10.1016/j.compag.2014.03.009

Bibtex

Thesis CTDIA prize

The Academic Dalcimar Casanova won the second best dissertation in the field of Artificial Intelligence. The prize was awarded by the Special Committee on Artificial Intelligence of the Brazilian Computer Society (SBC-CEIA) during the 2010 Joint Conference.

To read more visit: VII Best MSc Dissertation/PhD Thesis Contest in Artificial Intelligence

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

L. M. Romualdo and P. H. C. Luz and F. F. S. Devechio and M. A. Marin and A. M. G. Zuniga and O. M. Bruno and V. R. Herling

COMPUTERS AND ELECTRONICS IN AGRICULTURE, 104():63-70, 2014

The identification of the nutritional status of maize by foliar chemical analysis requires sampling of leaves when the plant is in an advanced stage of development, hindering corrective action in ongoing cultivation, if deficiency detection of a specific nutrient occurs. An artificial vision system (AVS) is a set of methods used for analysis and interpretation of images. Therefore, an AVS is being developed to identify nutrient deficiencies at different stages of plant development, especially in the early stages of growth, which may contribute to early diagnosis and correction in the same cycle of growth. The objective was to evaluate methods of digital image processing to develop the AVS to diagnose induced nitrogen deficiency in maize leaves. The experiment was done in greenhouse and the treatments were N doses (0.0; 3.0; 6.0 e 15.0 mMol L-1) combined with three growing stages (V4, V7 and R1). The images of maize leaves were digitized in 1200 dpi. After scanning, leaves were chemically analyzed for N content and was determined the dry mass of plants. The studied methods in AVS were: Volumetric Fractal Dimension (VFD), Gabor Wavelet (GW) and VFD with canonical analysis. The omission and reduction of nitrogen in maize plants resulted in typical symptoms of N deficiency. The AVS was able to identify levels of nitrogen deficiency in the early stages of development of corn, with global percentage of right of 82.5% at V4 and 87.5% at V7. The GW technique with color images resulted in the better method for features extraction. (C) 2014 Elsevier B.V. All rights reserved.