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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.

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Related Experiment Video

Updated: Sep 3, 2025

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

High-throughput image-based plant stand count estimation using convolutional neural networks.

Saeed Khaki¹, Hieu Pham², Zahra Khalilzadeh¹

¹Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, Iowa, United States of America.

|July 28, 2022

Summary

This summary is machine-generated.

A new deep learning algorithm, DeepStand, accurately counts corn stands from early-stage images. This innovation aids agricultural data collection for better crop breeding decisions.

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Area of Science:

Agricultural Science
Computer Science
Plant Breeding

Background:

Modern agriculture faces complex demands, driving a need for data-driven innovation.
Advancements in imaging technology allow for detailed in-field crop data collection.
Accurate phenotypic trait identification is crucial for efficient crop breeding.

Purpose of the Study:

To develop an advanced algorithm for accurate, image-based counting of corn stands.
To address the challenge of identifying phenotypic traits from crop imagery.
To improve data collection efficiency in the crop breeding cycle.

Main Methods:

Developed DeepStand, a novel deep learning algorithm for corn stand counting.
Utilized a truncated VGG-16 network as a feature extractor.
Combined multi-dimensional feature maps to enhance robustness against size variations.

Main Results:

DeepStand accurately identifies corn stands at early phenological stages.
The proposed method demonstrates superior performance compared to existing cutting-edge techniques.
Computational experiments validated the effectiveness of the DeepStand framework.

Conclusions:

DeepStand offers an innovative solution for automated corn stand counting.
The algorithm enhances the accuracy and efficiency of agricultural data acquisition.
This advancement supports informed decision-making in crop breeding programs.