Prediction of Harvest Time of Tomato Using Mask R-CNN
In recent years, the agricultural field has been confronting difficulties such as the aging of farmers, a shortage of workers, and difficulties for new farmers. Harvesting time prediction has the potential to solve these problems, and is expected to effectively utilize human resources, save labor, a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | AgriEngineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-7402/4/2/24 |
| _version_ | 1797490918246318080 |
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| author | Daichi Minagawa Jeyeon Kim |
| author_facet | Daichi Minagawa Jeyeon Kim |
| author_sort | Daichi Minagawa |
| collection | DOAJ |
| description | In recent years, the agricultural field has been confronting difficulties such as the aging of farmers, a shortage of workers, and difficulties for new farmers. Harvesting time prediction has the potential to solve these problems, and is expected to effectively utilize human resources, save labor, and reduce labor costs. To achieve harvesting time prediction, various works are being actively conducted. Methods for harvesting time prediction using meteorological information such as temperature and solar radiation, etc., and methods for harvesting time prediction using neural networks based on color information from fruit bunch images are being investigated. However, the prediction accuracy is still insufficient, and the harvesting time prediction for individual tomato fruits has not been studied. In this study, we propose a novel method to predict the harvesting time for individual tomato fruits. The method uses Mask R-CNN to detect tomato bunches and uses two types of ripeness determination to predict the harvesting time of individual tomato fruits. The experimental results showed that the accuracy of the prediction using the ratio of <i>R</i> values was better for the harvesting time prediction of tomatoes that are close to the harvesting time, and the accuracy of the prediction using the average of the differences between <i>R</i> and <i>G</i> in RGB values was better for the harvesting time prediction of tomatoes that are far from the harvesting time. These results show the effectiveness of the proposed method. |
| first_indexed | 2024-03-10T00:39:55Z |
| format | Article |
| id | doaj.art-0f3218bc0c7d4114a3cef001ab7dfa15 |
| institution | Directory Open Access Journal |
| issn | 2624-7402 |
| language | English |
| last_indexed | 2024-03-10T00:39:55Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj.art-0f3218bc0c7d4114a3cef001ab7dfa152023-11-23T15:08:25ZengMDPI AGAgriEngineering2624-74022022-03-014235636610.3390/agriengineering4020024Prediction of Harvest Time of Tomato Using Mask R-CNNDaichi Minagawa0Jeyeon Kim1Advanced Engineering Course at National Institute of Technology, Tsuruoka College, Tsuruoka, Yamagata 997-8511, JapanDepartment of Creative Engineering at National Institute of Technology, Tsuruoka College, Tsuruoka, Yamagata 997-8511, JapanIn recent years, the agricultural field has been confronting difficulties such as the aging of farmers, a shortage of workers, and difficulties for new farmers. Harvesting time prediction has the potential to solve these problems, and is expected to effectively utilize human resources, save labor, and reduce labor costs. To achieve harvesting time prediction, various works are being actively conducted. Methods for harvesting time prediction using meteorological information such as temperature and solar radiation, etc., and methods for harvesting time prediction using neural networks based on color information from fruit bunch images are being investigated. However, the prediction accuracy is still insufficient, and the harvesting time prediction for individual tomato fruits has not been studied. In this study, we propose a novel method to predict the harvesting time for individual tomato fruits. The method uses Mask R-CNN to detect tomato bunches and uses two types of ripeness determination to predict the harvesting time of individual tomato fruits. The experimental results showed that the accuracy of the prediction using the ratio of <i>R</i> values was better for the harvesting time prediction of tomatoes that are close to the harvesting time, and the accuracy of the prediction using the average of the differences between <i>R</i> and <i>G</i> in RGB values was better for the harvesting time prediction of tomatoes that are far from the harvesting time. These results show the effectiveness of the proposed method.https://www.mdpi.com/2624-7402/4/2/24Mask R-CNNharvesting time predictiondeep learning |
| spellingShingle | Daichi Minagawa Jeyeon Kim Prediction of Harvest Time of Tomato Using Mask R-CNN AgriEngineering Mask R-CNN harvesting time prediction deep learning |
| title | Prediction of Harvest Time of Tomato Using Mask R-CNN |
| title_full | Prediction of Harvest Time of Tomato Using Mask R-CNN |
| title_fullStr | Prediction of Harvest Time of Tomato Using Mask R-CNN |
| title_full_unstemmed | Prediction of Harvest Time of Tomato Using Mask R-CNN |
| title_short | Prediction of Harvest Time of Tomato Using Mask R-CNN |
| title_sort | prediction of harvest time of tomato using mask r cnn |
| topic | Mask R-CNN harvesting time prediction deep learning |
| url | https://www.mdpi.com/2624-7402/4/2/24 |
| work_keys_str_mv | AT daichiminagawa predictionofharvesttimeoftomatousingmaskrcnn AT jeyeonkim predictionofharvesttimeoftomatousingmaskrcnn |