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish and subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator.

Greenhouse cultivation has evolved from simple covered rows of open-fields crops to highly sophis... more Greenhouse cultivation has evolved from simple covered rows of open-fields crops to highly sophisticated controlled environment agriculture (CEA) facilities that projected the image of plant factories for urban farming. The advances and improvements in CEA have promoted the scientific solutions for the efficient production of plants in populated cities and multi-story buildings. Successful deployment of CEA for urban farming requires many components and subsystems, as well as the understanding of the external influencing factors that should be systematically considered and integrated. This review is an attempt to highlight some of the most recent advances in greenhouse technology and CEA in order to raise the awareness for technology transfer and adaptation, which is necessary for a successful transition to urban farming. This study reviewed several aspects of a high-tech CEA system including improvements in the frame and covering materials, environment perception and data sharing, and advanced microclimate control and energy optimization models. This research highlighted urban agriculture and its derivatives, including vertical farming, rooftop greenhouses and plant factories which are the extensions of CEA and have emerged as a response to the growing population, environmental degradation, and urbanization that are threatening food security. Finally, several opportunities and challenges have been identified in implementing the integrated CEA and vertical farming for urban agriculture.