Abstract
In order to determine the physiological state of a plant it is necessary to monitor it throughout the developmental period. One of the main parameters to monitor is the Leaf Area Index (LAI). The objective of this work was the development of a non-destructive methodology for the LAI estimation in wine growing. This method is based on stereo images that allow to obtain a bard 3D representation, in order to facilitate the segmentation process, since to perform this process only based on color component becomes practically impossible due to the high complexity of the application environment. In addition, the Normalized Difference Vegetation Index will be used to distinguish the regions of the trunks and leaves. As an low-cost and non-evasive method, it becomes a promising solution for LAI estimation in order to monitor the productivity changes and the impacts of climatic conditions in the vines growth.
Keywords
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Arnó, J., et al.: Leaf area index estimation in vineyards using a ground-based lidar scanner. Precision Agric. 14(3), 290–306 (2013)
Confalonieri, R., Francone, C., Foi, M.: The PocketLAI smartphone app: an alternative method for leaf area index estimation. In: Proceedings of the 7th International Congress on Environmental Modelling and Software, San Diego, CA, USA (2014)
De Bei, R., et al.: VitiCanopy: a free computer app to estimate canopy vigor and porosity for grapevine. Sensors 16(4), 585 (2016)
Dobrowski, S., Ustin, S., Wolpert, J.: Remote estimation of vine canopy density in vertically shoot-positioned vineyards: determining optimal vegetation indices. Aust. J. Grape Wine Res. 8(2), 117–125 (2002)
Easlon, H.M., Bloom, A.J.: Easy leaf area: automated digital image analysis for rapid and accurate measurement of leaf area. Appl. Plant Sci. 2(7), 1400033 (2014)
Rituerto, A., Puig, L., Guerrero, J.J.: Comparison of omnidirectional and conventional monocular systems for visual SLAM. In: The 10th Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras, OMNIVIS 2010. Zaragoza, Spain (2010)
Kalisperakis, I., Stentoumis, C., Grammatikopoulos, L., Karantzalos, K.: Leaf area index estimation in vineyards from uav hyperspectral data, 2D image mosaics and 3D canopy surface models. Int. Arch. Photogram. Remote Sensing Spatial Inf. Sci. 40(1), 299 (2015)
Kliewer, W.M., Dokoozlian, N.K.: Leaf area/crop weight ratios of grapevines: influence on fruit composition and wine quality. Am. J. Enol. Viticulture 56(2), 170–181 (2005)
Liu, J., Chen, J., Cihlar, J., Park, W.: A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sens. Environ. 62(2), 158–175 (1997)
Llorens, J., Gil, E., Llop, J., et al.: Ultrasonic and lidar sensors for electronic canopy characterization in vineyards: advances to improve pesticide application methods. Sensors 11(2), 2177–2194 (2011)
de Miguel, P.S., et al.: Estimation of vineyard leaf area by linear regression. Span. J. Agric. Res. 9(1), 202–212 (2011)
del Moral-Martínez, I., et al.: Mapping vineyard leaf area using mobile terrestrial laser scanners: should rows be scanned on-the-go or discontinuously sampled? Sensors 16, 119 (2016)
Oliveira, M., Santos, M.: A semi-empirical method to estimate canopy leaf area of vineyards. American journal of enology and viticulture 46(3), 389–391 (1995)
Orlando, F., et al.: Estimating leaf area index (LAI) in vineyards using the PocketLAI smart-app. Sensors 16(12), 2004 (2016)
Patakas, A., Noitsakis, B.: An indirect method of estimating leaf area index in cordon trained spur pruned grapevines. Scientia Horticulturae 80, 299–305 (1999)
Patrignani, A., Ochsner, T.E.: Canopeo: a powerful new tool for measuring fractional green canopy cover. Agron. J. 107(6), 2312–2320 (2015)
Raajan, N., Ramkumar, M., Monisha, B., Jaiseeli, C., et al.: Disparity estimation from stereo images. Procedia Eng. 38, 462–472 (2012)
Sanz, R., et al.: Lidar and non-lidar-based canopy parameters to estimate the leaf area in fruit trees and vineyard. Agric. Forest Meteorol. 260, 229–239 (2018)
Siegfried, W., Viret, O., Huber, B., Wohlhauser, R.: Dosage of plant protection products adapted to leaf area index in viticulture. Crop Prot. 26(2), 73 (2007)
Smart, R.E.: Principles of grapevine canopy microclimate manipulation with implications for yield and quality: a review. Am. J. Enol. Viticulture 36(3), 230–239 (1985)
Taipale, E.: NDVI and Your Farm: Understanding NDVI for Plant Health Insights. Sentera Precision Agriculture (2017). https://sentera.com/understanding-ndvi-plant-health/. Accessed March 2019
Welles, J.M.: Some indirect methods of estimating canopy structure. Remote Sensing Rev. 5(1), 31–43 (1990)
Zheng, G., Moskal, L.M.: Retrieving leaf area index (LAI) using remote sensing: theories, methods and sensors. Sensors 9(4), 2719–2745 (2009)
Zhu, X., et al.: Improving leaf area index (LAI) estimation by correcting for clumping and woody effects using terrestrial laser scanning. Agric. Forest Meteorol. 263, 276–286 (2018)
Acknowledgements
This work was funded by FCT (Portuguese Foundation for Science and Technology), 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). This work was developed under the Doctoral fellowship with the reference “SFRH/BD/129813/2017”, from FCT.
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Mendes, J.M., Filipe, V.M., dos Santos, F.N., Morais dos Santos, R. (2019). A Low-Cost System to Estimate Leaf Area Index Combining Stereo Images and Normalized Difference Vegetation Index. 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_21
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