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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: Jan 7, 2026

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

A keypoint-based method for detecting weed growth points in corn field environments.

Mochen Liu^1,2, Xiaoli Xu¹, Tingdong Tian¹

¹College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, Shandong, 271018, China.

Plant Phenomics (Washington, D.C.)

|December 19, 2025

Summary

This study introduces SRD-YOLO, a precise weed detection system for corn fields. It accurately locates weed growth points, improving weed control and corn yield even in challenging conditions.

Keywords:

Corn seedlings Growth points Keypoints Precision weeding Weed detection

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Last Updated: Jan 7, 2026

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Published on: February 2, 2019

Protocols for Robust Herbicide Resistance Testing in Different Weed Species

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Published on: July 2, 2015

Measuring Rates of Herbicide Metabolism in Dicot Weeds with an Excised Leaf Assay

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Published on: September 7, 2015

Area of Science:

Agricultural Engineering
Computer Vision
Machine Learning

Background:

Weed growth significantly reduces corn yield, necessitating advanced weed management strategies.
Precision weeding requires accurate detection and localization of weed growth points, especially during early growth stages.
Field conditions like occlusion, dense growth, and variable lighting pose significant challenges for current weed detection systems.

Purpose of the Study:

To develop a precise weed growth point detection method for corn fields.
To enhance weed detection accuracy and robustness in complex agricultural environments.
To create a lightweight and efficient model for real-time weed control applications.

Main Methods:

Proposed a keypoint pose estimation model for detecting various weed species and their growth points.
Designed a dilation-wise residual module (DWRM) to handle occlusion and dense growth.
Incorporated a separation and enhancement attention module (SEAM) for improved pose estimation.
Utilized the RepViT block (RVB) for model lightweighting to suit field computational constraints.

Main Results:

The SRD-YOLO model achieved a keypoint mean average precision (mAP_kpt) of 96.5% and an F1 score of 94%.
The system demonstrated a high processing speed with 169 frames per second (FPS).
Model parameters were reduced by 8.7 million, indicating significant lightweighting.

Conclusions:

SRD-YOLO effectively meets the demands for growth point localization in challenging corn field conditions.
The developed method provides robust technical support for real-time and precise weed control in agriculture.
This advancement contributes to more efficient and sustainable corn production through improved weed management.