New image processing method tracks chlorosis and necrosis in plant leaves, aiding understanding of stress impact on grain filling. High-throughput, deep learning-based approach promises genetic insights. #PlantScience
Details:spj.science.org/doi/10.34133/p…

Developed generic model estimates wheat leaf area index (LAI) from aerial multispectral data across diverse soils without ground calibration. Robust against soil variations, adaptable to different sensors and species traits.
Details:spj.science.org/doi/10.34133/p…

Innovative approach using YOLO v5, ResNet50, and DeepSORT models to analyze rice panicle phenotypes under varied nitrogen treatments, yielding precise insights. #RicePhenotyping #AgTech
Details:spj.science.org/doi/10.34133/p…

Maize canopy light interception hinges on leaf orientation shaped by sowing patterns. ALAEM algorithm validates, revealing genotypic & environmental impacts on maize architecture. #CropScience #AgTech
Details:spj.science.org/doi/10.34133/p…

Field phenotyping platforms, crucial for plant breeding, face challenges in aligning point cloud data for accurate phenotypic trait extraction. This study uses LiDAR and RGB cameras, aligns data, and demonstrates improved accuracy.
Details:spj.science.org/doi/10.34133/p…

Integrating solar-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI) enhances crop canopy gross primary productivity (GPP) estimation. Two-leaf model improves accuracy. #RemoteSensing #VegetationMonitoring
Details:spj.science.org/doi/10.34133/p…

Boosting rice yield relies on maximizing photosynthesis. Our model, leveraging sun-induced chlorophyll fluorescence, accurately estimates key photosynthetic traits, aiding crop productivity and climate resilience. #AgTech
Details:spj.science.org/doi/10.34133/p…

So cool 👇🌱😎

Revolutionizing rapeseed maturity classification with nondestructive hyperspectral imaging & machine learning. D2nd-IVISSA-SPA-SVM model boasts 97.86% accuracy. #AgTech #CropScience
Details:spj.science.org/doi/10.34133/p…

This study explores dynamic responses of chlorophyll a fluorescence (ChlF) under varying excitations using pseudo-random binary sequences. Nonlinear and nonstationary ChlF responses offer insights. #PlantScience
Details:spj.science.org/doi/10.34133/p…

Thrilled to welcome Integrative Phenomics as our latest startup sponsor! 🤩

Amid machine learning's rise, plant growth models remain mechanistic, rooted in understanding system dynamics. This study unifies identifiability analysis, vital for accurate parameter estimation and model interpretation.
Details:spj.science.org/doi/10.34133/p…

While cleaning the IBERS - Aber notebooks, students from Information Studies found this plant still pressed between the pages of a book from the 1920s. #PlantScience #DISProject #Archives

Register for the conference here: shorturl.at/anxA4

Novel multi-target regression method enhances accuracy in predicting plant element concentrations, surpassing single-target regression. Promising results in tomato leaves with hyperspectral imaging. #PlantNutrition #MachineLearning
Details:spj.science.org/doi/10.34133/p…

JOB OPPORTUNITY: Join the Adelaide team at the Plant Accelerator as a Field Operations Officer. You will oversee and organise field phenotyping operations for The Plant Accelerator node. Applications close Sunday 28th April 2024. Apply at:
careers.adelaide.edu.au/cw/en/job/5133…

Proposed photosynthetic accumulation model (PAM) and its simplified version (SPAM) leverage UAV images to estimate rice aboveground biomass (AGB) accurately, aiding crop management and breeding endeavors effectively.
Details:spj.science.org/doi/10.34133/p…

Always good to have a new crop to challenge our systems, elder #SambucusNigra in a seed priming #drought screen.

A collaborative study between ⁦UNL Ag Research⁩ and U Wyoming on using plant autofluorescence ⁦UNL Plant Phenomics,⁩ to separate genotypes and extract biophysical traits. ⁦ frontiersin.org/journals/plant…