Integration of optical and analogue sensors for monitoring canopy health and vigour in precision viticulture

Precision Agriculture

Volumn 11, Issue 6, 2010, Pages 636-649

  Mazzetto, F ^a   Calcante, A ^b   Mena, A ^b   Vercesi, A ^b  

^a FREE UNIVERSITY OF BOZEN BOLZANO   (Italy)

^b UNIVERSITY OF MILAN   (Italy)

Author keywords

Canopy thickness; Ground sensing; NDVI; Vegetative vigour

Indexed keywords

AGROCHEMICAL; COMPARATIVE STUDY; FUNGAL DISEASE; NDVI; REAL TIME; REMOTE SENSING; SATELLITE DATA; ULTRASONICS; VIGOR; VINE; VINEYARD; VITICULTURE;

PLASMOPARA VITICOLA;

EID: 78249271986     PISSN: 13852256     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11119-010-9186-1     Document Type: Article

References (32)

1. Bausch, W. C., & Delgado, J. A. (2003). Ground-based sensing of plant nitrogen status in irrigated corn to improve nitrogen management. In T. VanToai et al. (Eds.), Digital imaging and spectral techniques: Application to Precision Agriculture and Crop Physiology (pp. 145-157). ASA Special Publication 66. Madison, WI, USA: ASA, CSSA, SSSA.
2. Calcante, A., Mazzetto, F., Oberti, R., & Brancadoro, L. (2006). Ultrasonic canopy sensing for precision viticulture practice. In VDI Verlag GmbH (Ed.), Proceedings of the XVI CIGR World Congress (CD). Düsseldorf (Germany): University of Bonn.
3. Dobrowski, S. Z., Ustin, S. L., & Wolpert, J. A. (2003). Grapevine dormant pruning weight prediction using remotely sensed data. Australian Journal of Grape and Wine Research, 9, 177-182.
4. Doruchowski, G., Balsari, P., & van de Zande, J. (2009). Development of a crop adapted spray application system for sustainable plant protection in fruit growing. In Proceedings of the Symposium on the Applications of Precision Agriculture for Fruits and Vegetables International Conference. Acta Horticulturae ISHS 824, 251-260.
5. Drissi, R., Goutouly, J. P., Forget, D., & Gaudillère, J. P. (2009). Nondestructive measurement of grapevine leaf area by ground Normalized Difference Vegetation Index. Agronomy Journal, 101(1), 226-231.
6. Gil, E., Escolà, A., Rosell, J. R., Planas, S., & Val, L. (2007). Variable rate application of plant protection products in vineyard using ultrasonic sensors. Crop Protection, 26, 1287-1297.
7. Goutouly, J. P., Drissi, R., Forget, D., & Gaudillere, J. P. (2006). Characterization of vine vigour by ground based NDVI measurements. In Proceedings of VI International Terroir Congress, ENITA, Bordeaux (France), pp. 237-242.
8. Holland, K. H., Schepers, J. S., & Shanahan, J. F. (2006). Configurable multi-spectral active sensor for highspeed plant canopy assessment. In D. J. Mulla (Ed.), Proceedings of the 8th International Conference on Precision Agriculture (CD). Minneapolis, MN, USA: University of Minnesota.
9. Inamasu, R. Y., Tangerino, G. T., Porto, A. J. V., Luchiari, A., Schepers, J. S., Shanahan, J. F., et al. (2008). Using active sensor and ultrasound sensor readings to assess plant N status. In Proceedings of the 9th International Conference on Precision Agriculture. Denver, CO, USA, paper no 339.
10. Inman, D., Khosla, R., Reich, R. M., & Westfall, D. G. (2007). Active remote sensing and grain yield in irrigated maize. Precision Agriculture, 8, 241-252.
11. Johnson, L. F. (2003). Temporal stability of an NDVI-LAI relationship in a Napa Valley vineyard. Australian Journal of Grape and Wine Research, 9, 96-101.
12. Johnson, L., Roczen, D., Youkhana, S., Neemani, R. R., & Bosch, D. F. (2003). Mapping vineyard leaf area with multispectral satellite imagery. Computers and Electronics in Agriculture, 38, 33-44.
13. Jones, C. L., Maness, N. O., Stone, M. L., & Jayasekara, R. (2004). Chlorophyll estimation using multi-spectral reflectance and height sensing. ASAE paper no. 04-3081, ASAE/CSAE Annual International Meeting, Ottawa (Canada).
14. Jones, C. L., Maness, N. O., Stone, M. L., & Jayasekara, R. (2007). Chlorophyll estimation using multispectral reflectance and height sensing. Transactions of the ASABE, 50(5), 1867-1872.
15. Lamb, D. W., Hall, A., & Louis, J. (2001). Airborne remote sensing of vines for canopy variability and productivity. Australian Grapegrower and Winemaker, 449a, 89-92.
16. Lan, Y., Zhang. H., Lacey, R., Hoffmann, W. C., & Wu, W. (2009). Development of an integrated sensor and instrumentation system for measuring crop conditions. Agricultural Engineering International: The CGIR Ejournal. Manuscript IT 08 1115, vol. 11.
17. Mazzetto, F. (2006). La centralitá dei sistemi informativi aziendali tra management informatizzato e agricoltura di precisione (The centrality of farm information systems between computerized management and precision agriculture). In C. R. A. S. T. (Ed.), Proceedings of CITIMAP Workshop (CD). Piacenza, Italy: Universitá Cattolica del Sacro Cuore.
18. Mazzetto, F., Calcante, A., & Mena, A. (2009). Comparing commercial optical sensors for crop monitoring tasks in precision viticulture. Journal of Agricultural Engineering, 1, 11-18.
19. Meier, U. (2001). Growth stages of mono-and dicotyledonous plants. BBCH Monograph. 2. Edition. Federal Biological Research Centre for Agriculture and Forestry, Blackwell Wissenschaft, Berlin (Germany), pp. 91-93.
20. Milics, G., Burai, P., Lénárt, C., Tamás, J., Papp, Z., Deákvári, J., et al. (2008). Comparison of multispectral and hyperspectral vegetation indices for prediction of yield and grain quality of spring barley in Hungary. In G. Papadakis (Ed.), Proceedings of Ageng 2008, International Conference on Agricultural Engineering (CD). Athens, GR: University of Athens, paper no 1177698.
21. Moshou, D., Bravo, C., Wahlen, S., West, J., McCartney, A., De Baerdemaeker, J., et al. (2006). Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self-organising maps. Precision Agriculture, 7, 149-164.
22. Rosell Polo, J. R., Sanz, R., Llorens, J., Arnó, J., Escolà, A., Ribes-Dasi, M., et al. (2009). A tractor-mounted scanning LIDAR for non-destructive measurements of vegetative volume and surface area tree-row plantations: A comparison with conventional destructive measurements. Biosystems Engineering, 102(2), 128-134.
23. Schumann, A. W., & Zaman, Q. U. (2005). Software development for real-time ultrasonic mapping of tree canopy size. Computers and Electronics in Agriculture, 47, 25-40.
24. Scotford, I. M., & Miller, P. C. H. (2004). Combination of spectral reflectance and ultrasonic sensing to monitor the growth of winter wheat. Biosystems Engineering, 87(1), 27-38.
25. Shearer, S. A., Thomasson, J. A., & McNeill, S. G. (1996). Filter selection for NIR sensing of plant and soil materials. Transaction of the ASAE, 39(3), 1209-1214.
26. Stamatiadis, S., Taskos, D., Tsadilas, C., Christofides, C., Tsadila, E., & Schepers, J. S. (2006). Relation of ground-sensor canopy reflectance to biomass production and grape color in two Merlot vineyards. American Journal of Enology and Viticulture, 57(4), 415-422.
27. Stamatiadis, S., Taskos, D., Tsadilas, C., Christofides, C., Tsadila, E., & Schepers, J. S. (2009). Comparison of passive and active sensors for the estimation of vine biomass production. Precision Agriculture. doi: 10. 1007/s11119-009-9131-3.
28. Sui, R., & Thomasson, J. A. (2006). Ground-based sensing system for cotton nitrogen status determination. Transactions of the ASABE, 49(6), 1983-1991.
29. Townsend, G. R., & Heuberger, I. W. (1943). Methods for estimating losses caused by disease in fungicide experiments. Plant Disease Reporter, 27(17), 340-343.
30. Zaman, Q. U., & Schumann, A. W. (2005). Performance of an ultrasonic tree volume measurement system in commercial citrus groves. Precision Agriculture, 6, 467-480.
31. Zaman, Q. U., Schumann, A. W., & Miller, W. M. (2005). Variable rate nitrogen applications in Florida citrus based on ultrasonically-sensed tree size. Applied Engineering in Agriculture, 21(3), 331-335.
32. Zaman, Q. U., Schumann, A. W., & Hostler, H. K. (2006). Estimation of citrus fruit yield using ultrasonically-sensed tree size. Applied Engineering in Agriculture, 22(1), 39-44.