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This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF,
Nahina Islam; Mamunur Rashid; Santoso Wibowo; Cheng-Yuan Xu; Ahsan Morshed; Saleh Wasimi; Steven Moore; Sk Rahman. Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm. Agriculture 2021, 11, 387 .
AMA StyleNahina Islam, Mamunur Rashid, Santoso Wibowo, Cheng-Yuan Xu, Ahsan Morshed, Saleh Wasimi, Steven Moore, Sk Rahman. Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm. Agriculture. 2021; 11 (5):387.
Chicago/Turabian StyleNahina Islam; Mamunur Rashid; Santoso Wibowo; Cheng-Yuan Xu; Ahsan Morshed; Saleh Wasimi; Steven Moore; Sk Rahman. 2021. "Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm." Agriculture 11, no. 5: 387.
To reach the goal of sustainable agriculture, smart farming is taking advantage of the Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) paradigm. These smart farms are designed to be run by interconnected devices and vehicles. Some enormous potentials can be achieved by the integration of different IoT technologies to achieve automated operations with minimum supervision. This paper outlines some major applications of IoT and UAV in smart farming, explores the communication technologies, network functionalities and connectivity requirements for Smart farming. The connectivity limitations of smart agriculture and it’s solutions are analysed with two case studies. In case study-1, we propose and evaluate meshed Long Range Wide Area Network (LoRaWAN) gateways to address connectivity limitations of Smart Farming. While in case study-2, we explore satellite communication systems to provide connectivity to smart farms in remote areas of Australia. Finally, we conclude the paper by identifying future research challenges on this topic and outlining directions to address those challenges.
Nahina Islam; Mamunur Rashid; Faezeh Pasandideh; Biplob Ray; Steven Moore; Rajan Kadel. A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming. Sustainability 2021, 13, 1821 .
AMA StyleNahina Islam, Mamunur Rashid, Faezeh Pasandideh, Biplob Ray, Steven Moore, Rajan Kadel. A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming. Sustainability. 2021; 13 (4):1821.
Chicago/Turabian StyleNahina Islam; Mamunur Rashid; Faezeh Pasandideh; Biplob Ray; Steven Moore; Rajan Kadel. 2021. "A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming." Sustainability 13, no. 4: 1821.
In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.
Mamunur Rashid; Joarder Kamruzzaman; Mohammad Mehedi Hassan; Tasadduq Imam; Steven Gordon. Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques. International Journal of Environmental Research and Public Health 2020, 17, 9347 .
AMA StyleMamunur Rashid, Joarder Kamruzzaman, Mohammad Mehedi Hassan, Tasadduq Imam, Steven Gordon. Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques. International Journal of Environmental Research and Public Health. 2020; 17 (24):9347.
Chicago/Turabian StyleMamunur Rashid; Joarder Kamruzzaman; Mohammad Mehedi Hassan; Tasadduq Imam; Steven Gordon. 2020. "Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques." International Journal of Environmental Research and Public Health 17, no. 24: 9347.
The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area.
Mamunur Rashid; Joarder Kamruzzaman; Mohammad Mehedi Hassan; Sakib Shahriar Shafin; Zakirul Alam Bhuiyan. A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things. IEEE Access 2020, 8, 33318 -33341.
AMA StyleMamunur Rashid, Joarder Kamruzzaman, Mohammad Mehedi Hassan, Sakib Shahriar Shafin, Zakirul Alam Bhuiyan. A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things. IEEE Access. 2020; 8 (99):33318-33341.
Chicago/Turabian StyleMamunur Rashid; Joarder Kamruzzaman; Mohammad Mehedi Hassan; Sakib Shahriar Shafin; Zakirul Alam Bhuiyan. 2020. "A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things." IEEE Access 8, no. 99: 33318-33341.