The vigor of potato plants is of crucial importance for potato seed producers, who are interested in predicting it at scale by exploiting the dependence of plant growth and development on the origin and physiological state of the seed tuber. In this article we present the results of a three-year long experiment in which we studied six potato varieties in three test fields. We identify a 73– overall correlation in the vigor of plants from the same seedlot grown in different test fields. Similarly, the biochemical tuber data produce plant vigor predictions that correlate up to 70– with the measurements. However, these relatively large data and prediction correlations are mostly due to the strong dependence of the seedlot vigor on the tuber genotype. For five out of six studied varieties, variety-specific cross-field and cross-year vigor predictions produce negligible or even negative correlations when the seed tubers and young plants experience environmental stress. At the same time, for the variety that appeared to be less sensitive to environmental stresses, we obtained cross-field and cross-year vigor predictions correlating up to with the measurements. Analysis of individual predictor variables, such as the abundance of a particular metabolite, indicates that the vigor-enhancing properties of the seed tubers are also variety-specific and that the FTIR spectroscopy data is the most reliable predictor.

We propose to use diurnal temperature variations for nondestructive monitoring of growing tubers and investigate the feasibility by numerically simulating the data collected with a grid of passive thermal sensors placed in the ground and sampled at regular time intervals. A qualitative linear imaging algorithm that produces an approximate projected view of the tubers is proposed and an effective inversion method is applied to recover the volume fraction of tubers. In particular, it is shown that a correlation-based cost functional outperforms the usual least-squares metric, although, requiring additional steps to deal with the non-uniqueness of the solution.

Potato vigour, the growth potential of seed potatoes, is a key agronomic trait that varies significantly across production fields due to factors such as genetic background and environmental conditions. Seed tuber microbiomes are thought to influence plant health and crop performance, yet the precise relationships between microbiome composition and potato vigour remain unclear. Here we conducted microbiome sequencing on seed tuber eyes and heel ends from 6 potato varieties grown in 240 fields. By using time-resolved drone imaging of three trial fields in the next season to track crop development, we were able to link microbiome composition with potato vigour. We used microbiome data at varying taxonomic resolutions to build random forest predictive models and found that amplicon sequence variants provided the highest predictive accuracy for potato vigour. The model revealed variety-specific relationships between the seed tuber microbiome and next season’s crop vigour in independent trial fields. With a coefficient of determination value of 0.69 for the best-performing variety, the model accurately predicted vigour in seed tubers from fields not previously included in the analysis. Moreover, the model identified key microbial indicators of vigour from which a Streptomyces, an Acinetobacter and a Cellvibrio amplicon sequence variant stood out as the most important contributors to the model’s accuracy. This study shows that seed potato vigour can be reliably predicted based on the microbiota associated with seed tuber eyes, potentially guiding future microbiome-informed breeding strategies.

Vitality is a fundamental trait for the development of a plant. It is known to depend on various factors, such as climate, soil, and the plant’s genetics, but the progressive depletion of soil nutrients make it a priority for the industry to pinpoint which of the controllable qualities of a seed have the biggest impact on vitality. This work describes techniques applied in a high-throughput phenotyping project, the first of this magnitude for a complex plant, the potato (solanum tuberosum). We also present the results of an analysis of associations between the chemical composition of the seed potatoes and field performance, solving the arising underdetermined linear systems by means of PLS regression. We show that some but not all of the chemical data is strongly associated to vitality.

This paper presents a modified self-consistent drift-diffusion-reaction model suitable for the analysis of electron-beam irradiated insulators at both short and long time scales. A novel boundary condition is employed that takes into account the reverse electron current and a fully dynamic trap-assisted generation-recombination mechanism is implemented. Sensitivity of the model with respect to material parameters is investigated and a calibration procedure is developed that reproduces experimental yield-energy curves for uncharged insulators. Long-time charging and yield variations are analyzed for stationary defocused and focused beams as well as moving beams dynamically scanning composite insulators.

By placing, in vacuum, a stack of transmission dynodes (tynodes) on top of a CMOS pixel chip, a single free electron detector could be made with outstanding performance in terms of spatial and time resolution. The essential object is the tynode: an ultra thin membrane, which emits, at the impact of an energetic electron on one side, a multiple of electrons at the other side. The electron yields of tynodes have been calculated by means of GEANT-4 Monte Carlo simulations, applying special low-energy extensions. The results are in line with another simulation based on a continuous charge-diffusion model. By means of Micro Electro Mechanical System (MEMS) technology, tynodes and test samples have been realized. The secondary electron yield of several samples has been measured in three different setups. Finally, several possibilities to improve the yield are presented.