Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones
Integrating deep learning for crop monitoring presents opportunities and challenges, particularly in object detection under varying environmental conditions. This study investigates the efficacy of image preprocessing methods for olive identification using mobile cameras under natural light. The res...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | AgriEngineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-7402/6/1/10 |
| _version_ | 1797242424882364416 |
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| author | David Mojaravscki Paulo S. Graziano Magalhães |
| author_facet | David Mojaravscki Paulo S. Graziano Magalhães |
| author_sort | David Mojaravscki |
| collection | DOAJ |
| description | Integrating deep learning for crop monitoring presents opportunities and challenges, particularly in object detection under varying environmental conditions. This study investigates the efficacy of image preprocessing methods for olive identification using mobile cameras under natural light. The research is grounded in the broader context of enhancing object detection accuracy in variable lighting, which is crucial for practical applications in precision agriculture. The study primarily employs the YOLOv7 object detection model and compares various color correction techniques, including histogram equalization (HE), adaptive histogram equalization (AHE), and color correction using the ColorChecker. Additionally, the research examines the role of data augmentation methods, such as image and bounding box rotation, in conjunction with these preprocessing techniques. The findings reveal that while all preprocessing methods improve detection performance compared to non-processed images, AHE is particularly effective in dealing with natural lighting variability. The study also demonstrates that image rotation augmentation consistently enhances model accuracy across different preprocessing methods. These results contribute significantly to agricultural technology, highlighting the importance of tailored image preprocessing in object detection models. The conclusions drawn from this research offer valuable insights for optimizing deep learning applications in agriculture, particularly in scenarios with inconsistent environmental conditions. |
| first_indexed | 2024-04-24T18:39:00Z |
| format | Article |
| id | doaj.art-96f64d71513640b9af24dee11183444c |
| institution | Directory Open Access Journal |
| issn | 2624-7402 |
| language | English |
| last_indexed | 2024-04-24T18:39:00Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj.art-96f64d71513640b9af24dee11183444c2024-03-27T13:16:17ZengMDPI AGAgriEngineering2624-74022024-01-016115517010.3390/agriengineering6010010Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell PhonesDavid Mojaravscki0Paulo S. Graziano Magalhães1School of Agricultural Engineering, Campinas State University (UNICAMP), Campinas 13083-875, BrazilSchool of Agricultural Engineering, Campinas State University (UNICAMP), Campinas 13083-875, BrazilIntegrating deep learning for crop monitoring presents opportunities and challenges, particularly in object detection under varying environmental conditions. This study investigates the efficacy of image preprocessing methods for olive identification using mobile cameras under natural light. The research is grounded in the broader context of enhancing object detection accuracy in variable lighting, which is crucial for practical applications in precision agriculture. The study primarily employs the YOLOv7 object detection model and compares various color correction techniques, including histogram equalization (HE), adaptive histogram equalization (AHE), and color correction using the ColorChecker. Additionally, the research examines the role of data augmentation methods, such as image and bounding box rotation, in conjunction with these preprocessing techniques. The findings reveal that while all preprocessing methods improve detection performance compared to non-processed images, AHE is particularly effective in dealing with natural lighting variability. The study also demonstrates that image rotation augmentation consistently enhances model accuracy across different preprocessing methods. These results contribute significantly to agricultural technology, highlighting the importance of tailored image preprocessing in object detection models. The conclusions drawn from this research offer valuable insights for optimizing deep learning applications in agriculture, particularly in scenarios with inconsistent environmental conditions.https://www.mdpi.com/2624-7402/6/1/10color correction techniquesobject detectiondata augmentation |
| spellingShingle | David Mojaravscki Paulo S. Graziano Magalhães Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones AgriEngineering color correction techniques object detection data augmentation |
| title | Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones |
| title_full | Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones |
| title_fullStr | Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones |
| title_full_unstemmed | Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones |
| title_short | Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones |
| title_sort | comparative evaluation of color correction as image preprocessing for olive identification under natural light using cell phones |
| topic | color correction techniques object detection data augmentation |
| url | https://www.mdpi.com/2624-7402/6/1/10 |
| work_keys_str_mv | AT davidmojaravscki comparativeevaluationofcolorcorrectionasimagepreprocessingforoliveidentificationundernaturallightusingcellphones AT paulosgrazianomagalhaes comparativeevaluationofcolorcorrectionasimagepreprocessingforoliveidentificationundernaturallightusingcellphones |