The study assessed how well common deep learning models (ResNet, EfficientNet, Inception, MobileNet) generalize across different tomato pest and disease image datasets. While models performed well on the dataset they were trained on, they suffered substantial accuracy drops when applied to other datasets, indicating that architectural changes alone cannot overcome dataset variability. The results highlight the necessity for more diverse, representative training data to improve real-world deployment of PPD diagnostic tools.

In a two-year controlled-environment experiment, diploid and tetraploid individuals of wild-type and cultivar Marshall annual ryegrass (Lolium multiflorum) were grown under elevated CO2 (540 vs 800 ppm) and differing evapotranspiration regimes. Elevated CO2 increased total biomass by 44% across ploidy levels, and tetraploid wild-type plants matched the improved cultivar in growth and forage quality, indicating that chromosome manipulation and wild genetic resources can enhance climate resilience.

The study examined how polyploidy, hybridization, and incomplete lineage sorting affect phylogenetic reconstructions in the genus Packera, evaluating several published paralog‑processing pipelines. Results showed that the choice of orthology and paralog handling methods markedly altered tree topology, time‑calibrated phylogenies, biogeographic histories, and detection of ancient reticulation, underscoring the need for careful methodological selection alongside comprehensive taxon sampling.