Automated Detection and Counting of Gossypium barbadense Fruits in Peruvian Crops Using Convolutional Neural Networks
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| Publicado en: | AgriEngineering vol. 7, no. 5 (2025), p. 152 |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3211845950 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2624-7402 | ||
| 024 | 7 | |a 10.3390/agriengineering7050152 |2 doi | |
| 035 | |a 3211845950 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Ballena-Ruiz, Juan | |
| 245 | 1 | |a Automated Detection and Counting of <i>Gossypium barbadense</i> Fruits in Peruvian Crops Using Convolutional Neural Networks | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study presents the development of a system based on convolutional neural networks for the automated detection and counting of Gossypium barbadense fruits, specifically the IPA cotton variety, during its maturation stage, known as “mota”, in crops located in the Lambayeque region of northern Peru. To achieve this, a dataset was created using images captured with a mobile device. After applying data augmentation techniques, the dataset consisted of 2186 images with 70,348 labeled fruits. Five deep learning models were trained: two variants of YOLO version 8 (nano and extra-large), two of YOLO version 11, and one based on the Faster R-CNN architecture. The dataset was split into 70% for training, 15% for validation, and 15% for testing, and all models were trained over 100 epochs with a batch size of 8. The extra-large YOLO models achieved the highest performance, with precision scores of 99.81% and 99.78%, respectively, and strong recall and F1-score values. In contrast, the nano models and Faster R-CNN showed slightly lower effectiveness. Additionally, the best-performing model was integrated into a web application developed in Python, enabling automated fruit counting from field images. The YOLO architecture emerged as an efficient and robust alternative for the automated detection of cotton fruits and stood out for its capability to process images in real time with high precision. Furthermore, its implementation in crop monitoring facilitates production estimation and decision-making in precision agriculture. | |
| 653 | |a Accuracy | ||
| 653 | |a Cotton | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Agricultural production | ||
| 653 | |a Models | ||
| 653 | |a Applications programs | ||
| 653 | |a Fruits | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Crops | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Crop production | ||
| 653 | |a Automation | ||
| 653 | |a Machine learning | ||
| 653 | |a Efficiency | ||
| 653 | |a Agriculture | ||
| 653 | |a Data augmentation | ||
| 653 | |a Computer vision | ||
| 653 | |a Precision agriculture | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 653 | |a Data collection | ||
| 653 | |a Images | ||
| 653 | |a Object recognition | ||
| 653 | |a Decision making | ||
| 653 | |a Gossypium barbadense | ||
| 700 | 1 | |a Arcila-Diaz, Juan | |
| 700 | 1 | |a Tuesta-Monteza Victor | |
| 773 | 0 | |t AgriEngineering |g vol. 7, no. 5 (2025), p. 152 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211845950/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211845950/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211845950/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |