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Related Experiment Video

Updated: Jun 13, 2025

Protocols for Quantifying Transferable Pesticide Residues in Turfgrass Systems

Protocols for Quantifying Transferable Pesticide Residues in Turfgrass Systems

Published on: March 15, 2017

Deep learning-based weed detection for precision herbicide application in turf.

Xiaojun Jin^1,2, Hua Zhao¹, Xiaotong Kong²

¹College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.

Pest Management Science

|March 1, 2025

Summary

This summary is machine-generated.

This study demonstrates that deep convolutional neural networks (DCNNs) can create accurate weed maps for targeted herbicide application. Integrating these maps with path-planning algorithms on smart sprayers significantly reduces herbicide use.

Keywords:

computer vision deep learning precision herbicide application weed detection weed mapping

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Last Updated: Jun 13, 2025

Protocols for Quantifying Transferable Pesticide Residues in Turfgrass Systems

Protocols for Quantifying Transferable Pesticide Residues in Turfgrass Systems

Published on: March 15, 2017

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

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

Published on: September 7, 2015

Area of Science:

Agricultural Science
Computer Science
Robotics

Background:

Precision weed mapping is crucial for efficient herbicide application in turf management.
Smart sprayers require accurate weed identification and susceptibility data for targeted spraying.
Deep convolutional neural networks (DCNNs) offer potential for advanced weed mapping.

Purpose of the Study:

To evaluate the feasibility of herbicide susceptibility-based weed mapping using DCNNs.
To facilitate targeted and efficient herbicide applications through advanced mapping.
To optimize herbicide application paths using path-planning algorithms.

Main Methods:

Implemented DCNNs (DenseNet, GoogLeNet, ResNet) for weed mapping based on herbicide susceptibility.
Compared the performance of various DCNN models in terms of accuracy and efficiency.
Applied path-planning algorithms (Christofides, Greedy, 2-opt) to optimize spraying nozzle trajectories.

Main Results:

ResNet model demonstrated high accuracy (0.9980) and efficiency for weed detection.
DenseNet achieved excellent F1 scores (0.992-0.999) across all herbicide categories.
The Greedy algorithm was most efficient for optimizing nozzle path planning.

Conclusions:

Herbicide susceptibility-based weed mapping enables precise herbicide application by targeting susceptible weeds.
Integrating weed mapping with optimized path planning on smart sprayers can significantly reduce overall herbicide input.
This approach enhances the efficiency and environmental sustainability of turf weed management.