Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation Images
The scale of horticultural facilities in Japan is expanding, making the efficient management of labor costs essential, particularly in large-scale tomato production. This study developed a consistent and practical system for predicting harvest working time and estimating the quantity and weight of h...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/14/12/2257 |
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| author | Hiroki Naito Tomohiko Ota Kota Shimomoto Fumiki Hosoi Tokihiro Fukatsu |
| author_facet | Hiroki Naito Tomohiko Ota Kota Shimomoto Fumiki Hosoi Tokihiro Fukatsu |
| author_sort | Hiroki Naito |
| collection | DOAJ |
| description | The scale of horticultural facilities in Japan is expanding, making the efficient management of labor costs essential, particularly in large-scale tomato production. This study developed a consistent and practical system for predicting harvest working time and estimating the quantity and weight of harvested fruit using panoramic images of cultivation rows. The system integrates a deep learning model, the Mask ResNet-50 convolutional neural network, to count harvestable fruits from images and a predictive algorithm to estimate working time based on the fruit count. The results indicated that the average for all workers could be predicted with an error margin of 30.1% when predicted three days before the harvest date and 15.6% when predicted on the harvest date. The trial also revealed that the accuracy of the predictions varied based on workers’ experience and cultivation methods. This study highlights the system’s potential to optimize harvesting plans and labor allocation, providing a novel tool for reducing labor costs while maintaining efficiency in large-scale tomato greenhouse production. |
| format | Article |
| id | doaj-art-8a93735c909644eca1b8ec9d9487d38e |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-8a93735c909644eca1b8ec9d9487d38e2024-12-27T14:03:10ZengMDPI AGAgriculture2077-04722024-12-011412225710.3390/agriculture14122257Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation ImagesHiroki Naito0Tomohiko Ota1Kota Shimomoto2Fumiki Hosoi3Tokihiro Fukatsu4Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 1138657, JapanResearch Center for Agricultural Robotics, National Agriculture and Food Research Organization, Tsukuba 3050856, JapanInstitute of Agricultural Machinery, National Agriculture and Food Research Organization, Tsukuba 3050856, JapanGraduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 1138657, JapanInstitute of Agricultural Machinery, National Agriculture and Food Research Organization, Tsukuba 3050856, JapanThe scale of horticultural facilities in Japan is expanding, making the efficient management of labor costs essential, particularly in large-scale tomato production. This study developed a consistent and practical system for predicting harvest working time and estimating the quantity and weight of harvested fruit using panoramic images of cultivation rows. The system integrates a deep learning model, the Mask ResNet-50 convolutional neural network, to count harvestable fruits from images and a predictive algorithm to estimate working time based on the fruit count. The results indicated that the average for all workers could be predicted with an error margin of 30.1% when predicted three days before the harvest date and 15.6% when predicted on the harvest date. The trial also revealed that the accuracy of the predictions varied based on workers’ experience and cultivation methods. This study highlights the system’s potential to optimize harvesting plans and labor allocation, providing a novel tool for reducing labor costs while maintaining efficiency in large-scale tomato greenhouse production.https://www.mdpi.com/2077-0472/14/12/2257tomatopredictionharvest working timedeep learningMask R-CNN |
| spellingShingle | Hiroki Naito Tomohiko Ota Kota Shimomoto Fumiki Hosoi Tokihiro Fukatsu Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation Images Agriculture tomato prediction harvest working time deep learning Mask R-CNN |
| title | Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation Images |
| title_full | Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation Images |
| title_fullStr | Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation Images |
| title_full_unstemmed | Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation Images |
| title_short | Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation Images |
| title_sort | accuracy assessment of tomato harvest working time predictions from panoramic cultivation images |
| topic | tomato prediction harvest working time deep learning Mask R-CNN |
| url | https://www.mdpi.com/2077-0472/14/12/2257 |
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