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

Using UV-C radiation and image analysis for fungus control in tomato plants

“…Some diseases do not have any visible symptoms associated, in those cases; usually some sort of complex evaluation is necessary. However, most diseases exhibit some type of indication visible for humans and hence they are susceptible to be captured using commercial or industrial cameras [28,30]. Trained raters may be also efficient in recognizing and quantifying diseases, but this approach have some disadvantages.…”

“…On the other hand, parameters like canopy, yield, product quality, and diseases characterization are important measurements from the farmer's point of view, where image acquisition and analysis are especially useful. These techniques have helped in characterization of plants and fruits as well as to implement sorting techniques, due to rapid processing, non-physical contact with the specimens and its low cost [28,29]. Some diseases do not have any visible symptoms associated, in those cases; usually some sort of complex evaluation is necessary.…”

“…By the use of digital images and depending on the application, some of the mentioned problems can be reduced or even eliminated. Typical visual disease assessments rely on estimates of percent of leaf area covered by lesions that can be performed measuring the area of the leaves that are affected by the disease using calibrated systems of image acquisition [28]. Since lesions could be small, accurate estimates of lesions are difficult to make only visually.…”

Análise de textura em imagens de folha para diagnose nutricional precoce em culturas de milho

“…Exemplos da utilização de metodologia de visão computacional para diagnóstico nutricional são a dissertação Zúñiga (2012) e as referências Romualdo et al (2014) e . A metodologia utilizada nesses trabalhos é a base para esta tese e é explicada no Capítulo 5.…”

Reconhecimento de padrões de nutrição para nitrogênio e potássio em híbridos de milho por análise de imagens digitais

“…Esse resultado concorda com os encontrados por Romualdo et al (2014) Na Tabela 3.4.19, observa-se ainda que no H1 a classificação foi considerada muito boa em 1 (um) caso, boa em 1 caso, razoável em 8 (oito casos) e péssima em 3 (três) casos.…”