E.J. van Henten

An Autonomous Robot for Harvesting Cucumbers in Greenhouses

This paper describes the concept of an autonomous robot for harvesting cucumbers in greenhouses. A description is given of the working environment of the robot and the logistics of harvesting. It is stated that for a 2 ha Dutch nursery, 4 harvesting robots and one docking station are needed during the peak season. Based on these preliminaries, the design specifications of the harvest robot are defined. The main requirement is that a single harvest operation may take at most 10 s. Then, the paper focuses on the individual hardware and software components of the robot. These include, the autonomous vehicle, the manipulator, the end-effector, the two computer vision systems for detection and 3D imaging of the fruit and the environment and, finally, a control scheme that generates collision-free motions for the manipulator during harvesting. The manipulator has seven degrees-of-freedom (DOF). This is sufficient for the harvesting task. The end-effector is designed such that it handles the soft fruit without loss of quality. The thermal cutting device included in the end-effector prevents the spreading of viruses through the greenhouse. The computer vision system is able to detect more than 95% of the cucumbers in a greenhouse. Using geometric models the ripeness of the cucumbers is determined. A motion planner based on the A*-search algorithm assures collision-free eye-hand co-ordination. In autumn 2001 system integration took place and the harvesting robot was tested in a greenhouse. With a success rate of 80%, field tests confirmed the ability of the robot to pick cucumbers without human interference. On average the robot needed 45 s to pick one cucumber. Future research focuses on hardware and software solutions to improve the picking speed and accuracy of the eye-hand co-ordination of the robot.

Sensing Soil Properties in the Laboratory, In Situ, and On-Line: A Review

Abstract Since both the spatial and vertical heterogeneities in soil properties have an impact on crop growth and yield, accurate characterization of soil properties at high sampling resolution is a preliminary step in successful management of soil-water-plant system. Conventional soil sampling and analyses have shown mixed economical returns due to the high costs associated with labor-intensive sampling and analysis procedures, which might be accompanied with map uncertainties. Therefore, the conventional laboratory methods are being replaced or complemented with the analytical soil sensing techniques. The objective of this chapter is to review different soil sensing methods used to characterize key soil properties for management of soil-water-plant system. This will cover laboratory, in situ in the field, and on-line measurement methods. This review chapter is furnished with an overview of background information about a sensing concept, basic principle and brief theory, various factors affecting the output of the sensor, and justification of why specific soil properties can be related with its output. The literature review is succeeded with an integration and analysis of findings in view of application in the precision agriculture domain. Potentials and limitations of current sensor technologies are discussed and compared with commonly used state-of-the-art laboratory techniques. As sensing is commonly addressed as a very technical discipline, the match between the information currently collected with sensors and those required for site-specific application of different inputs, and crop growth and development is discussed, highlighting the most accurate method to measure a soil property for a given application.

Release of lipoxygenase products and monoterpenes by tomato plants as an indicator of Botrytis cinerea-induced stress

Changes in emission of volatile organic compounds (VOCs) from tomato induced by the fungus Botrytis cinerea were studied in plants inoculated by spraying with suspensions containing B. cinerea spores. VOC emissions were analysed using on-line gas chromatography-mass spectrometry, with a time resolution of about 1 h, for up to 2 days after spraying. Four phases were delimited according to the starting point and the applied day/night rhythm of the experiments. These phases were used to demonstrate changes in VOC flux caused by B. cinerea infestation. Tomato plants inoculated with B. cinerea emitted a different number and amount of VOCs after inoculation compared to control plants that had been sprayed with a suspension without B. cinerea spores. The changes in emissions were dependent on time after inoculation as well as on the severity of infection. The predominant VOCs emitted after inoculation were volatile products from the lipoxygenase pathway (LOX products). The increased emission of LOX products proved to be a strong indicator of a stress response, indicating that VOC emissions can be used to detect plant stress at an early stage. Besides emission of LOX products, there were also increases in monoterpene emissions. However, neither increased emission of LOX products nor of monoterpenes is specific for B. cinerea attack. The emission of LOX products is also induced by other stresses, and increased emission of monoterpenes seems to be the result of mechanical damage induced by secondary stress impacts on leaves.

Detection of diseased plants by analysis of volatile organic compound emission.

This review focuses on the detection of diseased plants by analysis of volatile organic compound (VOC) emissions. It includes an overview of studies that report on the impact of infectious and noninfectious diseases on these emissions and discusses the specificity of disease-induced emissions. The review also provides an overview of processes that affect the gas balance of plant volatiles, including their loss processes. These processes are considered as important because they contribute to the time-dynamic concentration profiles of plant-emitted volatiles. In addition, we describe the most popular techniques currently in use to measure volatiles emitted from plants, with emphasis on agricultural application. Dynamic sampling coupled with gas chromatography and followed by an appropriate detector is considered as the most appropriate method for application in agriculture. It is recommended to evaluate the state-of-the-art in the fields concerned with this method and to explore the development of a new instrument based on the specific needs for application in agricultural practice. However, to apply such an instrument in agriculture remains a challenge, mainly due to high costs.

Sensitivity Analysis of an Optimal Control Problem in Greenhouse Climate Management

Optimal control systems are based on a performance measure to be optimised and a model description of the dynamic process to be controlled. When on-line implementation is considered, the performance of optimally controlled processes will depend on the accuracy of the model description used. Sensitivity analysis offers insight into the impact of uncertainty in the model parameters on the performance of the optimally controlled process. Additionally, sensitivity analysis may reveal the mechanisms underlying optimal process operation. This paper describes the methodology and results of a sensitivity analysis of an optimal control problem in greenhouse climate management. The methodology used, is based on variational arguments and requires a single solution of the optimal control problem, resulting in a computationally efficient technique. The example considered deals with economic optimal greenhouse climate management during the cultivation of a lettuce crop. The sensitivity analysis produced valuable insight into the performance sensitivity and operation of the controlled process. Both the model description of crop growth and production as well as the outside climate conditions have a strong impact on the performance. Humidity control plays a dominant role in economic optimal greenhouse climate management, emphasising the need for an accurate description of humidity effects on crop growth and production, either in terms of quantitative models or time-varying constraints on the humidity level in the greenhouse. Finally, the study revealed that the dynamic response times in the greenhouse climate are not limiting factors for economic optimal greenhouse climate control.

Colour based detection of volunteer potatoes as weeds in sugar beet fields using machine vision

The possible spread of late blight from volunteer potato plants requires the removal of these plants from arable fields. Because of high labour, energy, and chemical demands, a method of automatic detection and removal is needed. The development and comparison of two colour-based machine vision algorithms for in-field volunteer potato plant detection in two sugar beet fields are discussed. Evaluation of the results showed that both methods gave closely matched results within fields, although large differences exist between the fields. At plant level, in one field up to 97% of the volunteer potato plants were correctly classified. In another field, only 49% of the volunteer plants were correctly identified. The differences between the fields were higher than the differences between the methods used for plant classification.

Optimal control of greenhouse climate.

Abstract Using mathematical process models and dynamic optimization theory optimal temperature and CO2 strategies for the cultivation of a lettuce crop have been calculated. The application of long term mean weather data as weather forecast results in a lower energy and CO2 consumption compared to static strategies containing no information about the weather. The applicability of the optimal strategies as open-loop control of a real process depends on the differences between the expected and the actual outdoor climate, modelling errors and the accuracy of the climate control. The effect of an inaccurate long term weather forecast is analyzed. Repeated optimization, in literature reffered to as a way to cope with differences between the expected and the actual weather, does not lead to a lower energy and CO2 consumption.

Optimal Control of Greenhouse Climate: Computation of the Influence of Fast and Slow Dynamics

Abstract In case of optimal greenhouse climate control the fast greenhouse dynamics are generally ignored. Only the slow dynamics that describe the crop behaviour are considered. Through the computation of optimal climlate controls for growing lettuce in greenhouses, subjected to actual weather, it is demonstrated that the neglect of the greenhouse dynamics seriously affects the result (net profit).

ON-LINE ESTIMATION OF THE VENTILATION RATE OF GREENHOUSES

Abstract, accepted for the 24th Benelux Meeting, March 22-24, 2005, Houffalize, Belgium On-line estimation of the ventilation rate of greenhouses using an unknown input observer Jan Bontsema, Eldert van Henten, Jan Kornet Jorrit Budding, Theo Rieswijk Agrotechnology and Food Innovations B.V. P.O. Box 17, 6700 AA Wageningen The Netherlands Email: Jan.Bontsema@wur.nl Priva B.V. P.O. Box 18, 2678 ZG De Lier The Netherlands Email: T.Rieswijk@priva.nl 1 Introduction In modern greenhouse horticulture, the climate in the greenhouse is controlled by a sophisticated greenhouse climate computer. The role of the grower is to define among others temperature trajectories, carbon dioxide setpoints and relative humidity bounds, in such a way that during the growing season the crop is maintained in an optimal condition and the crop production is maximised. The climate computer will then realise the by the grower desired climate. Beside the heat supply and the carbon dioxide supply, a main variable to control the inside climate in a greenhouse is the natural ventilation through the windows in the roof of the greenhouse. The ventilation is controlled by adjusting the window openings and is heavily depending on the outside wind conditions and the difference between inside and outside temperature. An easy method to estimate the ventilation on-line would give the grower valuable insight into the process of his greenhouse climate.

A new optimization algorithm for singular and non-singular digital time-optimal control of robots

Time-optimal controls for 2-link robots are often of bang-bang type. Many algorithms to solve time-optimal robot control problems a-priori assume the optimal control to be bang-bang. Industrial robots very often have 5 or 6 links and then the associated time-optimal controls are usually singular. This paper presents a new algorithm that enables computation of both bang-bang and singular time-optimal controls for robots. The algorithm uses both the conjugate gradient and Gauss-Newton method to enhance its efficiency and does not require state-parameterization, which introduces additional errors. The algorithm is used to compute time-optimal controls for an industrial 5-link robot model including gravity and viscous friction.