Estimation of red tip disease severity in pineapple using a non-contact sensor approach

Siva Kumar Balasundram, Faizah Abu Kassim, Ganesan Vadamalai, Ahmad Husni Mohd Hanif

Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Malaysia.
Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Malaysia.
Department of Plant Protection, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Malaysia.

DOI: 10.4236/as.2013.44029                

Abstract

Red tip disease on pineapple (Ananas comosus) was first recognized about 20 years ago in a commercial pineapple stand located in Simpang Renggam, Johor, Peninsular Malaysia. Since its discovery, there has been no confirmation on the causal agent of red tip disease. The epidemiology of red tip disease is still not fully understood. However, based on disease symptoms and field transmission mode, red tip disease seems to be strongly associated with viral infection. The aim of this work was to assess the feasibility of using an optical sensor to estimate red tip disease severity. This work was performed in a commercial pineapple plantation located in Simpang Renggam, Johor. Four observation plots bearing pineapple variety SR36 were demarcated based on crop growth stage. Each plot comprised a total of eighty corresponding measurements of percent Disease Severity (% DS) and Normalized Difference Vegetation Index (NDVI). Our data showed a strong correlation between % DS and NDVI. The 7- and 11-month plantings registered a correlation coefficient (r) of -0.83 and -0.88, respectively. The negative correlation infers that NDVI increases when disease severity is low. This is expected since healthy leaves reflect more near-infrared light and less visible light which results in a higher NDVI. The regression of NDVI on % DS for the 7-month planting was explained by: % DS = 181.6 - 185.6*NDVI. Meanwhile, the regression of NDVI on % DS for the 11-month planting was explained by: % DS = 213.2 - 219.8*NDVI. The linear fit between measured % DS and estimated % DS from the 7-month and 11-month plantings was relatively strong. This work has demonstrated that NDVI is a reliable predictor of % DS in pineapple.

Keywords

Pineapple; Red Tip Disease; Optical Sensor

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Abou-Ismail, O., Huang, J.F. and Wang, R.C. (2004) Rice yield estimation by integrating remote sensing and rice growth simulation model. Pedosphere, 14, 519-526.
[2]	Zhang, M. and Qin, Z. (2004) Spectral analysis of tomato late blight infection for remote sensing of tomato disease stress in California. IEEE 2004 International Geosciences and Remote Sensing Symposium, Anchorage, 4091-4094.
[3]	Muhammed, H.H. (2005) Hyperspectral crop reflectance data for characterizing and estimating fungal disease severity in wheat. Biosystems Engineering, 91, 9-20. doi:10.1016/j.biosystemseng.2005.02.007
[4]	Lu, Y.L., Bai, Y.L., Yang, L.P. and Wang, L. (2008) Diagnosis on nitrogen status using greenseeker in spring maize. Journal of Maize Sciences, 16, 111-114.
[5]	Shaver, T.M., Khosla, R. and Westfall, D.G. (2011) Evaluation of two crop canopy sensors for nitrogen variability determination in irrigated maize. Precision Agriculture, 12, 892-904. doi:10.1007/s11119-011-9229-2
[6]	Solari, F., Shanahan, J., Ferguson, R., Schepers, J. and Gitelson. A. (2008) Active sensor reflectance measurements of corn nitrogen status and yield potential. Agronomy Journal, 100, 571-579. doi:10.2134/agronj2007.0244
[7]	Singh, I., Srivastava, A.K., Chandna, P. and Gupta, R.K. (2006) Crop sensors for efficient nitrogen management in sugarcane: Potential and constraints. Sugar Technology, 8, 299-302. doi:10.1007/BF02943572
[8]	Inman, D., Khosla, R. and Mayfield, T. (2005) On-the-go active remote sensing for efficient crop nitrogen management. Sensor Review, 25, 209-214. doi:10.1108/02602280510606499
[9]	Mazzetto, F., Calcante, A. and Mena, A. (2009) Comparing commercial optical sensors for crop monitoring tasks in precision viticulture. Journal of Agricultural Engineering, 40, 11-18.
[10]	Chappelle, E.W., Wood, F.M., McMurtrey, J.E. and Newcomb, W.W. (1984) Laser-induced fluorescence of green plants: 1. A technique for the remote detection of plant stress and species differentiation. Applied Optics, 23, 134-138. doi:10.1364/AO.23.000134