A Diagnostic Tool for Magnesium Nutrition in Maize Based on Image Analysis of Different Leaf Sections

Abstract

The nutritional status of maize (Zea mays L.) can be diagnosed by chemical analysis of leaves, which is very slow, or by visual diagnosis of deficiency symptoms, which is dependent on observer experience. The artificial visual system (AVS) is a technology to identify nutritional deficiencies in maize, allowing correction for nutrient supply at earlier development stages in maize. Our objective was to propose methods of artificial vision and pattern recognition to identify the concentration of magnesium (Mg) in maize plants grown in the greenhouse. Magnesium concentrations were 0.0, 0.65, 1.3, and 2.0 mM Mg, with four replications. Leaf scans were collected at V4 (four leaves fully developed), V6 (six leaves fully developed), and V8 (eight leaves fully developed) stages, and these leaves were samples for chemical assays. Such images were processed using AVS methods. Volumetric fractal dimension (VFD), Gabor wavelet (GW), and VFD with canonical analysis (VFDCA) were techniques used by the AVS to extract deficiency characteristics in the leaf images. The increase of Mg in the nutrient solution caused an increase in the Mg concentration in leaves, resulting in typical visual symptoms. The AVS method was able to identify all levels of deficiency, scoring 75.5% of rights in images of the middle section of leaves in the VFDCA method, in color scans of V4 leaves. The AVS was efficient at diagnosing Mg concentrations in leaves of maize during the V4 stage.

Use of artificial vision techniques for diagnostic of nitrogen nutritional status in maize plants

“…These results confirm the importance of color information in this study in order to characterize the nutritional symptoms on leaves of corn. Similar results were observed by Silva et al (2014) using the same methods of this study to patterns recognize of nutrient deficiencies for magnesium. The confusion matrices for GW in each growing stage of maize are shown in Table 3.…”

“…Sena Júnior et al (2008) identified through image analysis nutritional stages of wheat plants. Silva et al (2014) evaluated different methods for feature extraction in images of maize leaves in the V4 stage, grown in greenhouse under nutritional deficiency induced of magnesium. With the obtained results, it was found global percentage of right 76% with reliable Kappa index.…”

Rapid Recognition of Field-Grown Wheat Spikes Based on a Superpixel Segmentation Algorithm Using Digital Images

“…Moreover, computer vision with its advantages of high precision and intelligence attracted it as an alternative to human inspection. This technology was a dramatic boost for pest detection (Boissard et al, 2008;Shahin and Symons, 2011;Ding and Taylor, 2016;Senthilkumar et al, 2017), growth monitoring (Clevers and Leeuwen, 1996;Chaerle and Straeten, 2000;Wang et al, 2013;Silva et al, 2014), yield prediction (Salazar et al, 2007;Dunn and Martin, 2010;Aggelopoulou et al, 2011;Aguate et al, 2017) and species recognition (Neuman et al, 1987;Lópezgranados et al, 2006;Tellaeche et al, 2011;Pantazi et al, 2016).…”

High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning

“…The width is measured as the Euclidean distance between the minimum and maximum point coordinates of the axis corresponding to the width. The calculation for each part is as shown in Equation (3).…”

“…In addition to sensor technology, a variety of algorithms, especially machine learning, have enabled a variety of analyzes of plant phenotypes. Leaf area and nutrient concentration have been identified through the use of plant images and pattern recognition methods [3,4]. These studies were developed for the purpose of automation.…”