AURORA:autonomous real-time on-board video analytics

In this paper, we describe the design and implementation of a small light weight, low-cost and power-efficient payload system for the use in unmanned aerial vehicles (UAVs). The primary application of the payload system is that of performing real-time autonomous objects detection and tracking in the videos taken from a UAV camera. The implemented objects detection and tracking algorithms utilise Recursive Density Estimation (RDE) and Evolving Local Means (ELM) clustering to perform detection and tracking moving objects. Furthermore, experiments are presented which demonstrate that the introduced system is able to detect by on-board processing any moving objects from a UAV and start tracking them in real-time while at the same time sending important data only to a control station located on the ground

A nested hierarchy of dynamically evolving clouds for big data structuring and searching

The need to analyse big data streams and prescribe actions pro-actively is pervasive in nearly every industry. As growth of unstructured data increases, using analytical systems to assimilate and interpret images and videos as well as interpret structured data is essential. In this paper, we proposed a novel approach to transform image dataset into higher-level constructs that can be analysed more computationally efficiently, reliably and extremely fast. The proposed approach provides a high visual quality result between the query image and data clouds with hierarchical dynamically nested evolving structure. The results illustrate that the introduced approach can be an effective yet computationally efficient way to analyse and manipulate storedimages which has become the centre of attention of many professional fields and institutional sectors over the last few years

Look-a-like:a fast content-based image retrieval approach using a hierarchically nested dynamically evolving image clouds and recursive local data density

The need to find related images from big data streams is shared by many professionals, such as architects, engineers, designers, journalist, and ordinary people. Users need to quickly find the relevant images from data streams generated from a variety of domains. The challenges in image retrieval are widely recognised and the research aiming to address them led to the area of CBIR becoming a 'hot' area. In this paper, we propose a novel computationally efficient approach which provides a high visual quality result based on the use of local recursive density estimation (RDE) between a given query image of interest and data clouds/clusters which have hierarchical dynamically nested evolving structure. The proposed approach makes use of a combination of multiple features. The results on a data set of 65,000 images organised in two layers of an hierarchy demonstrate its computational efficiency. Moreover, the proposed Look-a-like approach is self-evolving and updating adding new images by crawling and from the queries made

Time-intensive geoelectrical monitoring under winter wheat

Several studies have explored the potential of electrical resistivity tomography to monitor changes in soil moisture associated with the root water uptake of different crops. Such studies usually use a set of limited below-ground measurements throughout the growth season but are often unable to get a complete picture of the dynamics of the processes. With the development of high-throughput phenotyping platforms, we now have the capability to collect more frequent above-ground measurements, such as canopy cover, enabling the comparison with below-ground data. In this study hourly DC resistivity data were collected under the Field Scanalyzer platform at Rothamsted Research with different winter wheat varieties and nitrogen treatments in 2018 and 2019. Results from both years demonstrate the importance of applying the temperature correction to interpret hourly electrical conductivity (EC) data. Crops which received larger amounts of nitrogen showed larger canopy cover and more rapid changes in EC, especially during large rainfall events. The varieties showed contrasted heights although this does not appear to have influenced EC dynamics. The daily cyclic component of the EC signal was extracted by decomposing the time series. A shift in this daily component was observed during the growth season. For crops with appreciable difference in canopy cover, high frequency DC resistivity monitoring was able to distinguish the different below-ground behaviors. The results also highlight how coarse temporal sampling may affect interpretation of resistivity data from crop monitoring studies

Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages

Machine learning methods for automatic segmentation of images of field-and glasshouse-based plants for high-throughput phenotyping

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green–red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness

A Fast Recursive Approach to Autonomous Detection, Identification and Tracking of Multiple Objects in Video Streams under Uncertainties

Real-time processing the information coming form video, infra-red or electro-optical sources is a challenging task due the uncertainties such as noise and clutter, but also due to the large dimensionalities of the problem and the demand for fast and efficient algorithms. This paper details an approach for automatic detection, single and multiple objects identification and tracking in video streams with applications to surveillance, security and autonomous systems. It is based on a method that provides recursive density estimation (RDE) using a Cauchy type of kernel. The main advantage of the RDE approach as compared to other traditional methods (e.g. KDE) is the low computational and memory storage cost since it works on a frame-by-frame basis; the lack of thresholds, and applicability to multiple objects identification and tracking. A robust to noise and clutter technique based on spatial density is also proposed to autonomously identify the targets location in the frame

A Real-time Approach for Novelty Detection and Trajectories Analysis for Anomaly Recognition in Video Surveillance Systems

In this paper, we present a novel approach for automatic object detection and also using on-line trajectory clustering for RT anomaly detection in video streams. The proposed approach is based on two main steps. In the first step, a recently introduced approach called Recursive Density Estimation (RDE) is used for novelty detection. This method is using a Cauchy type of kernel which works on a frame-by-frame basis and does not require a pre-defined threshold to identify objects. In the second step, multifeature object trajectory is clustered on-line to identify anomalies in video streams. To identify an anomaly, first the trajectories are transformed into a set of features in a space to which eClustering approach identifies the modes and the corresponding clusters. At the end, by using cluster fusion the final common pattern is estimated and any sparse trajectories are considered as anomalous

Online Self-Evolving Fuzzy Controller for Autonomous Mobile Robots

In this paper, an online self-evolving fuzzy controller is proposed for an autonomous leader/follower. The self-evolving controller starts with a simple configuration and learns from its own actions while controlling the mobile robot during the leader following behaviour. A traditional Takagi-Sugeno type fuzzy controller is also implemented and compared with the proposed controller to verify the reliability and performance of the self-evolving controller. Experiments are carried out with a real mobile robot Pioneer 3DX at Lancaster University