Plant Phenomics 2024 Journal Impact Factor (Clarivate) 6.4

Wearable sensors hug plants, giving breeders contact-level data for growth & microclimate. Our review maps materials to physiology, spotlighting roadblocks & next-gen high-res phenotyping. #PlantTech.Details:

Multispectral imaging spots early green-up in Phedimus takesimensis, links it to 2 dormancy-break QTLs, paving the way for breeding rooftop plants that wake up greener sooner. #PlantBreeding #UrbanGreen.Details:

We distilled YOLOR into featherweight crop doctors—60.4% mAP, 4 tiny models, any plant. Catch disease early on phones, save yields, feed the world. Our code is available at #AgTech.Details:

New phenotyping robot: adjustable 1400–1600mm track, 6DoF gimbal, real-time data fusion. Works in fields/paddies, aids food security. #AgriTech #CropEnhancement.Details:

Spotibot (web/mobile) uses deep learning to detect Botrytis on roses—measures lesions, 99/96 F1 scores. Boosts breeding with objective data! #HorticultureTech #AI.Details:

New framework uses Helios 3D to simulate plant sensing images with labels—cuts need for labor-heavy real data. Enables unsupervised AI for crop trait analysis! #AgriTech #deeplearning .Details:

New study uses UAVs + deep learning to ace maize tassel detection—even small or detasseled ones. Enhanced data & blocking boost accuracy to 98%, solving field misses! #AgTech #AIart .Details:

Cracking plant development predictions: Cultivar-specific temp models beat species-level ones. Uncovers stressor links—boosts crop breeding for harsh climates. Field tools here!.Details:

New plant phenotyping approach: Segmentation-free pose estimation via SLEAP! Fast, accurate root trait extraction with less annotation. Try sleap-roots—code & data available. #PlantScience #AIart .Details:

New smart greenhouse robot spots tomato diseases & counts fruit 2.5× faster—YOLO-TGI+ByteTrack combo hits 93% fruit count R² on phone cam data. #Tomatoes.Details:

RGB phenotyping + BPNNs predict olive oil/phenol content with R² up to 0.95. Cultivar-specific models enable non-destructive, digital quality assessment for the olive sector. Details:

New VIS-NIR-SWIR indices isolate drought/ABA effects beyond chlorophyll/water, enabling precision plant diagnostics and pathway-specific stress detection via simple ML classifiers. #leaf #machinelearning.Details:

Global Wheat Challenge crowdsourced robust wheat head detection via 2020-2021 field data. Lessons: competition design shapes winning robustness. Recommendations for future phenotyping contests. #wheat .Details:

New FHTW-Net framework enhances cross-modal retrieval for rice leaf diseases, achieving high accuracy. Model supports data-driven decisions for disease prevention. #AgricultureTech #DeepLearning .Details:

New deep learning framework counts litchi flowers accurately. FlowerNet algorithm overcomes background interference, aiding orchard management. #AgricultureTech #DeepLearning .Details:

New strategy analyzes 58 image-based traits in rice, explaining 84.8% yield variance. Method supports genetic improvements and breeding. #RiceGenetics #Phenotyping.Details:

New deep learning framework predicts citrus rind color transformation with high accuracy. Model integrated into mobile app for real-world use. #CitrusFarming #AI #ColorPrediction.Details:

New photosynthetic accumulation model (PAM) accurately estimates rice aboveground biomass using UAV data. Simplified version (SPAM) also shows high accuracy. #PrecisionAgriculture #RiceManagement.Details:

RT @MPlantPCom: The evolutionary rise of oxygen sensing in land plants #spotlight #MolecularPlant .

RT @NewPhyt: Differing metabolic responses of guard cells to blue light. 📖 #Commentary by Fernie and Timm 👆 highlig….