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Related Experiment Video

Updated: Jul 9, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Published on: February 2, 2019

Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing.

Alexis Carlier¹, Sébastien Dandrifosse¹, Benjamin Dumont²

¹Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium.

Frontiers in Plant Science

|December 6, 2023

Summary

Deep learning models, particularly convolutional neural networks (CNNs), effectively estimate crop biophysical variables using advanced training techniques like pseudo-labeling. This approach overcomes data scarcity in crop phenotyping for improved yield prediction.

Keywords:

CNN PLSr biophysical variables close-range sensing multi-task phenotyping wheat

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A Rapid Laser Probing Method Facilitates the Non-invasive and Contact-free Determination of Leaf Thermal Properties

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Area of Science:

Agricultural Science
Remote Sensing
Computer Science (Deep Learning)

Background:

Accurate estimation of biophysical vegetation variables is crucial for crop monitoring and yield prediction.
Traditional remote sensing methods face challenges due to complex plant structures and the need for extensive feature engineering.
Limited labeled data hinders the application of deep learning, especially Convolutional Neural Networks (CNNs), in crop phenotyping for regression tasks.

Purpose of the Study:

To evaluate the effectiveness of various CNN models for predicting wheat dry matter, nitrogen uptake, and nitrogen concentration.
To address the challenge of limited labeled data in crop phenotyping using a novel training pipeline.
To compare CNN performance against traditional machine learning approaches for biophysical variable estimation.

Main Methods:

Utilized RGB and multispectral imagery from wheat tillering to maturity.
Developed a training pipeline incorporating transfer learning, pseudo-labeling of unlabeled data, and temporal relationship correction.
Compared the performance of different CNN architectures (EfficientNetB4, Resnet50) and a Partial Least Squares Regression (PLSr) model.

Main Results:

CNN models significantly improved prediction accuracy when employing the pseudo-labeling method.
EfficientNetB4 achieved the highest accuracy for above-ground biomass prediction (R² = 0.92).
Resnet50 excelled in predicting Leaf Area Index (LAI), nitrogen uptake, and nitrogen concentration (R² = 0.82, 0.73, and 0.80, respectively).

Conclusions:

CNNs, enhanced by pseudo-labeling, offer a promising and accessible solution for phenotyping quantitative crop biophysical variables.
The developed training pipeline effectively overcomes data scarcity issues in deep learning for crop science applications.
Further research is needed to fully realize the potential of CNNs in advanced crop phenotyping and management.