Model of Color Parameters Variation and Correction in Relation to “Time-View” Image Acquisition Effects in Wheat Crop
Open Access
- 20 March 2020
- journal article
- research article
- Published by MDPI AG in Sustainability
- Vol. 12 (6), 2470
- https://doi.org/10.3390/su12062470
Abstract
Many images of agricultural crops are made at different times of the day, images with different spectral information about the same crop in relation to conditions when the picture was taken. A set of 30 digital images of a wheat crop in the BBCH 3-Stem elongation code 32–33 stage was captured between 9 am and 14 (UTC+3), in the 0°–180° variation range of the image acquisition angle on the E-W axis (cardinal directions). A high variation of the spectral data given by the combination of the hour (h) and angle (a) at which the images were captured was found. The interdependence relationship between the analyzed parameters (r, g, and b), and the time (t) and the angle (a) of image acquisition was assessed with the linear correlation coefficient. By calculating the roots of the mathematical expressions of the correlation coefficients dependence on the angles (a) or times of day (t), the optimal angle and time were determined as a combination of the two variables for capturing images and obtaining optimal ro, go, bo values. The correction coefficients of the normalized r, g, and b values obtained out of the optimal field were determined. To this end, the multiplication of the r(a,t), g(a,t), and b(a,t) values with the ρa,t, γa,t, and βa,t correction coefficients was suggested to reach the optimal values for sustainable decisions.This publication has 59 references indexed in Scilit:
- A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent AdvancesSensors, 2013
- Nitrogen determination in pepper (Capsicum frutescens L.) plants by color image analysis (RGB)AFRICAN JOURNAL OF BIOTECHNOLOGY, 2011
- Geosensors to Support Crop Production: Current Applications and User RequirementsSensors, 2011
- Accurate inference of shoot biomass from high-throughput images of cereal plantsPlant Methods, 2011
- Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysisPlant Methods, 2011
- Remote Sensing and Geospatial Technological Applications for Site-specific Management of Fruit and Nut Crops: A ReviewRemote Sensing, 2010
- Can Commercial Digital Cameras Be Used as Multispectral Sensors? A Crop Monitoring TestSensors, 2008
- Responsive in-season nitrogen management for cerealsComputers and Electronics in Agriculture, 2008
- Autonomous robotic weed control systems: A reviewComputers and Electronics in Agriculture, 2008
- A field assessment of a potential method for weed and crop mapping on the basis of crop planting geometryComputers and Electronics in Agriculture, 2001