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 YOL...
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| Autores principales: | , , , , |
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| Formato: | Online |
| Lenguaje: | eng |
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Facultad de Ciencias Agrarias-UNCuyo
2023
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| Materias: | |
| Acceso en línea: | https://revistas.uncu.edu.ar/ojs3/index.php/RFCA/article/view/6452 |
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I11-R107article-6452 |
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Universidad Nacional de Cuyo |
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Revistas en línea |
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Revistas en línea |
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Revista de la Facultad de Ciencias Agrarias |
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Revista de la Facultad de Ciencias Agrarias |
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I-11 |
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R-107 |
| language |
eng |
| format |
Online |
| author |
del Brio, Dolores Tassile, Valentin Bramardi, Sergio Jorge Fernández, Darío Eduardo Reeb, Pablo Daniel |
| spellingShingle |
del Brio, Dolores Tassile, Valentin Bramardi, Sergio Jorge Fernández, Darío Eduardo Reeb, Pablo Daniel Apple (Malus domestica) and pear (Pyrus communis) yield prediction after tree image analysis detección de frutos visión artificial predicción de cosecha Malus domestica Pyrus communis fruit detection artificial vision yield forecast Malus domestica Pyrus communis |
| author_facet |
del Brio, Dolores Tassile, Valentin Bramardi, Sergio Jorge Fernández, Darío Eduardo Reeb, Pablo Daniel |
| author_sort |
del Brio, Dolores |
| title |
Apple (Malus domestica) and pear (Pyrus communis) yield prediction after tree image analysis |
| title_short |
Apple (Malus domestica) and pear (Pyrus communis) yield prediction after tree image analysis |
| title_full |
Apple (Malus domestica) and pear (Pyrus communis) yield prediction after tree image analysis |
| title_fullStr |
Apple (Malus domestica) and pear (Pyrus communis) yield prediction after tree image analysis |
| title_full_unstemmed |
Apple (Malus domestica) and pear (Pyrus communis) yield prediction after tree image analysis |
| title_sort |
apple (malus domestica) and pear (pyrus communis) yield prediction after tree image analysis |
| description |
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.
Highlights:
The number of fruits in apple and pear trees, could be estimated from images with promising results.
The possibility of estimating the fruit numbers from images could reduce the time spent on this task, and above all, the costs. This allow growers to increase the number of trees sampled to make yield forecasts.
|
| publisher |
Facultad de Ciencias Agrarias-UNCuyo |
| publishDate |
2023 |
| url |
https://revistas.uncu.edu.ar/ojs3/index.php/RFCA/article/view/6452 |
| topic |
detección de frutos visión artificial predicción de cosecha Malus domestica Pyrus communis fruit detection artificial vision yield forecast Malus domestica Pyrus communis |
| topic_facet |
detección de frutos visión artificial predicción de cosecha Malus domestica Pyrus communis fruit detection artificial vision yield forecast Malus domestica Pyrus communis |
| work_keys_str_mv |
AT delbriodolores applemalusdomesticaandpearpyruscommunisyieldpredictionaftertreeimageanalysis AT tassilevalentin applemalusdomesticaandpearpyruscommunisyieldpredictionaftertreeimageanalysis AT bramardisergiojorge applemalusdomesticaandpearpyruscommunisyieldpredictionaftertreeimageanalysis AT fernandezdarioeduardo applemalusdomesticaandpearpyruscommunisyieldpredictionaftertreeimageanalysis AT reebpablodaniel applemalusdomesticaandpearpyruscommunisyieldpredictionaftertreeimageanalysis |
| _version_ |
1800220956400025600 |
| spelling |
I11-R107article-64522023-12-18T19:07:45Z Apple (Malus domestica) and pear (Pyrus communis) yield prediction after tree image analysis Apple (Malus domestica) and pear (Pyrus communis) yield prediction after tree image analysis del Brio, Dolores Tassile, Valentin Bramardi, Sergio Jorge Fernández, Darío Eduardo Reeb, Pablo Daniel detección de frutos visión artificial predicción de cosecha Malus domestica Pyrus communis fruit detection artificial vision yield forecast Malus domestica Pyrus communis 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. Highlights: The number of fruits in apple and pear trees, could be estimated from images with promising results. The possibility of estimating the fruit numbers from images could reduce the time spent on this task, and above all, the costs. This allow growers to increase the number of trees sampled to make yield forecasts. 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. Highlights: The number of fruits in apple and pear trees, could be estimated from images with promising results. The possibility of estimating the fruit numbers from images could reduce the time spent on this task, and above all, the costs. This allow growers to increase the number of trees sampled to make yield forecasts. Facultad de Ciencias Agrarias-UNCuyo 2023-12-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.uncu.edu.ar/ojs3/index.php/RFCA/article/view/6452 10.48162/rev.39.104 Revista de la Facultad de Ciencias Agrarias UNCuyo; Vol. 55 No. 2 (2023): July-December; 1-11 Revista de la Facultad de Ciencias Agrarias UNCuyo; Vol. 55 Núm. 2 (2023): Julio-Diciembre; 1-11 1853-8665 0370-4661 eng https://revistas.uncu.edu.ar/ojs3/index.php/RFCA/article/view/6452/5803 Derechos de autor 2018 Revista de la Facultad de Ciencias Agrarias UNCuyo https://creativecommons.org/licenses/by-nc-sa/3.0/deed.es |
