Flowering Intensity Estimation Using Computer Vision
Flowering intensity is an important parameter to predict and control fruit yield. However, its estimation is often based on subjective evaluations of fruit growers. This study explores the application of the YOLO framework for flowering intensity estimation. YOLO is a popular computer vision solutio...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | AgriEngineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-7402/7/4/117 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850183181207076864 |
|---|---|
| author | Sergejs Kodors Imants Zarembo Ilmars Apeinans Edgars Rubauskis Lienite Litavniece |
| author_facet | Sergejs Kodors Imants Zarembo Ilmars Apeinans Edgars Rubauskis Lienite Litavniece |
| author_sort | Sergejs Kodors |
| collection | DOAJ |
| description | Flowering intensity is an important parameter to predict and control fruit yield. However, its estimation is often based on subjective evaluations of fruit growers. This study explores the application of the YOLO framework for flowering intensity estimation. YOLO is a popular computer vision solution for object-detecting tasks. It was applied to detect flowers in different studies. Still, it requires manual annotation of photographs of flowering trees, which is a complex and time-consuming process. It is hard to distinguish individual flowers in photos due to their overlapping and indistinct outlines, false positive flowers in the background, and the density of flowers in panicles. Our experiment shows that the small dataset of images (320 × 320 px) is sufficient to achieve an accuracy of 0.995 and 0.994 mAP@50 for YOLOv9m and YOLOv11m using aggregated mosaic augmentation. The AI-based method was compared with the manual method (flowering intensity estimation, 0–9 scale). The comparison was completed using data analysis and the MobileNetV2 classifier as an evaluation model. The analysis shows that the AI-based method is more effective than the manual method. |
| format | Article |
| id | doaj-art-9b1088e9fcf04e9baf5b8ef0c899a232 |
| institution | OA Journals |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-9b1088e9fcf04e9baf5b8ef0c899a2322025-08-20T02:17:25ZengMDPI AGAgriEngineering2624-74022025-04-017411710.3390/agriengineering7040117Flowering Intensity Estimation Using Computer VisionSergejs Kodors0Imants Zarembo1Ilmars Apeinans2Edgars Rubauskis3Lienite Litavniece4Engineering Center, Rezekne Academy of Riga Technical University, LV-4601 Rezekne, LatviaEngineering Center, Rezekne Academy of Riga Technical University, LV-4601 Rezekne, LatviaEngineering Center, Rezekne Academy of Riga Technical University, LV-4601 Rezekne, LatviaInstitute of Horticulture (LatHort), LV-3701 Dobele, LatviaCentre for Economics and Governance, Rezekne Academy of Riga Technical University, LV-4601 Rezekne, LatviaFlowering intensity is an important parameter to predict and control fruit yield. However, its estimation is often based on subjective evaluations of fruit growers. This study explores the application of the YOLO framework for flowering intensity estimation. YOLO is a popular computer vision solution for object-detecting tasks. It was applied to detect flowers in different studies. Still, it requires manual annotation of photographs of flowering trees, which is a complex and time-consuming process. It is hard to distinguish individual flowers in photos due to their overlapping and indistinct outlines, false positive flowers in the background, and the density of flowers in panicles. Our experiment shows that the small dataset of images (320 × 320 px) is sufficient to achieve an accuracy of 0.995 and 0.994 mAP@50 for YOLOv9m and YOLOv11m using aggregated mosaic augmentation. The AI-based method was compared with the manual method (flowering intensity estimation, 0–9 scale). The comparison was completed using data analysis and the MobileNetV2 classifier as an evaluation model. The analysis shows that the AI-based method is more effective than the manual method.https://www.mdpi.com/2624-7402/7/4/117artificial intelligencecomputer visionflowering intensityhorticultureprecision agriculture |
| spellingShingle | Sergejs Kodors Imants Zarembo Ilmars Apeinans Edgars Rubauskis Lienite Litavniece Flowering Intensity Estimation Using Computer Vision AgriEngineering artificial intelligence computer vision flowering intensity horticulture precision agriculture |
| title | Flowering Intensity Estimation Using Computer Vision |
| title_full | Flowering Intensity Estimation Using Computer Vision |
| title_fullStr | Flowering Intensity Estimation Using Computer Vision |
| title_full_unstemmed | Flowering Intensity Estimation Using Computer Vision |
| title_short | Flowering Intensity Estimation Using Computer Vision |
| title_sort | flowering intensity estimation using computer vision |
| topic | artificial intelligence computer vision flowering intensity horticulture precision agriculture |
| url | https://www.mdpi.com/2624-7402/7/4/117 |
| work_keys_str_mv | AT sergejskodors floweringintensityestimationusingcomputervision AT imantszarembo floweringintensityestimationusingcomputervision AT ilmarsapeinans floweringintensityestimationusingcomputervision AT edgarsrubauskis floweringintensityestimationusingcomputervision AT lienitelitavniece floweringintensityestimationusingcomputervision |