The study employed ultra large‑scale 2D clinostats to grow tomato (Solanum lycopersicum) plants beyond the seedling stage under simulated microgravity and upright control conditions across five sequential trials. Simulated microgravity consistently affected plant growth, but the magnitude and direction of the response varied among trials, with temperature identified as a significant co‑variant; moderate heat stress surprisingly enhanced growth under simulated microgravity. These results highlight the utility of large‑scale clinostats for dissecting interactions between environmental factors and simulated microgravity in plant development.

The authors used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.

PlantCV v4 is an open-source Python framework that simplifies image-based plant phenotyping by providing extensive tutorials and streamlined installation, enabling users with limited coding skills to automate trait extraction. The release adds support for fluorescence, thermal, and hyperspectral imaging and introduces a new subpackage for morphological measurements such as leaf angle, which is validated against manual data collection methods.

The study combined high-throughput image-based phenotyping with genome-wide association studies to uncover the genetic architecture of tolerance to the spittlebug Aeneolamia varia in 339 interspecific Urochloa hybrids. Six robust QTL were identified for plant damage traits, explaining up to 21.5% of variance, and candidate genes linked to hormone signaling, oxidative stress, and cell‑wall modification were highlighted, providing markers for breeding.

The study investigated metabolic responses of kale (Brassica oleracea) grown under simulated microgravity using a 2-D clinostat versus normal gravity conditions. LC‑MS data were analyzed with multivariate tools such as PCA and volcano plots to identify gravity‑related metabolic adaptations and potential molecular markers for spaceflight crop health.

The study genotyped 1,013 hard red spring wheat lines using SNP arrays and targeted KASP markers to track changes in genetic diversity and the distribution of dwarfing Rht alleles over a century of North American breeding. It found shifts from Rht‑D1b to Rht‑B1b dominance, identified low‑frequency dwarf alleles at Rht24 and Rht25 that have increased recently, and revealed gene interactions that can fine‑tune plant height, along with evidence of recent selection for photoperiod sensitivity.

The study introduces a deep‑learning based image analysis pipeline that scores blackleg disease severity from stem cross‑section images of canola species, achieving greater consistency than median expert raters while preserving comparable heritability of susceptibility traits. This standardized scoring method aims to improve selection of resistant varieties in breeding programs.

A biparental Vicia faba mapping population was screened under glasshouse conditions for resistance to a mixture of Fusarium avenaceum and Fusarium oxysporum, revealing several families with moderate to high resistance. Using the Vfaba_v2 Axiom SNP array, a high-density linkage map of 6,755 SNPs was constructed, enabling the identification of a major QTL on linkage group 4 associated with partial resistance to foot and root rot.