ArticleSensors (Basel, Switzerland)2020
Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Advanced deep learning vision transformer models for intelligent grain counting in agricultural data analytics.Scientific reports · 2026Article
- OpenEar: an ultra-affordable, high-throughput, and accurate maize ear phenotyping system.Plant methods · 2026Article
- Detection of surface defects in soybean seeds based on improved Yolov9.Scientific reports · 2025Article
- Visual Mamba UNet fusion multi-scale attention and detail infusion for unsound corn kernels segmentation.Scientific reports · 2025Article
- Toward automated plantar pressure analysis: machine learning-based segmentation and key point detection across multicenter data.Frontiers in bioengineering and biotechnology · 2025Article
- Ta-YOLO: overcoming target blocked challenges in greenhouse tomato detection and counting.Frontiers in plant science · 2025Article
- Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies.Plants (Basel, Switzerland) · 2024Review
- Digital techniques and trends for seed phenotyping using optical sensors.Journal of advanced research · 2024Review
- Rice Grain Detection and Counting Method Based on TCLE-YOLO Model.Sensors (Basel, Switzerland) · 2023Article
- Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.Frontiers in plant science · 2023Article
- Corn planting and harvest scheduling under storage capacity and growing degree units uncertainty.Scientific reports · 2022Article
- Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits.Plant methods · 2022Article
- Towards a better understanding of annotation tools for medical imaging: a survey.Multimedia tools and applications · 2022Article
- High-throughput image-based plant stand count estimation using convolutional neural networks.PloS one · 2022Article
- Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology.Plant reproduction · 2021Article
- Automated Counting Grains on the Rice Panicle Based on Deep Learning Method.Sensors (Basel, Switzerland) · 2021Article
- Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology.Frontiers in plant science · 2021Article
- Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels.Frontiers in robotics and AI · 2021Article
- Dataset of annotated food crops and weed images for robotic computer vision control.Data in brief · 2020Article
- CSensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detects and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the ( x , y ) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.