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For robotic harvesting of sweet-pepper fruits in greenhouses a sensor system is required to detect and localize the fruits on the plants. Due to the complex structure of the plant, most fruits are (partially) occluded when an image is taken from one viewpoint only. In this research the effect of multiple camera positions and viewing angles on fruit visibility and detectability was investigated. A recording device was built which allowed to place the camera under different azimuth and zenith angles and to move the camera horizontally along the crop row. Fourteen camera positions were chosen and the fruit visibility in the recorded images was manually determined for each position. For images taken from one position only with the criterion of maximum 50% occlusion per fruit, the fruit detectability (FD) was in no case higher than 69%. The best single positions were the front views and looking with a zenith angle of 60° upwards. The FD increased when a combination was made of multiple viewpoint positions. With a combination of five favourite positions the maximum FD was 90%. [ABSTRACT FROM AUTHOR]

A modular software framework design that allows flexible implementation of eye-in-hand sensing and motion control for agricultural robotics in dense vegetation is reported. Harvesting robots in cultivars with dense vegetation require multiple viewpoints and on-line trajectory adjustments in order to reduce the amount of false negatives and correct for fruit movement. In contrast to specialised software, the framework proposed aims to support a wide variety of agricultural use cases, hardware and extensions. A set of Robotic Operating System (ROS) nodes was created to ensure modularity and separation of concerns, implementing functionalities for application control, robot motion control, image acquisition, fruit detection, visual servo control and simultaneous localisation and mapping (SLAM) for monocular relative depth estimation and scene reconstruction. Coordination functionality was implemented by the application control node with a finite state machine. In order to provide visual servo control and simultaneous localisation and mapping functionalities, off-the-shelf libraries Visual Servoing Platform library (ViSP) and Large Scale Direct SLAM (LSD-SLAM) were wrapped in ROS nodes. The capabilities of the framework are demonstrated by an example implementation for use with a sweet-pepper crop, combined with hardware consisting of a Baxter robot and a colour camera placed on its end-effector. Qualitative tests were performed under laboratory conditions using an artificial dense vegetation sweet-pepper crop. Results indicated the framework can be implemented for sensing and robot motion control in sweet-pepper using visual information from the end-effector. Future research to apply the framework to other use-cases and validate the performance of its components in servo applications under real greenhouse conditions is suggested. [ABSTRACT FROM AUTHOR]

Greenhouse crop system design for maximum efficiency and quality of labour is an optimisation problem that benefits from model-based design evaluation. This study focussed on the harvest process of roses in a static system as a step in this direction. The objective was to identify parameters with strong influence on labour performance as well as the effect of uncertainty in input parameters on key performance indicators. Differential sensitivity was analysed and results were tested for model linearity and superposability and verified using the robust Monte Carlo analysis method since in the literature, performance and applicability of differential sensitivity analysis has been questioned for models with internal stochastic behaviour. Greenhouse section length and width, single rose cut time, and yield influence labour performance most, but greenhouse section dimensions and yield also affect the number of harvested stems directly. Throughput, i.e. harvested stems per second, being the preferred metric for labour performance, is most affected by single rose cut time, yield, number of harvest cycles per day, greenhouse length and operator transport velocity. The model is insensitive for σ of lognormal distributed stochastic variables describing the duration of low frequent operations in the harvest process, like loading and unloading rose nets. In uncertainty analysis, the coefficient of variation for the most important outputs, labour time and throughput, is around 5%. Total sensitivity as determined using differential sensitivity analysis and Monte Carlo analysis essentially agreed. The combination of both methods gives full insight into both individual and total sensitivity of key performance indicators. [Copyright &y& Elsevier]