Using Data-Driven Computer Vision Techniques to Improve Wheat Yield Prediction
Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution im...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/6/4/269 |
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| author | Merima Smajlhodžić-Deljo Madžida Hundur Hiyari Lejla Gurbeta Pokvić Nejra Merdović Faruk Bećirović Lemana Spahić Željana Grbović Dimitrije Stefanović Ivana Miličić Oskar Marko |
| author_facet | Merima Smajlhodžić-Deljo Madžida Hundur Hiyari Lejla Gurbeta Pokvić Nejra Merdović Faruk Bećirović Lemana Spahić Željana Grbović Dimitrije Stefanović Ivana Miličić Oskar Marko |
| author_sort | Merima Smajlhodžić-Deljo |
| collection | DOAJ |
| description | Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution images captured by unmanned aerial vehicles (UAVs). Drone-based images were captured during the late growth stage of wheat across 15 fields in Bosnia and Herzegovina. The images, processed to a resolution of 1024 × 1024 pixels, were manually annotated with regions of interest (ROIs) containing wheat ears. A dataset consisting of 556 high-resolution images was compiled, and advanced models including Faster R-CNN, YOLOv8, and RT-DETR were utilised for ear detection. The study found that although lower-quality images had a minor effect on detection accuracy, they did not significantly hinder the overall performance of the models. This research demonstrates the potential of digital technologies, particularly machine learning and UAVs, in transforming traditional agricultural practices. The novel application of automated ear counting via machine learning provides a scalable, efficient solution for yield prediction, enhancing sustainability and competitiveness in agriculture. |
| format | Article |
| id | doaj-art-3622ccd6cd86441687ebec8525c1e1d0 |
| institution | DOAJ |
| issn | 2624-7402 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-3622ccd6cd86441687ebec8525c1e1d02025-08-20T02:57:08ZengMDPI AGAgriEngineering2624-74022024-12-01644704471910.3390/agriengineering6040269Using Data-Driven Computer Vision Techniques to Improve Wheat Yield PredictionMerima Smajlhodžić-Deljo0Madžida Hundur Hiyari1Lejla Gurbeta Pokvić2Nejra Merdović3Faruk Bećirović4Lemana Spahić5Željana Grbović6Dimitrije Stefanović7Ivana Miličić8Oskar Marko9Verlab Research Institute for Biomedical Engineering, Medical Devices and Artificial Intelligence, 71 000 Sarajevo, Bosnia and HerzegovinaVerlab Research Institute for Biomedical Engineering, Medical Devices and Artificial Intelligence, 71 000 Sarajevo, Bosnia and HerzegovinaVerlab Research Institute for Biomedical Engineering, Medical Devices and Artificial Intelligence, 71 000 Sarajevo, Bosnia and HerzegovinaVerlab Research Institute for Biomedical Engineering, Medical Devices and Artificial Intelligence, 71 000 Sarajevo, Bosnia and HerzegovinaVerlab Research Institute for Biomedical Engineering, Medical Devices and Artificial Intelligence, 71 000 Sarajevo, Bosnia and HerzegovinaVerlab Research Institute for Biomedical Engineering, Medical Devices and Artificial Intelligence, 71 000 Sarajevo, Bosnia and HerzegovinaBioSense Institute, University of Novi Sad, 21000 Novi Sad, SerbiaBioSense Institute, University of Novi Sad, 21000 Novi Sad, SerbiaBioSense Institute, University of Novi Sad, 21000 Novi Sad, SerbiaBioSense Institute, University of Novi Sad, 21000 Novi Sad, SerbiaAccurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution images captured by unmanned aerial vehicles (UAVs). Drone-based images were captured during the late growth stage of wheat across 15 fields in Bosnia and Herzegovina. The images, processed to a resolution of 1024 × 1024 pixels, were manually annotated with regions of interest (ROIs) containing wheat ears. A dataset consisting of 556 high-resolution images was compiled, and advanced models including Faster R-CNN, YOLOv8, and RT-DETR were utilised for ear detection. The study found that although lower-quality images had a minor effect on detection accuracy, they did not significantly hinder the overall performance of the models. This research demonstrates the potential of digital technologies, particularly machine learning and UAVs, in transforming traditional agricultural practices. The novel application of automated ear counting via machine learning provides a scalable, efficient solution for yield prediction, enhancing sustainability and competitiveness in agriculture.https://www.mdpi.com/2624-7402/6/4/269precision agriculturewheat ear countingartificial intelligencecomputer visionreal-time processingdetection |
| spellingShingle | Merima Smajlhodžić-Deljo Madžida Hundur Hiyari Lejla Gurbeta Pokvić Nejra Merdović Faruk Bećirović Lemana Spahić Željana Grbović Dimitrije Stefanović Ivana Miličić Oskar Marko Using Data-Driven Computer Vision Techniques to Improve Wheat Yield Prediction AgriEngineering precision agriculture wheat ear counting artificial intelligence computer vision real-time processing detection |
| title | Using Data-Driven Computer Vision Techniques to Improve Wheat Yield Prediction |
| title_full | Using Data-Driven Computer Vision Techniques to Improve Wheat Yield Prediction |
| title_fullStr | Using Data-Driven Computer Vision Techniques to Improve Wheat Yield Prediction |
| title_full_unstemmed | Using Data-Driven Computer Vision Techniques to Improve Wheat Yield Prediction |
| title_short | Using Data-Driven Computer Vision Techniques to Improve Wheat Yield Prediction |
| title_sort | using data driven computer vision techniques to improve wheat yield prediction |
| topic | precision agriculture wheat ear counting artificial intelligence computer vision real-time processing detection |
| url | https://www.mdpi.com/2624-7402/6/4/269 |
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