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

PepAnno: A structure-aware deep learning framework for bioactive peptide prediction, structural visualization, and physicochemical profiling.

PLoS computational biology·2026

Same author

AWmeta Empowers Adaptively Weighted Transcriptomic Meta-Analysis.

Current issues in molecular biology·2026

Same author

T2T-Hub: a central platform for analyzing plant and animal telomere-to-telomere genomes.

Nucleic acids research·2026

Same author

Rapid identification of automotive window film brands <i>via</i> hyperspectral imaging with two-stage feature selection.

Analytical methods : advancing methods and applications·2026

Same author

A Single-Cell Atlas of Pan-Cancer Liver Metastasis Reveals Dynamic Cellular Programs Driving Metastatic Progression and Immune Modulation.

Research (Washington, D.C.)·2026

Same author

Mulberry Drought Diagnosis: Integrating Proximal Sensing and Metabolomics for Remote Monitoring.

Plants (Basel, Switzerland)·2026

Search research articles

Related Experiment Video

Updated: Aug 8, 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

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2

Haseeb Rehman Khan¹, Zeeshan Gillani^1,2, Muhammad Hasan Jamal¹

¹Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

Accurate early crop mapping using remote sensing is crucial for food security. A deep learning model achieved over 93% accuracy for staple crops within four weeks of sowing.

Keywords:

Sentinel-2 crop classification crop-type mapping deep learning

More Related Videos

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

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

Related Experiment Videos

Last Updated: Aug 8, 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

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

Area of Science:

Agricultural Science
Remote Sensing
Data Science

Background:

Global food supply chains are vulnerable to climate change and pandemics, impacting food security.
Accurate, timely crop maps are essential for food security but often unavailable early in the growing season, especially in developing nations.
Remote sensing for early crop-type identification faces challenges due to smallholder farming and crop diversity.

Purpose of the Study:

To develop and validate an accurate, early-stage crop-type mapping method for staple crops.
To address the challenge of unavailable crop maps in developing countries during the crucial early growth phases.
To support government agencies in ensuring food security through timely agricultural monitoring.

Main Methods:

A ground-based survey was conducted to collect field coordinates and crop type data.
Time-series satellite imagery from Sentinel-2 was acquired for the mapped fields.
A deep learning Long Short-Term Memory (LSTM) network was employed for crop classification.

Main Results:

The proposed deep learning model achieved high classification accuracy for staple crops (rice, wheat, sugarcane).
Accurate crop mapping was accomplished as early as the first four weeks after sowing, with 93.77% accuracy.
The method demonstrated effectiveness in classifying crop types within smallholder farming systems.

Conclusions:

The developed remote sensing and deep learning approach enables effective early-stage crop-type mapping.
This method can be scaled for large-area applications, particularly benefiting smallholder agricultural systems.
Timely crop information supports proactive planning for food availability and security.