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.

Key Elements for Plant Nutrition

Key Elements for Plant Nutrition

Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...

You might also read

Related Articles

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

Sort by

Same author

Correction: Cultivation viability of <i>Allium tuberosum</i> L. in the Western Ghats: insights into crop dynamics, yield and quality.

Frontiers in plant science·2026

Same author

Organic formulation modulating crop health, yield and managing anthracnose-twister and Stemphylium blight of onion.

Frontiers in plant science·2026

Same author

Editorial: Insights into the molecular dynamics of stress physiology in allium crops.

Frontiers in plant science·2026

Same author

Microbial succession and tissue-specific restructuring of bacterial and fungal communities during post-harvest onion bulb rotting.

Frontiers in microbiology·2026

Same author

Assessment of salinity tolerance in onion (<i>Allium cepa</i> L.) genotypes at germination stage through morphological traits and multivariate analysis.

Frontiers in plant science·2026

Same author

Microbial allies: shaping growth, physiology, and rhizosphere dynamics of onion (<i>Allium cepa</i> L.).

PeerJ·2026

Search research articles

Related Experiment Video

Updated: Feb 24, 2026

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

UAV multispectral sensing and data-driven modeling for precision onion yield prediction.

Sagar M Wayal¹, Shardul Parab², Anusha Raj¹

¹ICAR-Directorate of Onion and Garlic Research, Pune, India.

Frontiers in Plant Science

|February 23, 2026

Summary

This summary is machine-generated.

Unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning accurately predicts onion yield. Random Forest models showed the best performance for optimizing precision agriculture and crop management.

Keywords:

crop modeling machine learning multi-spectral sensors onion production precision agriculture remote sensing vegetation indices yield prediction

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

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

Published on: January 9, 2026

Related Experiment Videos

Last Updated: Feb 24, 2026

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

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

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

Published on: January 9, 2026

Area of Science:

Agricultural Science
Remote Sensing
Machine Learning

Background:

Precision agriculture benefits from integrating Unmanned Aerial Vehicle (UAV)-assisted remote sensing with the Internet of Things (IoT) and Internet of Everything (IoE).
Capturing spatiotemporal variability in crop growth is crucial for optimizing agricultural practices.
UAV-based multispectral imagery offers a powerful tool for monitoring crop health and predicting yield.

Purpose of the Study:

To predict the bulb yield of rainy-season onion crops using UAV-based multispectral imagery.
To evaluate the performance of various machine learning algorithms for onion yield prediction.
To assess the utility of vegetation indices derived from multispectral data for yield modeling.

Main Methods:

Acquisition of canopy reflectance mosaics from UAVs at key growth stages.
Extraction of vegetation indices (VIs) including NDVI, NDRE, SAVI, LAI, NORM2, and GNDVI.
Development and assessment of yield prediction models using five machine learning algorithms (linear regression, random forest, support vector machine, gradient boosting, elastic net regression) with 10-fold cross-validation.

Main Results:

Random Forest consistently outperformed other models, achieving high accuracy at the bulb development stage (validation R² = 0.755).
Support Vector Machine also demonstrated strong predictive capability (validation R² = 0.716).
Interannual variability in model performance was observed, with models trained on 2024 data showing better results than those from 2023.

Conclusions:

UAV-derived multispectral sensing combined with machine learning is an effective and scalable approach for reliable onion yield prediction.
This methodology provides timely decision support for managing rainy-season onion crops under diverse agronomic conditions.
The study highlights the potential of advanced remote sensing and AI techniques in modern agriculture.