Using UV-C radiation and image analysis for fungus control in tomato plants
M. A. Valencia, L. F. Patiño, J. A. Herrera-Ramírez, D. Castañeda, J. A. Gómez, J. C. Quijano
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Base Information
Volume
V50 - N4 / 2017 Ordinario
Reference
369-378
DOI
http://doi.org/10.7149/OPA.50.4.49073
Language
English
Keywords
Powdery mildew; Image analysis; Physical management of disease.
Abstract
The plant pathogenic fungi are the greatest threat to agriculture in the world; to counteract its effect synthetic chemical fungicides are used, which cause adverse effects on human health and the environment. Physical methods to control pathogens are emerging as an alternative for managing crop diseases; however, they have been little explored. Tomato (Solanum lycopersicum) is the most frequently cultivated vegetable in the world and it grows in a wide range of climates, both field and greenhouse. One of the most limiting factors for tomato production is the foliar disease known as powdery mildew. The yield and quality of the fruit can be reduced because the disease can develop rapidly and severely affected leaflets die. In this paper, the effect of UV-C (254 nm) on the development of powdery mildew under greenhouse conditions was evaluated. The leaflets were submitted to different UV-C exposure times and with the acquisition of RGB images and image analysis the growth rate of the lesion was determined. The exposure times that did not cause visible injuries in healthy leaflets were used to measure the effect on leaflets with the disease. After natural infection, the lesion area, the number of total lesions and the growth rate with respect to the initial lesion area were determined. Comparing with non-treated leaflets, 30 s or 60 s applications of UV-C each 48 h for 10 days, significantly reduced powdery mildew severity on tomato leaflets. We also compared traditional growth rate determination using a caliper with a custom program based on image acquisition and analysis, reporting that the latest is a more complete system to follow accurately the disease evolution. This work is an important approach of applying light to agricultural problems noninvasively in preharvest conditions, which has been poorly explored in agriculture, and it demonstrates the application of image analysis in the study of important agricultural variables.
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