Data-Driven Worker Activity Recognition and Efficiency Estimation in Manual Fruit Harvesting
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910021269323776 |
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| author | Bhattarai, Uddhav Arikapudi, Rajkishan Fennimore, Steven A. Martin, Frank N Vougioukas, Stavros G. |
| author_facet | Bhattarai, Uddhav Arikapudi, Rajkishan Fennimore, Steven A. Martin, Frank N Vougioukas, Stavros G. |
| contents | Manual fruit harvesting is common in agriculture, but the amount of time pickers spend on non-productive activities can make it very inefficient. Accurately identifying picking vs. non-picking activity is crucial for estimating picker efficiency and optimising labour management and harvest processes. In this study, a practical system was developed to calculate the efficiency of pickers in commercial strawberry harvesting. Instrumented picking carts (iCarritos) were developed to record the harvested fruit weight, geolocation, and iCarrito movement in real time. The iCarritos were deployed during the commercial strawberry harvest season in Santa Maria, CA. The collected data was then used to train a CNN-LSTM-based deep neural network to classify a picker's activity into "Pick" and "NoPick" classes. Experimental evaluations showed that the CNN-LSTM model showed promising activity recognition performance with an F1 score of 0.97. The recognition results were then used to compute picker efficiency and the time required to fill a tray. Analysis of the season-long harvest data showed that the average picker efficiency was 75.07% with an estimation accuracy of 97.23%. Furthermore, the average tray fill time was 6.85 minutes with an estimation accuracy of 96.78%. When integrated into commercial harvesting, the proposed technology can aid growers in monitoring automated worker activity and optimising harvests to reduce non-productive time and enhance overall harvest efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22809 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Worker Activity Recognition and Efficiency Estimation in Manual Fruit Harvesting Bhattarai, Uddhav Arikapudi, Rajkishan Fennimore, Steven A. Martin, Frank N Vougioukas, Stavros G. Machine Learning Artificial Intelligence Manual fruit harvesting is common in agriculture, but the amount of time pickers spend on non-productive activities can make it very inefficient. Accurately identifying picking vs. non-picking activity is crucial for estimating picker efficiency and optimising labour management and harvest processes. In this study, a practical system was developed to calculate the efficiency of pickers in commercial strawberry harvesting. Instrumented picking carts (iCarritos) were developed to record the harvested fruit weight, geolocation, and iCarrito movement in real time. The iCarritos were deployed during the commercial strawberry harvest season in Santa Maria, CA. The collected data was then used to train a CNN-LSTM-based deep neural network to classify a picker's activity into "Pick" and "NoPick" classes. Experimental evaluations showed that the CNN-LSTM model showed promising activity recognition performance with an F1 score of 0.97. The recognition results were then used to compute picker efficiency and the time required to fill a tray. Analysis of the season-long harvest data showed that the average picker efficiency was 75.07% with an estimation accuracy of 97.23%. Furthermore, the average tray fill time was 6.85 minutes with an estimation accuracy of 96.78%. When integrated into commercial harvesting, the proposed technology can aid growers in monitoring automated worker activity and optimising harvests to reduce non-productive time and enhance overall harvest efficiency. |
| title | Data-Driven Worker Activity Recognition and Efficiency Estimation in Manual Fruit Harvesting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.22809 |