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.

The study demonstrates that hyperspectral imaging can non‑destructively differentiate active nitrogen‑fixing root nodules from non‑fixing nodules and root tissue based on distinct spectral signatures. By integrating deep‑learning models, the authors created an automated nodule counting pipeline that works across multiple legume species and growth conditions, eliminating labor‑intensive manual counting and reliably detecting nodules within dense root systems.

The study demonstrates that limonene, a natural essential‑oil component, strongly inhibits Fusarium oxysporum, the causal agent of potato dry rot, by impairing colony growth, hyphal morphology, spore viability, membrane integrity, and transcription/translation processes, as well as disrupting ion homeostasis. Combined treatments reveal additive effects with mancozeb and synergistic effects with hymexazol, highlighting limonene's potential as an eco‑friendly bio‑fungicide for potato disease management.

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 the Botanical Spectrum Analyzer (BSA), a GUI that incorporates a modified U‑Net deep neural network for accurate segmentation of plant images from RGB and hyperspectral (VNIR and SWIR) data. BSA was tested on wheat, barley, and Arabidopsis datasets, achieving >99% accuracy and F1‑scores above 98%, and markedly outperformed commercial tools on root segmentation tasks.

The study developed a high-throughput phenotyping platform to assess root infestation by Orobanche cumana in a diverse sunflower association mapping population and applied a dual GWAS using SNPs and k-mers to uncover resistance loci. It validated known QTLs with higher resolution, identified novel candidate genes such as leucine‑rich repeat receptor kinases, and highlighted introgressed segments from wild Helianthus species that contribute to broomrape resistance.

The study reports the creation and validation of a high‑density Axiom SNP array for Solanum tuberosum, based on 10X Genomics sequencing of 108 diverse clones and integration of existing Illumina markers. The array demonstrated high reproducibility and, after filtering, provided 206,616 informative markers for population structure analysis, GWAS of polyphenol oxidase activity, and genomic prediction with accuracies up to 0.86.

A genome-wide association study of 166 Iberian Arabidopsis accessions identified loci, including ABA3 and GA2ox2, that modulate the inhibitory effect of the auxin precursor indole-3-acetamide (IAM) on primary root elongation. Integrating sequence analysis, transcriptomics, 3D protein modeling, and mutant physiology revealed that IAM promotes ABA biosynthesis and signaling, uncovering a novel node of hormone crosstalk.

The study used multi‑season phenotyping for iron, zinc, and protein content together with whole‑genome re‑sequencing of a groundnut mini‑core collection to conduct a genome‑wide association study, identifying numerous marker‑trait associations and candidate genes linked to nutrient homeostasis. SNP‑based KASP markers were designed for nine loci, of which three showed polymorphism and are ready for deployment in genomics‑assisted breeding for nutrient‑rich groundnut varieties.

The study presents a high-quality genome assembly for the nickel hyperaccumulator Noccaea caerulescens and uses it as a reference for comparative transcriptomic analyses across different N. caerulescens accessions and the non‑accumulating relative Microthlaspi perfoliatum. It identifies a limited set of metal transporters (NcHMA3, NcHMA4, NcIREG2, and NcIRT1) whose elevated expression correlates with hyperaccumulation, and demonstrates that frameshift mutations in NcIRT1 can abolish the trait, indicating an ancient, transporter‑driven origin of nickel hyperaccumulation.