Apple (Malus domestica) and pear (Pyrus communis) yield prediction after tree image analysis
Yield forecasting depends on accurate tree fruit counts and mean size estimation. This information is generally obtained manually, requiring many hours of work. Artificial vision emerges as an interesting alternative to obtaining more information in less time. This study aimed to test and train YOLO...
| Autores principales: | , , , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
| Publicado: |
Facultad de Ciencias Agrarias, Universidad Nacional de Cuyo
2023
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/16325 https://revistas.uncu.edu.ar/ojs3/index.php/RFCA/article/view/6452 https://doi.org/10.48162/rev.39.104 |
| Sumario: | Yield forecasting depends on accurate tree fruit counts and mean size estimation. This information is generally obtained manually, requiring many hours of work. Artificial vision emerges as an interesting alternative to obtaining more information in less time. This study aimed to test and train YOLO pre-trained models based on neural networks for the detection and count of pears and apples on trees after image analysis; while also estimating fruit size. Images of trees were taken during the day and at night in apple and pear trees while fruits were manually counted. Trained models were evaluated according to recall, precision and F1score. The correlation between detected and counted fruits was calculated while fruit size estimation was made after drawing straight lines on each fruit and using reference elements. The precision, recall and F1score achieved by the models were up to 0.86, 0.83 and 0.84, respectively. Correlation coefficients between fruit sizes measured manually and by images were 0.73 for apples and 0.80 for pears. The proposed methodologies showed promising results, allowing forecasters to make less time-consuming and accurate estimates compared to manual measurements. |
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