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Computer Vision-Based Biomass Estimation for Invasive Plants

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Predicting wheat yield and grain quality with UAV multispectral imagery and deep learning.

Mohammad Maruf Billah¹, Maitiniyazi Maimaitijiang¹, Swas Kaushal²

¹Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States.

Frontiers in Plant Science

|June 15, 2026

Summary

Deep learning models using multitemporal drone imagery can accurately predict winter wheat grain yield and quality. This technology aids in high-throughput phenotyping and site-specific crop management.

Keywords:

deep learning (DL)high-throughput phenotyping (HTP)protein and test weight estimation unmanned aerial vehicles (UAV)wheat yield

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Area of Science:

Agricultural Science
Remote Sensing
Artificial Intelligence

Background:

Accurate prediction of wheat grain yield (GY), grain protein content (GP), and grain test weight (TW) is crucial for crop management and high-throughput phenotyping (HTP).
Unmanned Aerial Vehicle (UAV) remote sensing, combined with artificial intelligence (AI) and deep learning (DL), offers a promising approach for rapid, plot-scale trait estimation.

Purpose of the Study:

To investigate the utility of multitemporal, multispectral UAV imagery for predicting winter wheat GY, GP, and TW.
To compare the performance of handcrafted feature-based versus end-to-end image-based deep learning workflows for trait prediction.

Main Methods:

Multispectral UAV data were collected across seven experimental wheat sites during the 2022 growing season.
Two modeling paradigms were evaluated: handcrafted feature-based (Support Vector Regression, Random Forest Regression, Deep Neural Network, 1D-CNN) and image-based end-to-end (2D-CNN, 3D-CNN, 2D-CNN-LSTM).
Model performance was assessed for predicting GY, GP, and TW using data from different growth stages and multitemporal imagery.

Main Results:

Image-based deep learning workflows performed comparably or slightly better than handcrafted feature-based workflows.
The 3D-CNN model achieved the highest prediction accuracy, with R² values of 0.65 for GY, 0.61 for GP, and 0.69 for TW.
Multitemporal UAV data consistently outperformed single-stage data, with the Feekes 10 (booting) stage showing slightly superior results (R²: 0.62 GY, 0.55 GP, 0.62 TW).

Conclusions:

Deep learning applied to high-resolution, multitemporal UAV imagery shows significant potential for in-season prediction of winter wheat yield and grain quality.
These findings support the integration of UAV remote sensing and DL for advancing HTP and enabling site-specific crop management strategies.