The next #PhenomicsWebinars video is now online & can be watched until 14 April. Frédéric Baret presents strategies for light interception & biomass modeling using phenotypic data: plant-phenotyping.org/PhenomicsWeb_A… Join us for a live Q&A on 15 April. Remember to send us question!

Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution deepai.org/publication/es… by Kaaviya Velumani et al. #GenerativeAdversarialNetwork #ComputerVision

I will be defending my PhD tomorrow at 14h CET ! Feel free to join @ bit.ly/3xjG9jI to learn about using #DeepLearning for #cereal field #phenotyping using #drones and #IOTA CAPTE

Some days ago New Phytologist tweeted a Tansley review (nph.onlinelibrary.wiley.com/doi/10.1111/np…) on improving vegetation & land-surface models. Here is a Loomis review in Field Crops Res on improving crop models (sciencedirect.com/science/articl…). Good to see, both reviews stress model simplicity, and more...

Plant Phenomics is now accepting submissions for its special issue titled Advances in Proximal Sensing and Image Analysis for Time-Series Phenotyping. Click the link to learn more on how to submit your original research for publication consideration. spj.sciencemag.org/plant.../time-…

Happy to present our work "Sensitivities to temperature and evaporative demand in wheat relatives" with LEPSE in JXB @jxbotany.bsky.social doi.org/10.1093/jxb/er…

#Callforpapers Plant Phenomics is now accepting submissions for a special issue titled Plant Functional Phenotyping. Guest Editors: Drs. Chunjiang Zhao, Yanfeng Ding, @MartrePierre, Shouyang Liu thanks INRAE for the nice picture. Learn more at spj.sciencemag.org/plant-phenomic…

Photosynthesis (A) is widely considered as a trait yet to be exploited for improving crop yield. But which aspects of A matter most? What else should be co-selected so that the benefit from improved A is maximal? Our review JXB @jxbotany.bsky.social addresses these questions doi.org/10.1093/jxb/er…

[#Postdoctoral position] in #phenotyping/Close Range sensing/Artificial Intelligence jobs.inrae.fr/en/ot-14802 #postdoc #postdocposition #science INRAE Provence-Alpes-Côte d'Azur @UnivAvignon INRAE

#workshop CAPTE : Fred Baret, Benoit de Solan and Alexis Comar welcome the participant’s . The afternoon session has started with the keynote talk from T. Pridmore: recovering root structural traits via #DeepMachineLearning. INRAE Provence-Alpes-Côte d'Azur @UnivAvignon INRAE

Please RT: I'm hiring a postdoc in statistics and ML for plant x-ray image segmentation at Institute of Agriculture and Natural Resources . @NAPPN @IPPN employment.unl.edu/postings/79667

#Plant #Phenomics has received its inaugural Journal Impact Factor, 6.961 from Clarivate, 2022. Thanks to all authors, reviewers and editors of articles that were published in our journal to make this score! Looking forward to more excellent articles published in PP! #JCR2021

DEADLINE APPROACHING: Plant Phenomics is accepting submissions for a special issue titled Plant Functional Phenotyping. Click the link to learn more spj.sciencemag.org/plant-phenomic…

#INRAEdanslesmedias Dans Élement Terre – France 24  « Sécheresse : l'eau au compte-gouttes » @MartrePierre chercheur #INRAE mène des études avec robots & capteurs s/ des variétés de blé, maïs et vigne adaptées aux futures conditions climatiques 🌾🌽🍇 Replay (à 9mn) url.inrae.fr/3peOIxI

Self-supervised deep phenotyping with 3D plant model No human annotation, only a simulated dataset model: github.com/PheniX-Lab/sim… #D3P #fspm

Developed a high-throughput method for leaf counting in plants using deep learning and domain adaptation. Achieved 94% accuracy with Faster-RCNN model. Self-supervised approach. Code:github.com/YinglunLi/Whea… Details:spj.science.org/doi/10.34133/p…

Revolutionizing plant growth analysis: Our self-supervised leaf tip detection method, trained on diverse datasets, offers precise results across environments. Check out our code for enhanced phenotyping! #PlantScience #DeepLearning Details:spj.science.org/doi/10.34133/p…

Monitoring green fraction (GF) is crucial for breeders. Our self-supervised strategy enhances segmentation accuracy for rice and wheat. #CropScience #MachineLearning Details: spj.science.org/doi/10.34133/p…