Theoretical Determination of a Critical Nitrogen Dilution Curve Based on the Carrot Case Study
The objectives of the present study were to: (a) formulate a general dilution equation for a critical nitrogen percentage in a leaf (Nc); (b) monitor dilution function of carrot as a case study; (c) demonstrate applicability of digital color imaging to monitor total nitrogen (Ntot) in crops. Carrot (Daucus carota) was grown on loess soil at five Ntot application rates. Ntot weight (kg/ha) was obtained by standard laboratory analysis and by image processing. Total yield of the treatments was the sum of canopy and roots dry matter. A new logistic decay curve Nc = a/[1 + (W/W0)b] agreed with experimental data. Nc (g/kg) is the critical nitrogen level, a and b are coefficients, and W/W0 is the relative dry matter biomass. Values of Nitrogen Nutrition Index (NNI) ≥ 1.0 indicated that there was no nitrogen deficiency in treatment larger than 100% application of Ntot. The weight of nitrogen in form of multiplication of the dry leaves weight (W) and N percent is suitable for the determination of nitrogen status. The availability of image-based data for N percent is faster, timely and less expensive than that of laboratory test. Applicability of digital color camera to monitor Ntot in crops instead of laboratory test was successfully demonstrated.
Applicability of digital color imaging for monitoring nitrogen uptake and fertilizer requirements in crops
The advancements in remote sensing in combination with sensor technology, both passive and active, enable growers to analyze an entire crop field, its local features and crop conditions. Nutrient deficiency, and in particular nitrogen deficiency, may cause substantial crop losses. This deficiency needs to be identified immediately. A faster the detection and correction, a lesser the damage to the crop yield. In the present work, an applicability of digital color imaging to monitor nitrogen uptake in crops is demonstrated. The measurements were performed in a carrot and wheat fields as well as in a greenhouse. The images of canopy were taken during the entire growing season by use a hand held digital color (RGB) camera together with plants collection for lab analysis. The nitrogen weight in plant leaves, kg/ha, was obtained by image processing and compared with the standard laboratory analysis. Applicability of digital color imaging to monitor N uptake in crops instead of laboratory test is successfully demonstrated. The availability of RGB image-based data for N uptake is faster, timely and less expensive than that of laboratory test and can be obtained by low-cost ground-based imaging devices, unmanned aerial vehicles (UAV) and satellites.
Evaluating Multiple Indices from a Canopy Reflectance Sensor to Estimate Corn N Requirements
With the increasing cost of fertilizer N, there is a renewed emphasis on developing new technologies for quantifying in-season N requirements for corn (Zea mays L.). The objectives of this research are (i) to evaluate different vegetative indices derived from an active reflectance sensor in estimating in-season N requirements for corn, and (ii) to consider the influence of the N:Corn price ratio on the economic optimum nitrogen rate (EONR) developed using these indices. Field experiments were conducted at eight site-years in central Pennsylvania. A two-way factorial experiment was implemented as a split-plot randomized complete block (four blocks) design, with different rates ofN applied (i) at planting (N PL ) to create a range ofN supply, corn color, and radiance; and (ii) at V6 (N V6 ) to measure yield response to N V6 . Canopy reflectance measurements were obtained using a Crop Circle (Holland Scientific, Lincoln, NE) sensor just before N V6 applications, and grain yield was determined at harvest. TheEONRwas determined using a quadratic-plateau yield response function for price ratios from zero to 14:1, then regressed on 21 combinations of absolute and relative spectral bands and indices. The EONR at V6 at 6:1 price ratio ranged from 0 to 221 kg ha -1 among the eight site-years, with a mean of 69 kg ha -1 . Better prediction of EONR was obtained by indices calculated relative to a high N plot rather than absolute indices. Relative Green Difference Normalized Vegetation Index by ratio (RGNDVI R ) was the best predictor of EONR at V6 when expressed as a linear-floor model (R 2 of 0.79). A relationship was developed so that EONR estimates derived using the Crop Circle sensor could be easily adjusted based on the current N:Corn price ratio. When N requirements are high (RGNDVI R = 0.8) and if the price ratio changes from 4:1 to 10:1, the EONR would change from 267 to 214 kg ha -1 N.