Abstract
Greenhouse control computers are an essential part of modern greenhouse operation. Climate, irrigation and nutrient supply must be controlled, in an economical way, to produce the best crop conditions. Current research on CO2 enrichment and optimal growth strategies implies the use of powerful tools, either based on hardware or software. This paper describes the design and implementation of a distributed data acquisition and control system for computerised agricultural management systems that is being developed at Universidade of Tras-os-Montes and Alto Douro, Vila Real. Different communications platform concepts, such as Controller Area Network (CAN), Wireless Technologies, Ethernet and Internet tools supported by Transfer Control Protocol/Internet Protocol (TCP/IP) and e-mail tools supported by Simple Mail Transfer Protocol (SMTP) were used to achieve a network with a low-cost, flexible, and functional characteristics. The system management and maintenance tasks are divided into two types of performance levels. At a lower supervision level, a Local Controller placed in the greenhouses performs the monitoring/control and communications actions. The management decisions are performed at the higher level. The techniques and tools, which provide to the user a transparent, friendly and intuitive Graphical User Interface (GUI) will be presented.

Abstract
Crop models are useful to improve the management and control of the greenhouse climate. In fact, they are essential if it is intended to optimize the climate control according to a defined production plan. In this paper are reviewed simulation models developed for horticultural crops with emphasis on leaf area and dry matter production. The dynamic crop models are based on the description of the crop photosynthesis and respiration processes. Various models were tested for a tomato crop grown in a greenhouse with a soil medium. The environmental factors such as air temperature and humidity, photosynthetic flux density, carbon dioxide concentration, among others, were measured, with a sampling rate of 1 minute and the mean values were stored with a 30min time interval. Plant leaf area and fresh and dry weights were measured by conducing periodic destructive tests. The results achieved with the different models employed are discussed and their applicability addressed.

Abstract
This paper describes the 5dpo team. The paper will be divided into three main sections, corresponding to three main blocks: the Global Level, the Local Level and the Interface Level. These Levels, their subsystems and some implementation details will be described next.

Abstract
This paper describes the method employed to track and identify each robot during a Robocup match. Also, the playing ball is tracked with almost no extra processing effort. To track the robots it is necessary the use of adequate markers so that not only the position is extracted but also the heading. We discuss the difficulties associated with this problem, various possible approaches and justify our solution. The identification is performed thanks to a minimalist bar code placed in each robot. The bar code solves the problem of resolving some ambiguities that can arise in certain configurations. The procedure described can be executed in real time as it was shown in Paris in RoboCup-98.

Abstract
This paper describes the 5dpo-2000 team, The paper will be divided into three main sections, corresponding to three main blocks: the Global Level, the Local Level and the Interface Level. These Levels, their subsystems and some implementation details will be described next.

Abstract
An adaptive greenhouse climate controller was implemented to regulate the air temperature, humidity and carbon dioxide concentration, with the aim of achieving set-point accuracy and reduce energy consumption. An optimization algorithm, based on the minimization of a cost function, was used to tune a Proportional-Integral-Derivative controller. The cost function is computed over a future time horizon of one hour as a function of the errors between the predicted and desired outputs and the predicted energy demand. Since the controller must be able to predict the greenhouse climate, it was needed to employ recursive identification algorithms to estimate in real-time the parameters of the climate model. When compared with commercially available controllers, this adaptive controller proved to have better performance regarding set-point accuracy and energy consumption.