Morphological variation on tomato leaves due to different nitrogen contents

Elliptic Fourier Analysis (EFA) is a method used to quantify shape differences.  It mathematically describes the entire shape of an object by transforming the contour into Fourier coefficients, used as variables for statistical analysis, and involving the fitting of some type of curve to the object outline.  Generally, the shape of agricultural products such as fruit, vegetables, grain and in addition other organs of plant is one of the most important factors for their classification and grading in relation to commercial quality and organoleptic properties.  The aim of this study is to quantify the morphological variation of the shape of tomato leaves in response to their different nitrogen (N) content using the EFA coefficients, the fractal geometry and the perimeter ratio in combination with the Partial Least Squares Discriminant Analysis (PLS-DA).  The analyses were realized on a tomato crop where each sample was chemically analyzed at the laboratory to establish the N content.  The leaves (168) were divided into 3 groups following different N concentrations.  Results suggest no relation between leaves lengths and N concentration is present following the Kruskal-Wallis performed with a p=0.735.  The PLS-DA performing on the EFA coefficients, fractal index and perimeter ratio shows a high sensitivity, sensibility, and reduced mean classification error (82.3%, 81.07% and 18.3% respectively).  The percentages of the correct classification in the model resulted to be 69.29% while the independent test equal to 56.1%.  This study demonstrated the relation between leaf shape and N content (expressed in 3 concentration groups).Keywords: Tomato leaf, elliptic Fourier analysis, fractal index, perimeter ratio, partial least squares discriminant analysis

Development of a Rapid Soil Water Content Detection Technique Using Active Infrared Thermal Methods for In-Field Applications

The aim of this study was to investigate the suitability of active infrared thermography and thermometry in combination with multivariate statistical partial least squares analysis as rapid soil water content detection techniques both in the laboratory and the field. Such techniques allow fast soil water content measurements helpful in both agricultural and environmental fields. These techniques, based on the theory of heat dissipation, were tested by directly measuring temperature dynamic variation of samples after heating. For the assessment of temperature dynamic variations data were collected during three intervals (3, 6 and 10 s). To account for the presence of specific heats differences between water and soil, the analyses were regulated using slopes to linearly describe their trends. For all analyses, the best model was achieved for a 10 s slope. Three different approaches were considered, two in the laboratory and one in the field. The first laboratory-based one was centred on active infrared thermography, considered measurement of temperature variation as independent variable and reported r = 0.74. The second laboratory–based one was focused on active infrared thermometry, added irradiation as independent variable and reported r = 0.76. The in-field experiment was performed by active infrared thermometry, heating bare soil by solar irradiance after exposure due to primary tillage. Some meteorological parameters were inserted as independent variables in the prediction model, which presented r = 0.61. In order to obtain more general and wide estimations in-field a Partial Least Squares Discriminant Analysis on three classes of percentage of soil water content was performed obtaining a high correct classification in the test (88.89%). The prediction error values were lower in the field with respect to laboratory analyses. Both techniques could be used in conjunction with a Geographic Information System for obtaining detailed information on soil heterogeneity

Nitrogen Concentration Estimation in Tomato Leaves by VIS-NIR Non-Destructive Spectroscopy

Nitrogen concentration in plants is normally determined by expensive and time consuming chemical analyses. As an alternative, chlorophyll meter readings and N-NO3 concentration determination in petiole sap were proposed, but these assays are not always satisfactory. Spectral reflectance values of tomato leaves obtained by visible-near infrared spectrophotometry are reported to be a powerful tool for the diagnosis of plant nutritional status. The aim of the study was to evaluate the possibility and the accuracy of the estimation of tomato leaf nitrogen concentration performed through a rapid, portable and non-destructive system, in comparison with chemical standard analyses, chlorophyll meter readings and N-NO3 concentration in petiole sap. Mean reflectance leaf values were compared to each reference chemical value by partial least squares chemometric multivariate methods. The correlation between predicted values from spectral reflectance analysis and the observed chemical values showed in the independent test highly significant correlation coefficient (r = 0.94). The utilization of the proposed system, increasing efficiency, allows better knowledge of nutritional status of tomato plants, with more detailed and sharp information and on wider areas. More detailed information both in space and time is an essential tool to increase and stabilize crop quality levels and to optimize the nutrient use efficiency