Over the last three decades, labor shortages and increased labor costs in greenhouses have driven investments in the development of agricultural robots. Priva has been developing a robot to automate the repetitive task of deleafing tomato plants. The main challenges for commercializing the robot are in terms of cost-efficiency and cut-quality. High success rates in the detection of leaves and subsequent cutting action are required, which are limited by occlusion and the unstructured, dynamic greenhouse environment. As a consequence of manipulator constraints, detected leaves can not always be cut from the current robot position. In addition, detected leaves with an unfavorable approach angle are skipped as cut quality can not be guaranteed. Conversely, many leaves are not detected at positions from where they can be cut due to detection limitations. By combining detections from different viewpoints, the detection rate can be increased significantly. Moreover, fusing multiple detections of the same object is known to improve detection accuracy. In this thesis, object trackers are investigated, aiming to increase the number of successfully cut leaves by tracking leaves over different robot positions and fusing multiple detections of the same leaf.

Turbulent layers high in the atmosphere cause anisoplanatic phase aberrations, that are responsible for image degradation [6, 33, 46]. Adaptive Optics (AO) aims to correct for these aberrations by sensing them with a wavefront sensor and performing the correction with phase conjugate devices [18]. Various wavefront sensors have been designed and successfully implemented over the years, including the curvature sensor, interferometers, the Shack-Hartmann (SH) sensor and the pyramid sensor [7, 32, 40, 51]. In recent work the plenoptic sensor, an intermediate between the SH and pyramid sensor designs [10, 32, 37], has been proposed to improve on modern wavefront sensing. It has been reported to be beneficial in presence of strong or complex
wavefronts, such as deep turbulence conditions [18, 47]. The goal of this thesis is to investigate the SH and plenoptic wavefront sensors, with the
specific application on the correction of anisoplanatic aberrations that vary throughout the field of view. The contribution of this thesis to the field of AO is the development of a simulation toolbox in MATLAB. This toolbox is designed to simulate the SH and plenoptic sensors, providing a comparative study between the two. The comparative simulations reinforced results obtained from a corresponding Literature Survey. The SH sensor can outperform the plenoptic sensor on many occasions. If both sensors share the same microlens array (MLA) the plenoptic sensor scores worse in terms of performance metrics. By adjusting the MLA of the plenoptic sensor its dynamic range and sensitivity can be improved, such that it outperforms the SH sensor. Additionally, it was shown that the plenoptic sensor performs best for strong aberrations, simulated using
randomly-generated Kolmogorov screens. On the other hand, it fails in the presence of weak aberrations where the SH performs best. The developed toolbox allows for iso- and anisoplanatic aberrations to be reconstructed in a single frame, by differentiating between aperture plane reconstruction and phase screen retrieval. Multiple approaches are implemented in the toolbox, such that different reconstruction methods can be selected depending on experimental conditions.