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Related Experiment Video

Updated: Jul 16, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging.

M A Batin¹, Muhaiminul Islam¹, Md Mehedi Hasan¹

¹Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh.

Frontiers in Plant Science

|September 11, 2023

Summary

Accurate wheat spike counting is crucial for phenotyping and yield estimation. This study introduces WheatSpikeNet, an enhanced instance segmentation model that achieves state-of-the-art performance in detecting and segmenting wheat spikes from field images.

Keywords:

cascade RCNN deformable convolution network plant phenotyping segmentation wheat spikes

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Last Updated: Jul 16, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Published on: February 2, 2019

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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Published on: March 28, 2025

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Area of Science:

Agricultural Science
Computer Vision
Plant Breeding

Background:

Plant phenotyping is vital for identifying desirable crop traits and assessing environmental impacts.
Wheat spike density is a key agronomic factor, but accurate counting in diverse field conditions remains challenging.

Purpose of the Study:

To develop an efficient and accurate method for segmenting and counting wheat spikes in field imagery.
To introduce a curated dataset (SPIKE-segm) and an optimized instance segmentation model (WheatSpikeNet).

Main Methods:

Utilized the Cascade Mask RCNN architecture with enhancements and hyperparameter tuning.
Employed ResNet50 with Deformable Convolution Network (DCN) as the backbone, Generic RoI Extractor (GRoIE), and Side Aware Boundary Localization (SABL).
Conducted comprehensive ablation analysis to determine the most efficient model components.

Main Results:

WheatSpikeNet achieved superior performance on the SPIKE dataset with bbox and mask mAP scores of 0.9303 and 0.9416, respectively.
Demonstrated state-of-the-art detection and segmentation capabilities, outperforming existing methods.
Achieved up to 0.41% mAP improvement in spike detection and 3.46% mAP improvement in segmentation tasks.

Conclusions:

The proposed WheatSpikeNet model offers a robust solution for accurate wheat spike segmentation and counting.
This advancement facilitates precise yield estimation in wheat breeding programs.
The optimized model and dataset contribute to improving automated plant phenotyping techniques.