Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Surface substance loss of subsurface bovine enamel lesions after different steps of the resinous infiltration technique: a 3D topography analysis.

Odontology·2011

Same author

Expression of IL-23/Th17 pathway in a murine model of Coxsackie virus B3-induced viral myocarditis.

Virology journal·2011

Same author

Extracellular matrix peptides of Artemia cyst shell participate in protecting encysted embryos from extreme environments.

PloS one·2011

Same author

Therapeutic effect of carboxymethylated and quanternized chitosan on insulin resistance in high-fat-diet-induced rats and 3T3-L1 adipocytes.

Journal of biomaterials science. Polymer edition·2011

Same author

Role of reactive oxygen species in triptolide-induced apoptosis of renal tubular cells and renal injury in rats.

Journal of Huazhong University of Science and Technology. Medical sciences = Hua zhong ke ji da xue xue bao. Yi xue Ying De wen ban = Huazhong keji daxue xuebao. Yixue Yingdewen ban·2011

Same author

Chlorination and ortho-acetoxylation of 2-arylbenzoxazoles.

Organic & biomolecular chemistry·2011

Search research articles

Related Experiment Video

Updated: May 30, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

An image dataset for analyzing tea picking behavior in tea plantations.

Ru Han^1,2, Ye Zheng², Renjie Tian²

¹School of Computer Science, Guangdong University of Petrochemical Technology, Maoming, China.

Frontiers in Plant Science

|January 30, 2025

Summary

A new Tea Garden Harvest Dataset enhances artificial intelligence recognition for tea picking in China. This resource improves intelligent tea picking practices, boosting efficiency and productivity in tea production.

Keywords:

behavior recognition image data outdoor scenes protection of tea plantation tea picking

More Related Videos

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Related Experiment Videos

Last Updated: May 30, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Area of Science:

Agricultural Science
Computer Science
Data Science

Background:

Tea production is vital to China's economy, with tea picking being a key agricultural activity.
The shift towards intelligent and mechanized tea picking necessitates advanced artificial intelligence (AI) recognition technologies.
A comprehensive dataset is crucial for developing effective AI models for large-scale tea picking operations.

Purpose of the Study:

To introduce the Tea Garden Harvest Dataset, a novel resource for advancing AI in tea picking.
To highlight the dataset's features that support improved tea garden management and intelligent harvesting.
To address the data gap in AI-driven tea picking research in China.

Main Methods:

Development of a comprehensive dataset featuring diverse tea garden imagery.
Implementation of advanced data augmentation techniques (rotation, cropping, enhancement, flipping) for enhanced image diversity.
Detailed annotation of images with boundary boxes, object categories, and sizes to support precise AI model training.
Enabling multi-scale training capabilities to accommodate targets of varying sizes and distances.

Main Results:

The dataset provides enhanced image diversity, improving AI model recognition across varied environments.
Precise annotations facilitate a better understanding of target features, boosting AI model learning and performance.
Multi-scale training capability ensures AI model adaptability and accuracy in real-world tea picking scenarios.
The dataset successfully fills a gap in data for intelligent tea picking in China.

Conclusions:

The Tea Garden Harvest Dataset is a significant contribution to intelligent tea picking practices.
Its unique advantages, including diversity, precise annotations, and multi-scale training, offer a powerful resource for tea garden management.
Leveraging this dataset promotes increased efficiency, accuracy, and productivity in tea production through AI advancements.