Estimation of critical nitrogen contents in peach orchards using visible-near infrared spectral mixture analysis

Abstract

The aim of this study was to predict the critical nitrogen (N) content in peach trees using spectrometric measurements. A nutrient-controlled hydroponics experiment was designed for this purpose. Peach saplings were grown under three N conditions: deficient, sufficient, and excessive. The reflectance values of a plant leaves were measured using a handheld field spectroradiometer fitted with a plant probe. The N contents of leaves were determined in the laboratory and Gaussian mixture discriminant analysis (GMDA) was used to estimate N levels in the leaves from reflectance values. The N levels were categorized for each of the three different N conditions. The wavelengths at 425 nm, 574 nm, 696 nm, and 700 nm were found to be diagnostic of the different N levels. The model developed here classified the experimental plants with high accuracy for NDeficient, 89.28%; NSufficient, 96.30%; and NExcess, 71.42% with 85.71% coefficients. The reliability of the model was also tested under field conditions using 96 peach trees representing the three different N status. Leaves were analyzed by reflectance at 425 nm, 574 nm, 696 nm, and 700 nm, which functioned in real N, percentage classes determined based on the laboratory analyses of the orchard samples, and the data were categorized as NDeficient, NSufficient, and NExcess with a similarity ratio of 77.78%, 80%, and 67.74%, respectively with the general correct classification rate of 75%. The study findings showed that the model developed using hyperspectral reflectance data can discriminate different N nutritional status in plants with an accuracy of ≥70% and can be applied under field conditions. The results of this research provide a new perspective for future studies by showing that GMDA with hyperspectral remote sensing may be useful for the classification of different plant nutrient contents.

Classification of the Nutritional Status of Peach Trees Using Indexes from Hyperspectral Images

“…Despite that, encouraging results have been obtained with reflectance spectra. For peach tree, VIS-NIR reflectance spectra were used to classify leaves according to deficient (N < 2.99%), sufficient (N 3.00 to 3.50%), and excessive (N > 3.50%) nitrogen content [17]. Gaussian mixture discriminant analysis provided a spectral index combining values at 425 nm, 574 nm, 696 nm, and 700 nm that achieved a general correct classification rate of 75% under field conditions.…”

A Novel Approach to Increase the Efficiency of Filter-Based Feature Selection Methods in High-Dimensional Datasets With Strong Correlation Structure

“…In the study, the data containing spectrometric measurements of the critical nitrogen nutrient content of peach leaves were provided from the literature [10]. The data includes training (𝑁 1 = 84) and test (𝑁 2 = 96) data with 𝐾 = 3 class and 𝑃 = 601 variables.…”

“…One of the important problems with these methods is to select multiple features carrying the same information when strong correlations exist between features unnecessarily. For example, high-dimensional and strongly correlated data such as reflectance, image, text or DNA microarray data problem arises that hindering the learning process, especially in classification [6,[8][9][10][11]. The focus of this study is to determine the variables that will give the highest classification accuracy in classification of high dimensional data.…”

Analysis of Spectral Characteristics of Cotton Leaves at Bud Stage under Different Nitrogen Application Rates

“…The results show that NIR spectroscopy has high accuracy in predicting essential elements (N, P, K, Ca, Mg, and S 0.76 ≤ R 2 ≤ 0.98) and most trace elements (Fe, Mn, Cu, Mo, B, Cl, and Na 0.64 ≤ R 2 ≤ 0.81), and the application of NIR spectroscopy on fresh leaves is also quite accurate [15]. Dedeoglu et al [16] designed hydroponics experiments on peach trees treated with different contents of nitrogen and constructed nitrogen estimation models with reflectance at 425 nm, 574 nm, 696 nm, and 700 nm. The results showed that the models developed using hyperspectral reflectance could distinguish different nitrogen nutrient states of plants with an accuracy of ≥70%.…”