Calcium Deficiency Diagnosis in Maize Leaves Using Imaging Methods Based on Texture Analysis

The artificial vision system (AVS) uses image analysis methods that can interpret images and identify nutritional deficiency symptoms in plant, even the early stages of development. objective this study was to propose processing using by texture calcium (Ca) maize (Zea mays L.) plants grown nutrient solution. Plants were solution a greenhouse. Calcium doses 0.0; 1.7; 3.3 5.0 mM Ca, with four replications. Plant leaf sampled at three main development: V4 (plants leaves fully developed), V6 six developed) V8 eight developed). Sampled material split into (i) index (IL) growing stage (V4 = 4, 6, 8), (ii) new (OL), both capture chemical analysis. Such scanned, processed AVS chemically analyzed. used extract characteristics were: Volumetric Fractal Dimension (VFD), Gabor Wavelet Energy (GWE) VFD canonical (VFDCA). amount Ca resulted variation concentration NL IL, allowing observation typical deficiency. method able all levels leaves, being GWE best indicator color images, scoring 80% rights middle section V4.