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A Novel Locating System for Cereal Plant Stem Emerging Points' Detection Using a Convolutional Neural Network.

Hadi Karimi^1,2, Søren Skovsen³, Mads Dyrmann⁴

¹Department of Biosystems Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 29 Bahman Boulevard, Tabriz 5166616471, Iran. hadi.karimi@tabrizu.ac.ir.

Sensors (Basel, Switzerland)

|May 23, 2018

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This study developed a system to locate cereal plant stem emerging points (PSEPs) using a neural network. The model accurately counts PSEPs, aiding in assessing seed drill performance and crop distribution quality.

Area of Science:

Agricultural Engineering
Computer Vision
Plant Science

Background:

Precise plant location is crucial for targeted agricultural treatments and evaluating sowing performance.
Current methods lack the ability to individually track plants across a field, hindering optimized crop management.

Purpose of the Study:

To develop an automated system for accurately locating cereal plant stem emerging points (PSEPs).
To enhance the precision of plant emergence point detection using a fully-convolutional neural network.
To provide a tool for evaluating seed drill performance and crop distribution quality.

Main Methods:

A fully-convolutional neural network was trained on 5719 images from cereal fields.
Manual marking of 212 images identified cereal plant stem emerging points (PSEPs) for training.

Keywords:

cereal plants distribution sowing performance

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A cost function incorporating penalty regions was developed to improve PSEP localization accuracy.

Main Results:

The developed system achieved a coefficient of determination of approximately 87% in counting PSEPs.
The inclusion of penalty regions significantly enhanced the network's ability to precisely locate plant emergence points.
The model demonstrated reliable PSEP counting, indicating its potential for field analysis.

Conclusions:

The developed system reliably identifies and counts cereal plant stem emerging points (PSEPs).
This technology offers a valuable assessment of seed drill performance and crop distribution uniformity.
Automated PSEP detection facilitates improved precision agriculture practices and crop management strategies.