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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: Aug 31, 2025

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Cabbage and Weed Identification Based on Machine Learning and Target Spraying System Design.

Xueguan Zhao^1,2, Xiu Wang^1,2, Cuiling Li^1,2

¹Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.

Frontiers in Plant Science

|August 22, 2022

Summary

A novel system using an artificial light source and support vector machine (SVM) achieved 95.7% accuracy for cabbage identification and targeted pesticide spraying, significantly improving efficiency and reducing waste.

Keywords:

effective spraying rate independent nozzle control pesticide saving amount target identification target spraying

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Area of Science:

Agricultural Engineering
Computer Vision
Precision Agriculture

Background:

Field target identification accuracy and speed are limited by natural elements and algorithm processing.
Efficient and accurate pesticide spraying systems are crucial for practical agricultural applications.

Purpose of the Study:

To develop a cabbage identification and pesticide spraying control system using an artificial light source.
To optimize support vector machine (SVM) parameters and feature combinations for accurate plant identification.
To design a targeted spraying control system and communication protocol for efficient pesticide application.

Main Methods:

Developed a system utilizing an artificial light source for consistent illumination.
Employed support vector machine (SVM) classification with image skeleton point-to-line ratio and ring structure features.
Designed a targeted spraying control system with an active light source and a delay model, using an electronic control unit (ECU) communication protocol.

Main Results:

The optimal SVM feature vector (point-to-line ratio, max inscribed circle radius, fitted curve coefficient) achieved 95.7% identification accuracy with 33ms processing time.
Field tests showed average identification accuracies of 95.0% for cabbage and 93.5% for weeds.
Targeted spraying achieved an average effective spraying rate of 92.9%, with significant pesticide savings (33.8% to 53.3%) and void rate reduction (65% to 76.6%).

Conclusions:

The developed artificial light source-based system effectively identifies cabbage and enables precise pesticide spraying.
The optimized SVM model provides high accuracy and speed for real-time agricultural applications.
The system demonstrates substantial potential for reducing pesticide usage and improving application efficiency in precision agriculture.