AIMC Topic: Flowers

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Robotic cross-pollination of genetically modified flowers.

Science robotics
Engineered tomato plants produced flowers with visible stigmas that a robot could detect and pollinate faster than a human.

Genetic regulations of citrus flowering: insights towards climatic factors and modern biotechnological approaches.

Planta
The review highlights the intricate relationship between genetic and molecular mechanisms that regulate floral development and responses in citrus under diverse climatic conditions. Citrus, the world's top traded and third most produced fruit crop, h...

Flight and Floral Acoustic Signals for Bee Species Identification.

Neotropical entomology
Animal identification is pivotal for ecological studies, yet automated recognition tools for bee species remain underexplored. Here, we present a machine learning approach using a Random Forest algorithm to identify five bee species representing thre...

Identifying Cocoa Flower Visitors: A Deep Learning Dataset.

Scientific data
Cocoa is a multi-billion-dollar industry but research on improving yields through pollination remains limited. New embedded hardware and AI-based data analysis is advancing information on cocoa flower visitors, their identity and implications for yie...

Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8.

Scientific reports
Tomato growing points and flower buds serve as vital physiological indicators influencing yield quality, yet their detection remains challenging in complex facility environments. This study develops an improved YOLOv8 model for robust flower bud dete...

Optimizing drone-based pollination method by using efficient target detection and path planning for complex durian orchards.

Scientific reports
Durian is a valuable tropical fruit whose pollination heavily relies on bats and nocturnal insects. However, environmental degradation and pesticide usage have reduced insect populations, leading to inefficient natural pollination. This study propose...

Machine learning-based assessment of sustainable extraction methodologies tackling the biotechnological exploitation of Arnica montana extracts.

Food chemistry
Heat-assisted (HAE), ultrasound-assisted (UAE), microwave-assisted (MAE), and pressurized liquid extraction (PLE) represent diverse techniques with distinct physical principles that influence the efficiency and selectivity of bioactive compound recov...

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...

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...

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...