Full text
| |
| Author(s): |
Liliane Maria Romualdo
Total Authors: 1
|
| Document type: | Doctoral Thesis |
| Press: | Pirassununga. |
| Institution: | Universidade de São Paulo (USP). Faculdade de Zootecnica e Engenharia de Alimentos (FZE/BT) |
| Defense date: | 2013-04-01 |
| Examining board members: |
Pedro Henrique de Cerqueira Luz;
Valdo Rodrigues Herling;
William Natale;
Francisco de Assis de Carvalho Pinto;
Silvia Helena Modenese Gorla da Silva
|
| Advisor: | Pedro Henrique de Cerqueira Luz |
| Abstract | |
An artificial vision system (AVS) for nutrient diagnosis of corn, based on analysis of images of leaves was recently proposed by SCG-IFSC and Agrarian-FZEA/USP. The objective was evaluate the nutritional status of maize grown in a greenhouse in nutrient solution with induced nutritional deficiency and sufficiency of nitrogen (N), phosphorus (P), potassium (K) and manganese (Mn) using artificial vision, and correlate the results obtained with foliar analysis, and then the field in order to validate the diagnosis by artificial vision system developed. Doses of nutrients were established by omission, 1/5, 2/5 and full dose, combined into three developmental stages of corn (V4, V7 and R1), with four replications. The experiment was for each individual element. Images of leaves were collected in each epoch indicating the stage (FI), old leaves (FV) for N, P and K and Mn for new leaves, which were first scanned at 1200 dpi, then sent to be analyzed chemically. The biometric variables (height, stem diameter and number of leaves) were also evaluated, and the dry matter production of shoots and roots was determined, besides the determination of nutritional content. The omission of N, P and K caused typical nutrient deficiencies provided in the leaves of maize when grown in a greenhouse. The changes in the leaves of maize, with doses of nutrients studied, allowed the imaging necessary for training SVA in a greenhouse. The use of images of the greenhouse to train the SVA aiming to validate images of the field has led to confusion in the interpretation leading to errors of classification, however the use of this technology for nutrient diagnosis of corn, both in the greenhouse and in the field, is promising. (AU) | |