Phenolic content discrimination in Thai holy basil using hyperspectral data analysis and machine learning techniques. [PDF]
PLoS OneSuratanee A +5 more
europepmc +1 more source
FieldDino: Rapid In‐Field Stomatal Anatomy and Physiology Phenotyping
Plant, Cell &Environment, EarlyView.ABSTRACT Stomatal anatomy and physiology define CO2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding.
Edward Chaplin +3 more
wiley +1 more source
Proto-DS: A Self-Supervised Learning-Based Nondestructive Testing Approach for Food Adulteration with Imbalanced Hyperspectral Data. [PDF]
FoodsPang K +6 more
europepmc +1 more source
The Plant Phenome Journal, Volume 9, Issue 1, December 2026.Abstract Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large‐scale phenotyping remains a bottleneck. This study assessed the potential of high‐throughput tools (spectroscopy and poro‐fluorometry) to predict ...
Eva Coindre +13 more
wiley +1 more source
A Deep Learning Approach for Chromium Detection and Characterization from Soil Hyperspectral Data. [PDF]
ToxicsMa C, Xu X, Zhou M, Hu T, Qi C.
europepmc +1 more source
Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities
The Plant Phenome Journal, Volume 9, Issue 1, December 2026.Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang +12 more
wiley +1 more source
Detection of the stem-boring damage by pine shoot beetle (<i>Tomicus</i> spp.) to Yunan pine (<i>Pinus yunnanensis</i> Franch.) using UAV hyperspectral data. [PDF]
Front Plant SciLiu MY, Li GY, Shi L, Li YY, Liu H.
europepmc +1 more source
Phenotypic scoring of canola blackleg severity using machine learning image analysis
The Plant Phenome Journal, Volume 9, Issue 1, December 2026.Abstract Canola blackleg is a fungal disease that causes significant yield loss and plant death of infected canola (Brassica napus L., Brassica rapa L., Brassica juncea L.) fields worldwide. One of the most effective methods for controlling blackleg is through the cultivation of resistant varieties.
Qiao Hu +15 more
wiley +1 more source
PlantCV v4: Image analysis software for high‐throughput plant phenotyping
The Plant Phenome Journal, Volume 9, Issue 1, December 2026.Abstract PlantCV is an open‐source Python project aimed at developing tools to address a range of image‐based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding ...
Haley Schuhl +61 more
wiley +1 more source
A systematic color correction pipeline for controlled‐environment imaging
The Plant Phenome Journal, Volume 9, Issue 1, December 2026.ABSTRACT We present a stepwise color correction (CC) pipeline for controlled imaging environments. The workflow integrates flat‐field correction (FFC), gamma correction, and white‐balance correction, followed by a color‐mapping (CM) stage using machine‐learning regression—linear, partial least squares, and neural networks (NNs)—to deliver reliable CC ...
Collins Wakholi +7 more
wiley +1 more source