Evaluation of Image Analysis to Determine the N‐Fertilizer Demand of Broccoli Plants (Brassica oleracea convar. botrytis var. italica)

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 parameters and in the color system. The image analysis showed a close correlation between the nitrogen status of broccoli plants and the parameter of the color system especially in the wavelength ranges and nm. The relationship between nutrient concentration in leaf dry matter and the parameters 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 kg 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.

A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances

“…Several stress and biophysical plant parameters can be detected with digital imaging, for example: tiller densities across a field, insect damage, and nutrient and water deficiencies [59]. Unlike most remote sensing methods, digital imaging does not require sophisticated instruments ( i.e.…”

“…, spectroradiometers, Crop Canopy reflectance meters), but only a commercial digital camera and an image processing system. The system proposed by Kim and collaborators [60] used a custom-developed 3-CCD camera as a multi-spectral imaging sensor, while a high-resolution Leica S1 PRO digital imager was employed by [59]. A custom system based on a pushbroom CCD camera was designed in order to acquire hyperspectral images of wheat canopy [5].…”

Detection of postharvest quality loss in broccoli by means of non-colorimetric reflection spectroscopy and hyperspectral imaging

“…Concerning reports of CIELAB parameters, nitrogen status of broccoli plants had been estimated by b ⁄ -values so far (Graeff et al, 2008). In the presented work, hue angle values were not influenced by varying fertilization (data not shown).…”

“…Schouten et al (2009) measured the color of the central floret of broccoli curds by RGB color image analysis and used the inverse red channel for color rating. Digital images were also used by Graeff et al (2008) in order to evaluate the nitrogen status of broccoli plants on the field. The images were analyzed using CIELAB, and mean color parameters (a ⁄ , b ⁄ ) were related to fertilizer applications.…”

Automated rice leaf disease detection using color image analysis

“…If no such cluster can be found after examining all current clusters, assign the element as the seed for a new cluster. 4. The algorithm stops after all the elements have been assigned to one or the other cluster.…”