Detection, Quantification and Classification of Ripened Tomatoes: A Comparative Analysis of Image Processing and Machine Learning

In this paper, specifically for detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared. One is a machine learning approach, known as ‘Cascaded Object Detector’ and the other is a composition of traditional customized methods, individually known as ‘Colour Transformation’, ‘Colour Segmentation’ and ‘Circular Hough Transformation’. The (Viola Jones) Cascaded Object Detector generates ‘histogram of oriented gradient’ (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, color thresholding is applied to detect tomatoes either from a natural or solid background and RGB colour is adjusted to identify ripened tomatoes. In this work, Colour Segmentation is applied in the detection of tomatoes with defects, which has not previously been applied under machine learning techniques. The function modules of this algorithm are fed formatted images, captured by a camera mounted on a mobile robot. This robot was designed, built and operated in a tomato field to identify and quantify both green and ripened tomatoes as well as to detect damaged/blemished ones. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of O(n3) for traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB. This result is potentially significant for farmers in crop fields to identify the condition of tomatoes quickly

Smart Agriculture Monitoring System Using Hybrid Energy Harvesting Internet of Things

Early researches went for detection or counting algorithms for crops and fruits using costly, bulky and limited sensor power. This research aims to develop algorithm for identification and quantification of crops which has direct economic value in the research fields with a hybrid energy harvesting agriculture monitoring system

A Scalable and Secure MANET for an i-Voting System

Internet Voting (i-Voting) is an online electronic voting process where a voter can vote staying online from anywhere or connected to a wireless network of a target place. In this paper, a wireless network built with a MANET has been considered for the voting process. National parliamentary voting process of Bangladesh has been taken as the case study. The MANET of the voting process is built using some stationary wireless nodes and mobile wireless nodes. Voters carry mobile wireless nodes using which they can vote. Stationary wireless nodes are installed and deployed in the MANET built in a polling area selected by the National Agency of Election process. These nodes are directly in connection with the national database of voters. Stationary nodes perform the authentication and validation processes of the voter (a mobile node) before the vote is given and casted. The secured transaction of data is the goal to be occurred and routed after a strong authentication and validation of the user has been confirmed. The whole process is completed in a scalable wireless network with a distributed goal based approach. Total processes are followed by secured routing of data in this MANET. The optimal routing protocol among OLSR, AODV, DSR, TORA and GRP has been chosen. Denial of Service (DoS) attacks have been considered as the major threat on nodes in this MANET. The simulation work is done in the OPNET simulator.Validerad;2017;Nivå 1;2017-10-04 (andbra)A belief-rule-based DSS to assess flood risks by using wireless sensor network

A Scalable and Secure MANET for an i-Voting System

Internet Voting (i-Voting) is an online electronic voting process where a voter can vote staying online from anywhere or connected to a wireless network of a target place. In this paper, a wireless network built with a MANET has been considered for the voting process. National parliamentary voting process of Bangladesh has been taken as the case study. The MANET of the voting process is built using some stationary wireless nodes and mobile wireless nodes. Voters carry mobile wireless nodes using which they can vote. Stationary wireless nodes are installed and deployed in the MANET built in a polling area selected by the National Agency of Election process. These nodes are directly in connection with the national database of voters. Stationary nodes perform the authentication and validation processes of the voter (a mobile node) before the vote is given and casted. The secured transaction of data is the goal to be occurred and routed after a strong authentication and validation of the user has been confirmed. The whole process is completed in a scalable wireless network with a distributed goal based approach. Total processes are followed by secured routing of data in this MANET. The optimal routing protocol among OLSR, AODV, DSR, TORA and GRP has been chosen. Denial of Service (DoS) attacks have been considered as the major threat on nodes in this MANET. The simulation work is done in the OPNET simulator.Validerad;2017;Nivå 1;2017-10-04 (andbra)A belief-rule-based DSS to assess flood risks by using wireless sensor network

Optimal Dynamic Routing Protocols for Agro-Sensor Communication in MANETs

Recent developments in the area of Wireless sensor networks and Mobile ad hoc networks provide flexible and easy- to-deploycommunication means for a wide range of appli- cations without any need for an infrastructure being pre-con- figured. Our paper studies performance of proactive and reactive routing protocols in a scenario with agro-sensors. Our results, achieved by simulating a network both in OPNET Modeler and NS2, show that the AODV routing protocol performs better for a large-scale network (where node density is higher) while the DSR routing protocol performs better in a small-scale network given the particular scenario we studied

Selection of Energy Efficient Routing Protocol for Irrigation Enabled by Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are playing remarkable contribution in real time decision making by actuating the surroundings of environment. As a consequence, the contemporary agriculture is now using WSNs technology for better crop production, such as irrigation scheduling based on moisture level data sensed by the sensors. Since WSNs are deployed in constraints environments, the life time of sensors is very crucial for normal operation of the networks. In this regard routing protocol is a prime factor for the prolonged life time of sensors. This research focuses the performances analysis of some clustering based routing protocols to select the best routing protocol. Four algorithms are considered, namely Low Energy Adaptive Clustering Hierarchy (LEACH), Threshold Sensitive Energy Efficient sensor Network (TEEN), Stable Election Protocol (SEP) and Energy Aware Multi Hop Multi Path (EAMMH). The simulation is carried out in Matlab framework by using the mathematical models of those algortihms in heterogeneous environment. The performance metrics which are considered are stability period, network lifetime, number of dead nodes per round, number of cluster heads (CH) per round, throughput and average residual energy of node. The experimental results illustrate that TEEN provides greater stable region and lifetime than the others while SEP ensures more througput.A belief-rule-based DSS to assess flood risks by using wireless sensor network

Estimation of Signal Coverage and Localization in Wi-Fi Networkswith AODV and OLSR

For estimation of signal coverage and localization, path loss is the major component for link budget of any communication system. Instead of traditional Doppler shift or Doppler spread techniques, the path loss has been chosen for IEEE 802.11 (Wi-Fi) signals of 2.5 and 5 GHz to measure the signal coverage and localization in this research. A Wi-Fi system was deployed in a MANET (Mobile Adhoc NETwork), involving both mobile and stationary nodes. The Adhoc network was also assessed in a routing environment under AODV and OLSR protocols. The proposal was evaluated using the OPNET Modeler simulation environment.Validerad;2018;Nivå 1;2018-10-19 (marisr)A belief-rule-based DSS to assess flood risks by using wireless sensor network

Performance analysis of a surveillance system to detect and track vehicles using Haar cascaded classifiers and optical flow method

This paper presents the real time vehicle detection and tracking system, based on data, collected from a single camera. In this system, vehicles are detected by using Haar Feature-based Cascaded Classifier on static images, extracted from the video file. The advantage of this classifier is that, it uses floating numbers in computations and hence, 20% more accuracy can be achieved in comparison to other classifiers and features of classifiers such as LBP (Local Binary Pattern). Tracking of the vehicles is carried out using Lucas-Kanade and Horn Schunk Optical Flow method because it performs better than other methods such as Morphological and Correlation Transformations. The proposed system consists of vehicle detection and tracking; and it is evaluated by using real data, collected from the route networks of Chittagong City of Bangladesh.ISBN för värdpublikation: 978-1-5090-6162-4, 978-1-5090-6161-7, 978-1-5386-2103-5A belief-rule-based DSS to assess flood risks by using wireless sensor network

Performance Analysis of Anomaly Based Network Intrusion Detection Systems

Because of the increased popularity and fast expansion of the Internet as well as Internet of things, networks are growing rapidly in every corner of the society. As a result, huge amount of data is travelling across the computer networks that lead to the vulnerability of data integrity, confidentiality and reliability. So, network security is a burning issue to keep the integrity of systems and data. The traditional security guards such as firewalls with access control lists are not anymore enough to secure systems. To address the drawbacks of traditional Intrusion Detection Systems (IDSs), artificial intelligence and machine learning based models open up new opportunity to classify abnormal traffic as anomaly with a self-learning capability. Many supervised learning models have been adopted to detect anomaly from networks traffic. In quest to select a good learning model in terms of precision, recall, area under receiver operating curve, accuracy, F-score and model built time, this paper illustrates the performance comparison between Naïve Bayes, Multilayer Perceptron, J48, Naïve Bayes Tree, and Random Forest classification models. These models are trained and tested on three subsets of features derived from the original benchmark network intrusion detection dataset, NSL-KDD. The three subsets are derived by applying different attributes evaluator’s algorithms. The simulation is carried out by using the WEKA data mining tool.A belief-rule-based DSS to assess flood risks by using wireless sensor network

Development of Algorithms for an IoT-Based Smart Agriculture Monitoring System

Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops