Lightnews — Scholar-powered news

Plant Phenomics @plantphenomics.bsky.social · May 22

We've developed ILCD, a VQA model for crop diseases, integrating coattention, MUTAN, and BiBa. Our new CDwPK-VQA dataset includes multi-attribute info. ILCD achieves 86.06% accuracy on CDwPK-VQA, outperforming others.
Details: doi.org/10.34133/plant…

Plant Phenomics @plantphenomics.bsky.social · May 15

We integrated hyperspectral & metabolomic data to identify salt-tolerant Medicago truncatula mutants. By combining data & using upper-level metabolomics, we achieved high efficiency & accuracy.
Details: doi.org/10.1016/j.plap…

Plant Phenomics @plantphenomics.bsky.social · Apr 7

New study uses 3D RTM & LiDAR to analyze light in larch plantations. Crown volume key for APAR, while competition still impacts. Combining tech helps optimize forest structure & guide precise management. #forestry #ecology
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Apr 7

New study uses YOLO v5, ResNet50, and DeepSORT models to analyze rice panicle traits via time - series images. Results show high accuracy in panicle counting and heading date estimation.
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Apr 7

Boosting rice yield via enhanced photosynthesis! New study uses sun-induced chlorophyll fluorescence (SIF) to accurately estimate key traits like Vcmax and gs. 🌱📈 #agritech #photosynthesis #cropscience
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 31

New study proposes ALAEM algorithm to measure maize leaf orientation via RGB images. It shows leaf orientation varies by sowing density, row spacing, and genotype, impacting light interception.
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 31

We propose PLPNet for precise tomato leaf disease detection. It uses perceptual adaptive convolution, location reinforcement attention, and proximity feature aggregation to tackle challenges like soil interference. Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 31

New study: DC2Net detects Asian soybean rust early using hyperspectral imaging & deep learning. It combines deformable & dilated convolutions, achieving 96.73% accuracy. #CropDisease
Details: spj.science.org/doi/10.34133/p…

1

Plant Phenomics @plantphenomics.bsky.social · Mar 31

New study uses AI & citizen science w/ smartphones to count coffee cherries on trees. Tested in Peru & Colombia, it shows promising results for scalable, low-cost crop monitoring in low-income regions. #AI #Coffee #CitizenScience
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 17

Hyperspectral imaging + machine learning = game-changing tool for analyzing pigments in Neopyropia! Fast, nondestructive, and accurate phenotyping of phycoerythrin, phycocyanin, allophycocyanin.
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 17

MG-YOLO: A novel detection algorithm for gray mold spores in precision ag. Combines Multi-head self-attention, BiFPN, and GhostCSP for high accuracy and speed. Achieves 0.983 accuracy in 0.009s/image, outperforming YOLOv5 by 6.8%.
Details: spj.science.org/doi/10.34133/p…

1

Plant Phenomics @plantphenomics.bsky.social · Mar 17

PanicleNeRF uses smartphone videos to create 3D rice panicle models in fields. Combining SAM and YOLOv8, it achieves high segmentation accuracy and outperforms traditional methods. #Reconstruction
Details: spj.science.org/doi/10.34133/p…

1

Plant Phenomics @plantphenomics.bsky.social · Mar 12

New VQA model for crop disease detection! ILCD uses co-attention, MUTAN, and bias balancing to identify disease stages. Achieves 86.06% accuracy on CDwPK-VQA dataset. Check it out: github.com/SdustZYP/ILCD-…
Details: spj.science.org/doi/10.34133/p��

1 1

Plant Phenomics @plantphenomics.bsky.social · Mar 12

New study improves LNC retrieval in Ginkgo trees using modified ratio indices & advanced BRF spectra methods. Results show enhanced accuracy & highlight potential for better nitrogen status assessment. #LeafNitrogen #RemoteSensing
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 12

New study develops LNA estimation model for wheat using UAV & hyperspectral data. Models consider vertical heterogeneity, improving accuracy. RF-LNASum model shows best results with 17.8% error.
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 12

New study uses field excavation & 3D digitization to analyze grapevine root systems, revealing genotype-specific water uptake. Excavation & in situ digitization are accurate, despite some fine root length underestimation. #Sustainableagriculture
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 12

Using drones & deep learning, we measured plant maturity, stand count, & height in fields. CNN-LSTM models improved maturity prediction. This tech offers accuracy & cost savings over traditional methods. #DeepLearning
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 10

We studied Phragmites australis & Typha orientalis to understand their canopy structure and solar radiation patterns. Key findings: layered solar radiation transmittance is more sensitive to canopy structure than pigments.
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 10

We propose using low-altitude aerial photography to create 3D point clouds and multispectral images of wheat plots. This helps extract dynamic digital phenotypes for genome-wide association studies. #Wheatplot #Image
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 10

Introducing CHCNet: A unified model for counting cereal crops like maize, rice, sorghum, and wheat using few-shot learning. It reduces labeling costs and enhances cross-crop generalization. Check it out at cerealcropnet.com
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 10

New AI model for plant disease diagnostics outperforms GPT-4! Uses 3 stages of image-text alignment to generate accurate phenotypic descriptions. Check it out: plantext.samlab.cn #ChartGPT
Details: spj.science.org/doi/10.34133/p…

2

Plant Phenomics @plantphenomics.bsky.social · Mar 4

Our study explores nitrogen responsiveness in wheat using drone phenotyping & machine learning. We quantify traits, map genetic components, and classify varieties for optimized N use. #nitrogen
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 4

Exploring plant drought response? Chlorophyll fluorescence beats spectral reflectance in reliability. But using both methods together offers best insights. Study on tobacco & barley leaves shows the way! #ClimateChange #PlantScience
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 4

🌾 CSNet revolutionizes wheat breeding! 🌾 Our count-supervised network uses quantity info to accurately count wheat ears without location data. Boosting yield & reducing costs! 🚀🌱 Learn more #AgTech #AI #FoodSecurity
Details: spj.science.org/doi/10.34133/p…

Plant Phenomics @plantphenomics.bsky.social · Mar 4

Using AI and computer vision, we developed models to detect sorghum panicles and estimate grain numbers from smartphone images. Our Sorghum-Net model achieved a 17% error rate, paving the way for efficient crop yield estimation.
Details: spj.science.org/doi/10.34133/p…

1