Automated potato tuber mass estimation and grading with multiangle 2D images
Estimating potato tuber mass and size grading with computer vision can help breeders, farmers, and potato processing units reduce manual labor for potato post-harvest handling through optimized technology. The objective of the study was to estimate potato tuber mass and size grades using 2D images....
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525000656 |
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| author | Ayush K. Sharma Lincoln Zotarelli Alina Zare Lakesh K. Sharma |
| author_facet | Ayush K. Sharma Lincoln Zotarelli Alina Zare Lakesh K. Sharma |
| author_sort | Ayush K. Sharma |
| collection | DOAJ |
| description | Estimating potato tuber mass and size grading with computer vision can help breeders, farmers, and potato processing units reduce manual labor for potato post-harvest handling through optimized technology. The objective of the study was to estimate potato tuber mass and size grades using 2D images. Physical data of potato tubers from 23 different cultivars were collected, and their 2D images were captured under controlled light conditions from top and side angles. The physically measured, volume-based, and image-extracted features were used to employ linear and stepwise regression models to estimate the tuber mass. These models were trained on 14 and tested on 9 different cultivars. In the second step, a random forest classification model was developed to grade the potato tubers based on image-extracted tuber width dimensions from the top, side, and both angles. Classification data was divided into 80% training and 20% test data, where the training process was conducted with 10-fold cross-validation with 5 replications, and the models were evaluated on test data. The tuber mass estimation was higher when combined with the image-extracted features from both angles (R2 = 0.99), followed by an volume based on the image-extracted including all geometric dimensions from both angles (R2 = 0.98) and top angle image-extracted features based stepwise regression (R2 = 0.98). The classification was 100% accurate when trained and tested using top and side widths. Future work is also required to train and test the model for the individual cultivar for higher model precision and robustness for popular potato cultivars. |
| format | Article |
| id | doaj-art-b0896ab3b1474471bfb1172edf3a8b18 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-b0896ab3b1474471bfb1172edf3a8b182025-02-12T05:33:07ZengElsevierSmart Agricultural Technology2772-37552025-03-0110100832Automated potato tuber mass estimation and grading with multiangle 2D imagesAyush K. Sharma0Lincoln Zotarelli1Alina Zare2Lakesh K. Sharma3Soil, Water, and Ecosystem Sciences, IFAS | University of Florida, Gainesville, FL, USAHorticultural Sciences, IFAS | University of Florida, Gainesville, FL, USAElectrical and Computer Engineering, University of Florida, Gainesville, FL, USASoil, Water, and Ecosystem Sciences, IFAS | University of Florida, Gainesville, FL, USA; Corresponding author.Estimating potato tuber mass and size grading with computer vision can help breeders, farmers, and potato processing units reduce manual labor for potato post-harvest handling through optimized technology. The objective of the study was to estimate potato tuber mass and size grades using 2D images. Physical data of potato tubers from 23 different cultivars were collected, and their 2D images were captured under controlled light conditions from top and side angles. The physically measured, volume-based, and image-extracted features were used to employ linear and stepwise regression models to estimate the tuber mass. These models were trained on 14 and tested on 9 different cultivars. In the second step, a random forest classification model was developed to grade the potato tubers based on image-extracted tuber width dimensions from the top, side, and both angles. Classification data was divided into 80% training and 20% test data, where the training process was conducted with 10-fold cross-validation with 5 replications, and the models were evaluated on test data. The tuber mass estimation was higher when combined with the image-extracted features from both angles (R2 = 0.99), followed by an volume based on the image-extracted including all geometric dimensions from both angles (R2 = 0.98) and top angle image-extracted features based stepwise regression (R2 = 0.98). The classification was 100% accurate when trained and tested using top and side widths. Future work is also required to train and test the model for the individual cultivar for higher model precision and robustness for popular potato cultivars.http://www.sciencedirect.com/science/article/pii/S2772375525000656AutomationComputer visionImagingTwo dimensionalWeight estimation |
| spellingShingle | Ayush K. Sharma Lincoln Zotarelli Alina Zare Lakesh K. Sharma Automated potato tuber mass estimation and grading with multiangle 2D images Smart Agricultural Technology Automation Computer vision Imaging Two dimensional Weight estimation |
| title | Automated potato tuber mass estimation and grading with multiangle 2D images |
| title_full | Automated potato tuber mass estimation and grading with multiangle 2D images |
| title_fullStr | Automated potato tuber mass estimation and grading with multiangle 2D images |
| title_full_unstemmed | Automated potato tuber mass estimation and grading with multiangle 2D images |
| title_short | Automated potato tuber mass estimation and grading with multiangle 2D images |
| title_sort | automated potato tuber mass estimation and grading with multiangle 2d images |
| topic | Automation Computer vision Imaging Two dimensional Weight estimation |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525000656 |
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