AIMC Topic: Flowers

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Feedback regulation of mA modification creates local auxin maxima essential for rice microsporogenesis.

Developmental cell
N-methyladenosine (mA) RNA modification and its effectors control various plant developmental processes, yet whether and how these effectors are transcriptionally controlled to confer functional specificity so far remain elusive. Herein, we show that...

Integrated of Hyperspectral Imaging and Machine Learning Algorithms for Nondestructive Detection of Therapeutic Properties of Plants.

Chemistry & biodiversity
The approaches used to determine the medicinal properties of the plants are often destructive, labor-intensive, time-consuming, and expensive, making it impossible to analyze their quality analysis online. Performance of hyperspectral imaging (HSI) i...

Characterizing Chinese saffron Origin, Age and grade using VNlR hyperspectral imaging and Machine learning.

Food research international (Ottawa, Ont.)
Saffron (Crocus sativus L.), the dried stigma, is an extremely valuable spice and medicinal herb, whose economic value is affected by geographical origin, age and grade. In this study, we proposed a method to identify saffron from different Chinese o...

Multitemporal monitoring of forest indicator species using UAV and machine learning image recognition.

Environmental monitoring and assessment
In natural restoration, it is important to improve the efficiency of monitoring. Remote sensing using unmanned aerial vehicle (UAV) platforms plays a major role in improving monitoring efficiency. UAV platforms are particularly suited for monitoring ...

Definition of reproductive structures in Eucalyptus for phenological data collection.

International journal of biometeorology
In an era where global climate change is shifting plant phenology, global meta-analyses of multiple species are required more than ever. Common language or references for enhanced data compatibility are key for such analyses. Although the Plant Pheno...

Data-centric AI approach for automated wildflower monitoring.

PloS one
We present the Eindhoven Wildflower Dataset (EWD) as well as a PyTorch object detection model that is able to classify and count wildflowers. EWD, collected over two entire flowering seasons and expert annotated, contains 2,002 top-view images of flo...

Deep learning approach for detecting tomato flowers and buds in greenhouses on 3P2R gantry robot.

Scientific reports
In recent years, significant advancements have been made in the field of smart greenhouses, particularly in the application of computer vision and robotics for pollinating flowers. Robotic pollination offers several benefits, including reduced labor ...

AI-driven determination of active compounds and investigation of multi-pharmacological effects of Chrysanthemi Flos.

Computers in biology and medicine
BACKGROUND: Chrysanthemi Flos as a medicine food homology species is widely used in the prevention and treatment of diseases, whereas comprehensive research of its active compounds related to multi-pharmacological effects remains limited. This study ...

Estimation of the amount of pear pollen based on flowering stage detection using deep learning.

Scientific reports
Pear pollination is performed by artificial pollination because the pollination rate through insect pollination is not stable. Pollen must be collected to secure sufficient pollen for artificial pollination. However, recently, collecting sufficient a...

Optimization of Flavonoid Extraction from Flowers Using Ultrasonic Techniques: Predictive Modeling through Response Surface Methodology and Deep Neural Network and Biological Activity Assessment.

Molecules (Basel, Switzerland)
Understanding the optimal extraction methods for flavonoids from flowers (AMF) is crucial for unlocking their potential benefits. This study aimed to optimize the efficiency of flavonoid extraction from AMF. After comparing extraction methods, the u...