Abstract
Fruit logistics during harvesting involves a large-scale deployment of material and human resources. In olive groves and, for small producers, the operation can be carried out in different ways, however, none of these allow a production monitoring or traceability of the harvested fruit. This study presents a compatible methodology with the usual harvesting logistics employed for harvest monitoring. The procedure uses a mechanical system for loading and unloading big bags of fruit weighing approximately 200 kg with a loading arm that can be adapted to a conventional trailer. An electronic system connected to a cloud application is installed on the trailer for geo-referenced recording of the yield. Tests to determine the accuracy of the Global Navigation Satellite System (GNSS) reported values of around 19 mm and 590 mm for the system with and without corrections, respectively, using the Networked Transport of RTCM via Internet Protocol (NTRIP). The error determination tests of the loading bolt weighing system showed high accuracy and linearity with a mean absolute error of 1.1 ± 0.99 kg. The complete system was tested in a traditional olive grove and an intensive olive grove. The harvest maps generated allowed the yield visualisation and to keep a traceability record of the harvested fruit batches. Application of the proposed methodology and systems presents a reduced operation time between loading and unloading of consecutive fruit batches (~ 2.5 min). The proposed system would be useful for small producers with limited resources who need to control production and fruit traceability on their farm.
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Acknowledgements
The authors are grateful for the funding received by the Consejería de Conocimiento, Investigación y Universidad (Junta de Andalucia) under the PYC20 RE 024 UCO Project 'Development of an IoT application to monitor the harvest performed by different mechanization systems in traditional olive harvesting for the improvement of its management and traceability'. We would also like to thank Juan Pérez-Moya for the support given in the design of the cloud application.
Funding
Consejería de Economía,Conocimiento,Empresas y Universidad,Junta de Andalucía, PYC20 RE 024 UCO, Sergio Bayano-Tejero
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Bayano-Tejero, S., Márquez-García, F., Sarri, D. et al. Olive yield monitor for small farms based on an instrumented trailer to collect big bags from the ground. Precision Agric 25, 412–429 (2024). https://doi.org/10.1007/s11119-023-10078-w
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DOI: https://doi.org/10.1007/s11119-023-10078-w