Image-based leaf analysis for detection of nitrogen status in sweet corn production

Ab Halim, Aimi Amira (2019) Image-based leaf analysis for detection of nitrogen status in sweet corn production. [Project Paper] (Submitted)

Text lp FK 2019 76.pdf Download (2MB)

Abstract

Accurate timing and rate of nitrogen (N) fertilizer application plays a significant part in the yield and quality of crops. For the effective use of nitrogen fertilizers, prediction of N demands is essential. Nitrogen is one of the most limiting factors for sweet corn production. Over-fertilization of nitrogen to the crop can decrease crop yield. The Dark Green Colour Index (DGCI) is a quantitative measure of greenness which is closely related to leaf N concentration. So, in this study, a field experiment was carried out to explore the feasibility of image processing technique in sweet corn nitrogen non-destructive diagnosis using the DGCI value from the image processing. Based on the image processing technology of visible light, this study investigated the relationship between the nutrients status and the greenness of sweet corn leaves, which are captured by smartphone camera. Data of moisture content, EC and included photos were taken daily. The processing of the colour plant image was done in MATLAB. DGCI and EC shows a correlation with R² was 0.7142. The predicted values of nitrogen status were correlated with R² value (0.7767), this showed that the plant nitrogen content can be estimated by its colour image feature. Future research should improve the relationship between the camera- and the app-methods in predicting DGCI values. The proposed method is fast, non-destructive and easy to apply as it does not require any laboratory work to be done.

Item Type:	Project Paper
Faculty:	Faculty of Engineering
Depositing User:	Ms Siti Mariam Giman
Date Deposited:	17 Nov 2022 07:23
Last Modified:	17 Nov 2022 07:23
URI:	http://psaspb.upm.edu.my/id/eprint/543

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