Estimation of Mango Fruit Production Using Image Analysis and Machine Learning Algorithms
Mango production is fundamental to the agricultural economy, generating income and employment in various communities. Accurate estimation of its production optimizes the planning and logistics of harvesting; traditionally, manual methods are inefficient and prone to errors. Currently, machine learni...
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MDPI AG
2024-11-01
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| Series: | Informatics |
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| Online Access: | https://www.mdpi.com/2227-9709/11/4/87 |
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| author | Liliana Arcila-Diaz Heber I. Mejia-Cabrera Juan Arcila-Diaz |
| author_facet | Liliana Arcila-Diaz Heber I. Mejia-Cabrera Juan Arcila-Diaz |
| author_sort | Liliana Arcila-Diaz |
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| description | Mango production is fundamental to the agricultural economy, generating income and employment in various communities. Accurate estimation of its production optimizes the planning and logistics of harvesting; traditionally, manual methods are inefficient and prone to errors. Currently, machine learning, by handling large volumes of data, emerges as an innovative solution to enhance the precision of mango production estimation. This study presents an analysis of mango fruit detection using machine learning algorithms, specifically YOLO version 8 and Faster R-CNN. The present study employs a dataset consisting of 212 original images, annotated with a total of 9604 labels, which has been expanded to include 2449 additional images and 116,654 annotations. This significant increase in dataset size notably enhances the robustness and generalization capacity of the model. The YOLO-trained model achieves an accuracy of 96.72%, a recall of 77.4%, and an F1 Score of 86%, compared to the results of Faster R-CNN, which are 98.57%, 63.80%, and 77.46%, respectively. YOLO demonstrates greater efficiency, being faster in training, consuming less memory, and utilizing fewer CPU resources. Furthermore, this study has developed a web application with a user interface that facilitates the uploading of images from mango trees considered samples. The YOLO-trained model detects the fruits of each tree in the representative sample and uses extrapolation techniques to estimate the total number of fruits across the entire population of mango trees. |
| format | Article |
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| institution | Kabale University |
| issn | 2227-9709 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Informatics |
| spelling | doaj-art-2ddab8b6bee149ada35bf97ab8b6cea32024-12-27T14:30:38ZengMDPI AGInformatics2227-97092024-11-011148710.3390/informatics11040087Estimation of Mango Fruit Production Using Image Analysis and Machine Learning AlgorithmsLiliana Arcila-Diaz0Heber I. Mejia-Cabrera1Juan Arcila-Diaz2Escuela de Ingeniería de Sistemas, Universidad Señor de Sipán, Chiclayo 14000, PeruEscuela de Ingeniería de Sistemas, Universidad Señor de Sipán, Chiclayo 14000, PeruEscuela de Ingeniería de Sistemas, Universidad Señor de Sipán, Chiclayo 14000, PeruMango production is fundamental to the agricultural economy, generating income and employment in various communities. Accurate estimation of its production optimizes the planning and logistics of harvesting; traditionally, manual methods are inefficient and prone to errors. Currently, machine learning, by handling large volumes of data, emerges as an innovative solution to enhance the precision of mango production estimation. This study presents an analysis of mango fruit detection using machine learning algorithms, specifically YOLO version 8 and Faster R-CNN. The present study employs a dataset consisting of 212 original images, annotated with a total of 9604 labels, which has been expanded to include 2449 additional images and 116,654 annotations. This significant increase in dataset size notably enhances the robustness and generalization capacity of the model. The YOLO-trained model achieves an accuracy of 96.72%, a recall of 77.4%, and an F1 Score of 86%, compared to the results of Faster R-CNN, which are 98.57%, 63.80%, and 77.46%, respectively. YOLO demonstrates greater efficiency, being faster in training, consuming less memory, and utilizing fewer CPU resources. Furthermore, this study has developed a web application with a user interface that facilitates the uploading of images from mango trees considered samples. The YOLO-trained model detects the fruits of each tree in the representative sample and uses extrapolation techniques to estimate the total number of fruits across the entire population of mango trees.https://www.mdpi.com/2227-9709/11/4/87detection of fruitsproduction estimationYOLOfaster R-CNNextrapolation |
| spellingShingle | Liliana Arcila-Diaz Heber I. Mejia-Cabrera Juan Arcila-Diaz Estimation of Mango Fruit Production Using Image Analysis and Machine Learning Algorithms Informatics detection of fruits production estimation YOLO faster R-CNN extrapolation |
| title | Estimation of Mango Fruit Production Using Image Analysis and Machine Learning Algorithms |
| title_full | Estimation of Mango Fruit Production Using Image Analysis and Machine Learning Algorithms |
| title_fullStr | Estimation of Mango Fruit Production Using Image Analysis and Machine Learning Algorithms |
| title_full_unstemmed | Estimation of Mango Fruit Production Using Image Analysis and Machine Learning Algorithms |
| title_short | Estimation of Mango Fruit Production Using Image Analysis and Machine Learning Algorithms |
| title_sort | estimation of mango fruit production using image analysis and machine learning algorithms |
| topic | detection of fruits production estimation YOLO faster R-CNN extrapolation |
| url | https://www.mdpi.com/2227-9709/11/4/87 |
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