See-and-avoid quadcopter using fuzzy control optimized by cross-entropy

In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of cross- entropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights

Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses

Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production

A model-free control strategy for an experimental greenhouse with an application to fault accommodation

Writing down mathematical models of agricultural greenhouses and regulating them via advanced controllers are challenging tasks since strong perturbations, like meteorological variations, have to be taken into account. This is why we are developing here a new model-free control approach and the corresponding intelligent controllers, where the need of a good model disappears. This setting, which has been introduced quite recently and is easy to implement, is already successful in many engineering domains. Tests on a concrete greenhouse and comparisons with Boolean controllers are reported. They not only demonstrate an excellent climate control, where the reference may be modified in a straightforward way, but also an efficient fault accommodation with respect to the actuators

Automatic Climate Control of a Greenhouse: A Review

Greenhouse crop production was a very significant event in the history of agriculture since it was realized that with the help of it many plants could be protected from different biotic and abiotic stress. It emerged as a system to protect crops from critical and adverse conditions affecting the growth of plants. The greenhouse is a non-linear system and controlling becomes a difficult task. The parameters affecting the plant growth are temperature, relative humidity, carbon dioxide, nutrition, availability of water and the growing media. The quality and productivity of the crop plants is highly dependent on the management of these parameters. From all the parameters, temperature and humidity are of primary importance to most growers as it is responsible for determining the reaction rates of various metabolic processes involved in the plant growth. In addition, regulating temperature has a direct influence on the relative humidity and carbon dioxide levels of the greenhouse system

A genetic algorithm based fuzzy-tuned neural network

Author name used in this publication: F. H. F. LeungAuthor name used in this publication: Y. S. LeeCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2002-2003 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

On interpretation of graffiti digits and characters for eBooks : neural-fuzzy network and genetic algorithm approach

Author name used in this publication: K. F. LeungCentre for Multimedia Signal Processing, Department of Electronic and Information Engineering2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

A novel genetic-algorithm-based neural network for short-term load forecasting

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

On interpretation of Graffiti digits and characters for eBooks: Neural-fuzzy network and genetic algorithm approach

This paper presents the rule optimization, tuning of the membership functions, and optimization of the number of fuzzy rules, of a neural-fuzzy network (NFN) using a genetic algorithm (GA). The objectives are achieved by training a proposed NFN with rule switches. The proposed NFN and GA are employed to interpret graffiti number inputs and commands for electronic books (eBooks)