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.

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, 94% using SVM and 63% using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.

Protein phosphorylation on serine (S) and threonine (T) has emerged as a key device in the control of many biological processes. Recently phosphorylation in microbial organisms has attracted much attention for its critical roles in various cellular processes such as cell growth and cell division. Here a novel machine learning predictor, MPSite (Microbial Phosphorylation Site predictor), was developed to identify microbial phosphorylation sites using the enhanced characteristics of sequence features. The final feature vectors optimized via a Wilcoxon rank sum test. A random forest classifier was then trained using the optimum features to build the predictor. Benchmarking investigation using the 5-fold cross-validation and independent datasets test showed that the MPSite is able to achieve robust performance on the S- and T-phosphorylation site prediction. It also outperformed other existing methods on the comprehensive independent datasets. We anticipate that the MPSite is a powerful tool for proteome-wide prediction of microbial phosphorylation sites and facilitates hypothesis-driven functional interrogation of phosphorylation proteins. A web application with the curated datasets is freely available at http://kurata14.bio.kyutech.ac.jp/MPSite/ .

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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.

Mining of sensor data for useful knowledge extraction is a very challenging task. Existing works generate sensor association rules using occurrence frequency of patterns to extract the knowledge. These techniques often generate huge number of rules, most of which are non-informative or fail to reflect true correlation among sensor data. In this paper, we propose a new type of behavioral pattern called associated sensor patterns which capture association-like co-occurrences as well as temporal correlations which are linked with such co-occurrences. To capture such patterns a compact tree structure, called associated sensor pattern tree (ASP-tree) and a mining algorithm (ASP) are proposed which use pattern growth-based approach to generate all associated patterns with only one scan over dataset. Moreover, when data stream flows through, old information may lose significance for the current time. To capture significance of recent data, ASP-tree is further enhanced to SWASP-tree by adopting sliding observation window and updating the tree structure accordingly. Finally, window size is made dynamically adaptive to ensure efficient resource usage. Different characteristics of the proposed techniques and their computational complexity are presented. Experimental results show that our approach is very efficient in discovering associated sensor patterns and outperforms existing techniques.

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