Summary Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.

We present a database and a software tool, VisGraB, for benchmarking of methods for vision-based grasping of unknown objects with no prior object knowledge. The benchmark is a combined real-world and simulated experimental setup. Stereo images of real scenes containing several objects in di erent configurations are included in the database. The user needs to provide a method for grasp generation based on the real visual input. The grasps are then planned, executed, and evaluated by the provided grasp simulator where several grasp-quality measures are used for evaluation. This setup has the advantage that a large number of grasps can be executed and evaluated while dealing with dynamics and the noise and uncertainty present in the real world images. VisGraB enables a fair comparison among di erent grasping methods. The user furthermore does not need to deal with robot hardware, focusing on the vision methods instead. As a baseline, benchmark results of our grasp strategy are included.

With the exception of a few small‐scale case studies, there has been no real investigation into how European companies manage design, so when Design Management Europe decided to mount a survey of small and medium‐size companies and their design management practices, the results were interesting, and in some cases surprising.

Abstract-Grasping unknown objects based on real-world visual input is a challenging problem. In this paper, we present an Early Cognitive Vision system that builds a hierarchical representation based on edge and texture information, which is a sparse but powerful description of the scene. Based on this representation we generate edge-based and surface-based grasps. The results show that the method generates successful grasps, that the edge and surface information are complimentary, and that the method can deal with more complex scenes. We furthermore present a benchmark for visual-based grasping.

Robotic plant-specific spraying can reduce herbicide usage in agriculture while minimizing labor costs and maximizing yield. Weed detection is a crucial step in automated weeding. Currently, weed detection algorithms are always evaluated at the image level, using conventional image metrics. However, these metrics do not consider the full pipeline connecting image acquisition to the site-specific operation of the spraying nozzles, which is vital for an accurate evaluation of the system. Therefore, we propose a novel application-specific image-evaluation method, which analyses the weed detections on the plant level and in the light of the spraying decision made by the robot. In this paper, a spraying robot is evaluated on three levels: (1) On image-level, using conventional image metrics, (2) on application-level, using our novel application-specific image-evaluation method, and (3) on field level, in which the weed-detection algorithm is implemented on an autonomous spraying robot and tested in the field. On image level, our detection system achieved a recall of 57% and a precision of 84%, which is a lower performance than detection systems reported in literature. However, integrated on an autonomous volunteer-potato sprayer-system we outperformed the state-of-the-art, effectively controlling 96% of the weeds while terminating only 3% of the crops. Using the application-level evaluation, an accurate indication of the field performance of the weed-detection algorithm prior to the field test was given and the type of errors produced by the spraying system was correctly predicted.

Background Hyperaccumulation of trace elements is a rare trait among plants which is being investigated to advance our understanding of the regulation of metal accumulation and applications in phytotechnologies. Noccaea caerulescens (Brassicaceae) is an intensively studied hyperaccumulator model plant capable of attaining extremely high tissue concentrations of zinc and nickel with substantial genetic variation at the population-level. Micro-X-ray Fluorescence spectroscopy (µXRF) mapping is a sensitive high-resolution technique to obtain information of the spatial distribution of the plant metallome in hydrated samples. We used laboratory-based µXRF to characterize a collection of 86 genetically diverse Noccaea caerulescens accessions from across Europe. We developed an image-processing method to segment different plant substructures in the µXRF images. We introduced the concentration quotient (CQ) to quantify spatial patterns of metal accumulation and linked that to genetic variation. Results Image processing resulted in automated segmentation of µXRF plant images into petiole, leaf margin, leaf interveinal and leaf vasculature substructures. The harmonic means of recall and precision (F1 score) were 0.79, 0.80, 0.67, and 0.68, respectively. Spatial metal accumulation as determined by CQ is highly heritable in Noccaea caerulescens for all substructures, with broad-sense heritability (H2) ranging from 76 to 92%, and correlates only weakly with other heritable traits. Insertion of noise into the image segmentation algorithm barely decreases heritability scores of CQ for the segmented substructures, illustrating the robustness of the trait and the quantification method. Very low heritability was found for CQ if randomly generated substructures were compared, validating the approach. Conclusions A strategy for segmenting µXRF images of Noccaea caerulescens is proposed and the concentration quotient is developed to provide a quantitative measure of metal accumulation pattern, which can be used to determine genetic variation for such pattern. The metric is robust to segmentation error and provides reliable H2 estimates. This strategy provides an avenue for quantifying XRF data for analysis of the genetics of metal distribution patterns in plants and the subsequent discovery of new genes that regulate metal homeostasis and sequestration in plants.

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This paper presents and evaluates a method for detecting and counting demersal fish species in complex, cluttered, and occluded environments that can be installed on the conveyor belts of fishing vessels. Fishes on the conveyor belt were recorded using a colour camera and were detected using a deep neural network. To improve the detection, synthetic data were generated for rare fish species. The fishes were tracked over the consecutive images using a multi-object tracking algorithm, and based on multiple observations, the fish species was determined. The effect of the synthetic data, the amount of occlusion, and the observed dorsal or ventral fish side were investigated and a comparison with human electronic monitoring (EM) review was made. Using the presented method, a weighted counting error of 20% was achieved, compared to a counting error of 7% for human EM review on the same recordings.