Abstract
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.
Highlights
The world maize (Zea mays L.) production was 1162 million tons during the 2020/2021 crop season
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, using an artificial vision system (AVS) of different leaf sections
As the Ca concentration increased in the nutrient solution, the concentration in leaves increased from 0.98 to 8.11 g.kg-1 of Ca in index leaf (IL) and from 0.42 to 2.44 g kg-1of Ca in new leaf (NL)
Summary
The world maize (Zea mays L.) production was 1162 million tons during the 2020/2021 crop season. The evaluation of nutritional state of the plants is usually done through chemical analysis or visual evaluation (Romualdo et al, 2014). Leaf chemical analyses of the nutrient status of the plant are time consuming and expensive Reis et al (2006). The identification of the deficiency using leaf chemical analyses imply sampling at advanced phenological stage, which does not allow to take remediation actions for the crop (Wu et al, 2007). The visual diagnosis is a practical and quick method to investigate the nutrient deficiency in the plant, its precision is limited and subjected to the experience of the observer (Baesso et al, 2007). The difficulties of evaluating the nutritional status of in maize plants on the same crop cycle are the motivation to propose additional approaches in nutrients (Luz et al, 2018). Since the chemical and visual diagnosis of jas.ccsenet.org
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