Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity

Computers and Electronics in Agriculture

Volumn 104, Issue , 2014, Pages 1-8

  Oberti, Roberto ^a   Marchi, Massimo ^a   Tirelli, Paolo ^a   Calcante, Aldo ^a   Iriti, Marcello ^a   Borghese, Alberto N ^a  

^a UNIVERSITY OF MILAN   (Italy)

Author keywords

Disease detection; Grapevine; Multispectral imaging; Powdery mildew; Precision pest management; Proximal sensing

Indexed keywords

AGRICULTURAL CHEMICALS; ALGORITHMS; CALIBRATION; CROPS; SENSORS; TISSUE;

DISEASE DETECTION; GRAPEVINE; MULTISPECTRAL IMAGING; PEST MANAGEMENT; POWDERY MILDEW; PROXIMAL SENSING;

FUNGI;

AGROCHEMICAL; ALGORITHM; AUTOMATION; CANOPY; DISEASE INCIDENCE; FUNGAL DISEASE; IMAGE ANALYSIS; OPTICAL METHOD; SENSITIVITY ANALYSIS; SYMPTOM;

EID: 84897950977     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2014.03.001     Document Type: Article

References (20)

1. Bélanger M.C., Roger J.M., Cartolaro P., Viau A.A., Bellon-Maurel V. Detection of powdery mildew in grapevine using remotely sensed UV-induced fluorescence. Int. J. Remote Sens. 2008, 29:1707-1724.
2. Bishop C.M. Pattern Recognition and Machine Learning 2006, Springer Science+Business Media, New York.
3. Calcante A., Mena A., Mazzetto F. Evaluation of "ground sensing" optical sensors for diagnosis of Plasmopara viticola on vines. Spanish J. Agric. Res. 2012, 10:619-630.
4. Calonnec A., Cartolaro P., Poupot C., Dubourdieu D., Darriet P. Effects of Uncinula necator on the yield and quality of grapes (Vitis vinifera) and wine. Plant Pathol. 2004, 53:434-445.
5. Crisp P., Wicks T.J., Bruer D., Scott E.S. An evaluation of biological and abiotic controls for grapevine powdery mildew. 2. Vineyard trials. Aust. J. Grape Wine Res. 2006, 12:203-211.
6. Crisp P., Wicks T.J., Lorimer M., Scott E.S. An evaluation of biological and abiotic controls for grapevine powdery mildew. 1. Greenhouse studies. Aust. J. Grape Wine Res. 2006, 12:192-202.
7. Devadas R., Lamb D.W., Simpfendorfer S., Backhouse D. Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agric. 2009, 10:459-470.
8. Iriti M., Vitalini S., Di Tommaso G., Di Tommaso D., Faoro F. A new chitosan formulation induces grapevine resistance against powdery mildew and improves grape quality traits. Aust. J. Grape Wine Res. 2011, 17:263-269.
9. Lee W.S., Alchanatis V., Yang C., Hirafuji M., Moshou D., Li C. Sensing technologies for precision specialty crop production. Comput. Electron. Agric. 2010, 74:2-33.
10. Mahlein A.K., Oerke E.C., Steiner U., Dehne H.W. Recent advances in sensing plant diseases for precision crop protection. Eur. J. Plant Pathol. 2012, 133:197-209.
11. Moshou D., Bravo C., Oberti R., West J.S., Ramon H., Vougioukas S., Bochtis D. Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosyst. Eng. 2011, 108:311-321.
12. Naidu R.A., Perry E.M., Pierce F.J., Mekuria T. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Comput. Electron. Agric. 2009, 66:38-45.
13. Oberti R. Optical systems for assessing the healthy status of plants (in Italian). I Georgofili - Atti dell'Accademia dei Georgofili 2003, 50:299-318.
14. Poutaraud A., Latouche G., Martins S., Meyer S., Merdinoglu D., Cerovic Z.G. Fast and local assessment of stilbene content in grapevine leaf by in vivo fluorometry. J. Agric. Food Chem. 2007, 55:4913-4920.
15. Rumpf T., Mahlein A.-K., Steiner U., Oerke E.-C., Dehne H.-W., Plümer L. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput. Electron. Agric. 2010, 74:91-99.
16. Sankaran S., Mishra A., Ehsani R., Davis C. A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 2010, 72:1-13.
17. Stummer B.L., Francis L., Markides A.J., Scott E.S. The effect of powdery mildew infection on grape berries and wine composition and sensory properties of Chardonnay wines. Aust. J. Grape Wine Res. 2003, 9:28-39.
18. West J.S., Bravo C., Oberti R., Lemaire D., Moshou D., McCartney H.A. The potential of optical canopy measurement for targeted control of field crop diseases. Annu. Rev. Phytopathol. 2003, 41:593-614.
19. Yuan L., Zhang J.-C., Wang K., Loraamm R., Huang W.-J., Wang J.-H., Zhao J.-L. Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat. Precision Agric. 2013, 14:495-511.
20. Zhang J.-C., Pu R.-L., Wang J.-H., Huang W.-J., Yuan L., Luo J.-H. Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements. Comput. Electron. Agric. 2012, 85:13-23.