Een grote toekomst voor kleine robots in open teelten

Een evaluatie wordt gemaakt na 1 jaar met ervaringen met verschillende kleine landbouwrobots, waarna de verdere ontwikkelingen voor de toekomst ter sprake kome

Herkenning en bestrijding van ridderzuring met een robot

Ridderzuring (Rumex obtusifolius L.) is een veelvoorkomend en lastig te bestrijden onkruid dat vooral biologische melkveehouders grote problemen bezorgt. Op initiatief van de sector wordt daarom een robot ontwikkeld die geheel zelfstandig ridderzuring opspoort en vernietigt

The ModCom modular simulation system

Simulation models of agro-ecological systems are typically written in a manner that precludes reusability of parts of the model without a significant amount of familiarity with and rewriting of existing code. Similarly, replacing a part of a model with a functionally equivalent part from another model is typically difficult. The objective of this study was to develop a method to enable the assembly of simulation models from previously and independently developed component models. Recent advances in software engineering have enabled the development of software applications from smaller parts (called components) on the basis of an abstract decomposition of the relevant domain (called a framework). Based on a requirements analysis of existing simulation models we developed the ModCom simulation framework. ModCom provides a set of interface specifications that describe components in a simulation. ModCom also provides implementations of the core simulation services. The framework interfaces use well-defined binary standards and allows developers to implement the interfaces using a broad range of computer languages. Using this framework, simulation models can be assembled by connecting component models in much the same way that Lego blocks are put together to assemble a house. ModCom thus allows modelers to create models and modeling tools that are easily exchanged (in binary form or source code) with colleagues across the hall or across the globe

A robot to detect and control broad-leaved dock (Rumex obtusifolius L.) in grassland

Broad-leaved dock is a common and troublesome grassland weed with a wide geographic distribution. In conventional farming the weed is normally controlled by using a selective herbicide, but in organic farming manual removal is the best option to control this weed. The objective of our work was to develop a robot that can navigate a pasture, detect broad-leaved dock, and remove any weeds found. A prototype robot was constructed that navigates by following a predefined path using centimeter-precision global positioning system (GPS). Broad-leaved dock is detected using a camera and image processing. Once detected, weeds are destroyed by a cutting device. Tests of aspects of the system showed that path following accuracy is adequate but could be improved through tuning of the controller or adoption of a dynamic vehicle model, that the success rate of weed detection is highest when the grass is short and when the broad-leaved dock plants are in rosette form, and that 75% of weeds removed did not grow back. An on-farm field test of the complete system resulted in detection of 124 weeds of 134 encountered (93%), while a weed removal action was performed eight times without a weed being present. Effective weed control is considered to be achieved when the center of the weeder is positioned within 0.1 m of the taproot of the weed—this occurred in 73% of the cases. We conclude that the robot is an effective instrument to detect and control broad-leaved dock under the conditions encountered on a commercial farm. © 2010 Wiley Periodicals, Inc

Herkenning en bestrijding van ridderzuring met een robot

Ridderzuring (Rumex obtusifolius L.) is een veelvoorkomend en lastig te bestrijden onkruid dat vooral biologische melkveehouders grote problemen bezorgt. Op initiatief van de sector wordt daarom een robot ontwikkeld die geheel zelfstandig ridderzuring opspoort en vernietigt

A robot to detect and control broad-leaved dock (Rumex obtusifolius L.) in grassland

Broad-leaved dock is a common and troublesome grassland weed with a wide geographic distribution. In conventional farming the weed is normally controlled by using a selective herbicide, but in organic farming manual removal is the best option to control this weed. The objective of our work was to develop a robot that can navigate a pasture, detect broad-leaved dock, and remove any weeds found. A prototype robot was constructed that navigates by following a predefined path using centimeter-precision global positioning system (GPS). Broad-leaved dock is detected using a camera and image processing. Once detected, weeds are destroyed by a cutting device. Tests of aspects of the system showed that path following accuracy is adequate but could be improved through tuning of the controller or adoption of a dynamic vehicle model, that the success rate of weed detection is highest when the grass is short and when the broad-leaved dock plants are in rosette form, and that 75% of weeds removed did not grow back. An on-farm field test of the complete system resulted in detection of 124 weeds of 134 encountered (93%), while a weed removal action was performed eight times without a weed being present. Effective weed control is considered to be achieved when the center of the weeder is positioned within 0.1 m of the taproot of the weed—this occurred in 73% of the cases. We conclude that the robot is an effective instrument to detect and control broad-leaved dock under the conditions encountered on a commercial farm. © 2010 Wiley Periodicals, Inc

Hugo

Our earlier robots have not solved the Field Robot Event’s row-following problem with a sufficient degree of robustness. The objective of the work presented here was to build a robot that can detect rows consisting of small or large maize plants by using a camera system; and to provide this robot with robust localization and navigation by using probabil-istic methods to process the data from the vision system in conjunction with data from other sensors. We employed a particle filter approach where information from the robot’s wheel encoders and a gyroscope is used in the control step and where the filter is updated using information from a downward-looking camera and a laser scanner. For the weed detection and control tasks, the robot is equipped with a self-contained spray unit consist-ing of two CMUCAM3 camera’s and four narrow-cone nozzles (two on each side of the robot) which allow for precision-treatment of small areas. At the Field Robot Event, the robot was able to follow rows and turn into the correct new row in all tasks. No manual intervention was necessary; the first objective was met. In the days and weeks leading up to the event, it was demonstrated that the robot can navigate even when the maize plants are very small. Thus, the second objective was also met. However, weed detection was less than perfect. It turned out to be more sensitive to the light conditions than we had realized. Also, the turf patches were placed almost between the maize plants instead of well inside the row, and were out of the camera’s view. In conclusion, the robot is capable of a high degree of autonomy in the tasks of the Field Robot Event: it didn’t once get lost and it damaged few plant