Abstract
Viticulturists need to obtain the estimation of productivity map during the grape vine harvesting, to understand in detail the vineyard variability. An accurate productivity map will support the farmer to take more informed and accurate intervention in the vineyard in line with the precision viticulture concept. This work presents a novel solution to measure the productivity during vineyard harvesting operation realized by a grape harvesting machine. We propose 2D LIDAR sensor attached to low cost IoT module located inside the harvesting machine, to estimate the volume of grapes. Besides, it is proposed data methodology to process data collected and productivity map, considering GIS software, expecting to support the winemakers decisions. A PCD map is also used to validate the method developed by comparison.
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Adamchuk, V.I., Hummel, J.W., Morgan, M.T., Upadhyaya, S.K.: On-the-go soil sensors for precision agriculture. Comput. Electron. Agric. 44(1), 71–91 (2004). https://doi.org/10.1016/j.compag.2004.03.002
Blom, P.E., Tarara, J.M.: Trellis tension monitoring improves yield estimation in vineyards. HortScience 44(3), 678–685 (2009)
Cunha, M., Ribeiro, H., Abreu, I.: Pollen-based predictive modelling of wine production: application to an arid region. Eur. J. Agron. 73, 42–54 (2016). https://doi.org/10.1016/j.eja.2015.10.008
Font, D., Tresanchez, M., Martínez, D., Moreno, J., Clotet, E., Palacín, J.: Vineyard yield estimation based on the analysis of high resolution images obtained with artificial illumination at night. Sensors (Switzerland) 15(4), 8284–8301 (2015). https://doi.org/10.3390/s150408284
Luo, L., Tang, Y., Lu, Q., Chen, X., Zhang, P., Zou, X.: A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard. Comput. Ind. 99, 130–139 (2018). https://doi.org/10.1016/j.compind.2018.03.017
Nuske, S., Wilshusen, K., Achar, S., Yoder, L., Narasimhan, S., Singh, S.: Automated Visual Yield Estimation in Vineyards, vol. 31, pp. 837–860 (2014). https://doi.org/10.1002/rob
Taylor, J.A., Sanchez, L., Sams, B., Haggerty, L., Jakubowski, R., Djafour, S., Bates, T.R.: Evaluation of a commercial grape yield monitor for use mid-season and at-harvest. J. Int. des Sciences de la Vigne et du Vin 50(2), 57–63 (2016)
Acknowledgments
This work is funded by funds through the FCT - Fundação para a Ciência e a Tecnologia, I.P., within the framework of the project “WaterJPI/0012/2016”. The authors would like to thank the EU and FCT for funding in the frame of the collaborative international consortium Water4Ever financed under the ERA-NET Water Works 2015 cofounded call. This ERA-NET is an integral part of the 2016 Joint Activities developed by the Water Challenge for a changing world joint programme initiation (Water JPI). The authors also thank to Aveleda S.A. for making available the harvest machinery for installation of AgIoT Lidar Sensor and allowing the collection of harvesting data.
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Moura, P., Ribeiro, D., dos Santos, F.N., Gomes, A., Baptista, R., Cunha, M. (2019). Estimation of Vineyard Productivity Map Considering a Cost-Effective LIDAR-Based Sensor. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_11
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DOI: https://doi.org/10.1007/978-3-030-30241-2_11
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