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This recent study by Yanjie Zhu and Matthias C. Rillig (currently sabbatical @OIST) Lab colleagues highlights the importance of emphasizing plant–soil systems in the field of multifactorial global change – it's a first attempt to untangle a complex series of interactions. 📖 nph.onlinelibrary.wiley.com/doi/10.1111/np… Wiley Plant Science

Exciting new research shows multi-target regression using hyperspectral imaging and machine learning boosts crop nutrient prediction accuracy by up to 12.5% for elements like Mn, Cu, and Mg. #AgricultureTech #MachineLearning Details: doi.org/10.34133/plant…

New method expands in situ root datasets using improved CycleGAN, boosting segmentation accuracy and versatility. Future work includes normal mapping for better shading simulations. #RootResearch #DeepLearning Details: doi.org/10.34133/plant…

New study uses dynamic ChlF responses to mitigate measurement variabilities in strong light. Low-frequency gain correlates with NPQ, aiding accurate plant analysis. #ChlorophyllFluorescence #PlantAnalysis Details: doi.org/10.34133/plant…

Integrating rice growth models, SNPs, and ML improves flowering time prediction. GSPs link to key genes like DTH2 and OsCOL15, aiding digital breeding. #RiceBreeding #CropModeling Details: doi.org/10.1016/j.plap…

Introducing DepthCropSeg: an almost unsupervised crop segmentation method using depth-informed models. Achieves 86.91% accuracy, comparable to fully supervised models. #CropSegmentation #AIinAgriculture Details: doi.org/10.1016/j.plap…

OsNIP3;1 mediates diurnal boron oscillation at rice vasculature tip 📖 nph.onlinelibrary.wiley.com/doi/10.1111/np… by Want et al. @WileyPlantSci #PlantScience

P2PNet-Soy: A new method for automated soybean seed counting & localization. Combines clustering, multi-level features, atrous convolution, and attention mechanisms! 🌿📈 #AgTech #AI #SoybeanResearch Details: doi.org/10.34133/plant…

New study uses SHS algorithm for accurate silique segmentation & phenotyping in oilseed rape. Achieves high R2 values for silique number & length, correlating strongly with yield. Promising for breeding! 🌿📈 #AgTech #PlantPhenotyping Details: doi.org/10.34133/plant…

Using UAV multispectral imaging, we predicted leaf N & NSC in slash pine trees over 11 months. Gradient boosting machine model achieved R2 values of 0.60 & 0.65. #TreeBreeding #RemoteSensing Details: doi.org/10.34133/plant…

Crop breeding needs accurate data. BreedingEIS, a free open-source system, helps manage breeding data via web and mobile clients. It uses QR codes, NFC, and portable labels for efficient field evaluation and data collection. Details: doi.org/10.34133/plant…

We developed a deep learning method for tea yield estimation using an enhanced YOLOv5 model. It accurately counts tea buds in the field with 91.88% precision and 0.98 correlation to manual counts. #tea Details: doi.org/10.34133/plant…

We developed a new motion-blur image restoration method using WRA-Net to improve crop and weed segmentation accuracy in motion-blurred images. Tested on 3 databases, it achieved top accuracy, outperforming state-of-the-art methods. Details: doi.org/10.34133/plant…

We developed a new motion-blur image restoration method using WRA-Net to improve crop and weed segmentation accuracy in motion-blurred images. Tested on 3 databases, it achieved top accuracy, outperforming state-of-the-art methods. Details: doi.org/10.1016/j.plap…

We developed a canopy spatial relationship-based method to construct forest spatial structure units and used ensemble learning to model maximum crown width height (HMCW) of Chinese fir. Details: doi.org/10.1016/j.plap…

The master splicing regulator PRMT5 acts as an evolutionary capacitor in Arabidopsis Beckel et al. Ariel Chernomoretz Fundación Leloir Marcelo Yanovsky 📖 nph.onlinelibrary.wiley.com/share/RVI4HM8C…

🚀 AI + imaging sensors revolutionize plant stress detection! 🌱📊 Our review of 2,704 studies shows deep learning dominates, but data standardization remains key. #AgTech #PrecisionAgriculture #AI Details: doi.org/10.34133/plant…

New study uses LSSVM to determine Fv/Fm from ChlF curves w/o dark adaptation. Tested on 7,231 samples, it's fast, accurate & practical for real-time plant phenotyping. #PlantScience #ChlorophyllFluorescence Details: doi.org/10.34133/plant…

New DeepCrop model uses deep learning for hydroponic sweet peppers. It's highly adaptable, efficient, and accurate. Could replace traditional models for complex ag systems! #CropModeling #DeepLearning #Agriculture Details: doi.org/10.34133/plant…

10-year study establishes universal critical N dilution curves for Japonica rice using SDM, RFA, and BHM. These models simplify N diagnosis, maintain accuracy, and boost regional application. #Agriculture #NitrogenManagement Details: doi.org/10.34133/plant…