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Flowers

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Delayed flowering phenology of red-flowering plants in response to hummingbird migration.

Current biology : CB
The radiation of angiosperms is marked by a phenomenal diversity of floral size, shape, color, scent, and reward. The multi-dimensional response to selection to optimize pollination has generated correlated suites of these floral traits across distan...

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 with a small dataset predicts chromatin remodelling contribution to winter dormancy of apple axillary buds.

Tree physiology
Epigenetic changes serve as a cellular memory for cumulative cold recognition in both herbaceous and tree species, including bud dormancy. However, most studies have discussed predicted chromatin structure with respect to histone marks. In the presen...

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 ...

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 ...

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 ...

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...

Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic archit...

A machine learning approach fusing multisource spectral data for prediction of floral origins and taste components of Apis cerana honey.

Food research international (Ottawa, Ont.)
This study explores the use of near-infrared (NIR), mid-infrared (MIR), and Raman spectral fusion for the rapid prediction of floral origins and main taste components in Apis cerana (A. cerana) honey. Feature-level fusion with the partial least squar...

Tasselyzer, a machine learning method to quantify maize anther exertion, based on PlantCV.

The Plant journal : for cell and molecular biology
Maize anthers emerge from male-only florets, a process that involves complex genetic programming and is affected by environmental factors. Quantifying anther exertion provides a key indicator of male fertility; however, traditional manual scoring met...