Evaluation of Image Analysis to Determine the N-Fertilizer Demand of Broccoli Plants (Brassica oleracea convar. botrytis var. italica). (16th June 2008)

• Record Type: Journal Article
• Authors: Graeff, Simone; Pfenning, Judit; Claupein, Wilhelm; Liebig, Hans-Peter
• Other Names: Fu J. Academic Editor.
• Is Part Of: Advances in optical technologies. Volume 2008(2008)
• Publication Date: 2008-06-16
• Subjects: Optoelectronics -- Periodicals; Optoelectronics; Physics; Periodicals; Electronic journals; 621.36
• DOI: 10.1155/2008/359760
• Languages: English
• ISSNs: 1687-6393

Abstract

Abstract : Numerous models have been developed for calculating optimum decision rules for nitrogen fertilization based on remote sensing techniques. New technologies related to digital image analysis may provide an alternative method to estimate nutrient status faster and more efficiently than current techniques. A series of field studies was conducted to determine the applicability of digital image analysis for nitrogen demand estimates in broccoli plants. Digital images were taken under constant light conditions in various wavelength ranges (380–1300 nm) using a digital imager. Images were processed for the parametersa ∗ andb ∗ in theL ∗ a ∗ b ∗ color system. The image analysis showed a close correlation between the nitrogen status of broccoli plants and the parameterb ∗ of theL ∗ a ∗ b ∗ color system especially in the wavelength ranges510 780 and516 780 nm. The relationship between nutrient concentration in leaf dry matter and the parametersb ∗ was used to determine the N fertilizer demand within the cultivation period. Estimated N amounts were applied as top dressing four weeks after setting and were 100 kgha − 1 lower than the standard fertilizer rate. Calculated N balances indicated a total uptake of applied N amounts in the image-based N treatments without considerable yield loss. Thus, digital image analysis proved to be an effective means of determining nitrogen status and adjusting fertilizer applications to preserve or enhance crop quality and yield considering sustainability.