Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis
In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leadi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Ital Publication
2024-04-01
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| Series: | Emerging Science Journal |
| Subjects: | |
| Online Access: | https://www.ijournalse.org/index.php/ESJ/article/view/2115 |
| _version_ | 1797198583526588416 |
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| author | Viviana Moya Angélica Quito Andrea Pilco Juan P. Vásconez Christian Vargas |
| author_facet | Viviana Moya Angélica Quito Andrea Pilco Juan P. Vásconez Christian Vargas |
| author_sort | Viviana Moya |
| collection | DOAJ |
| description | In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model’s efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model’s potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture.
Doi: 10.28991/ESJ-2024-08-02-08
Full Text: PDF |
| first_indexed | 2024-04-24T07:02:10Z |
| format | Article |
| id | doaj.art-8f7a0ede959247a9b474fccc40421d9a |
| institution | Directory Open Access Journal |
| issn | 2610-9182 |
| language | English |
| last_indexed | 2024-04-24T07:02:10Z |
| publishDate | 2024-04-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj.art-8f7a0ede959247a9b474fccc40421d9a2024-04-22T06:19:15ZengItal PublicationEmerging Science Journal2610-91822024-04-018249651210.28991/ESJ-2024-08-02-08630Crop Detection and Maturity Classification Using a YOLOv5-Based Image AnalysisViviana Moya0Angélica Quito1Andrea Pilco2Juan P. Vásconez3Christian Vargas4Faculty of Technical Sciences, International University of Ecuador UIDE, Quito 170411,Faculty of Technical Sciences, International University of Ecuador UIDE, Quito 170411,Faculty of Technical Sciences, International University of Ecuador UIDE, Quito 170411,Faculty of Engineering, Universidad Andres Bello, Santiago 7550196,Faculty of Technical Sciences, International University of Ecuador UIDE, Quito 170411,In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model’s efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model’s potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PDFhttps://www.ijournalse.org/index.php/ESJ/article/view/2115chili peppersdatasetdetectionclassificationyolov5. |
| spellingShingle | Viviana Moya Angélica Quito Andrea Pilco Juan P. Vásconez Christian Vargas Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis Emerging Science Journal chili peppers dataset detection classification yolov5. |
| title | Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis |
| title_full | Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis |
| title_fullStr | Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis |
| title_full_unstemmed | Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis |
| title_short | Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis |
| title_sort | crop detection and maturity classification using a yolov5 based image analysis |
| topic | chili peppers dataset detection classification yolov5. |
| url | https://www.ijournalse.org/index.php/ESJ/article/view/2115 |
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