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