ARTIFICIAL VISION FOR NUTRITIONAL DIAGNOSIS OF CORN GROWN WITH CALCIUM SILICATE AND MAGNESIUM IN PONDERAL DOSES AND HIGH DILUTIONS
DOI:
https://doi.org/10.18011/bioeng2020v14n1p36-47Keywords:
precision agriculture, digital image processing, statistical classifier, homeopathyAbstract
The hypothesis of the present work was that an artificial vision system was able to characterize the nutritional deficiency in the corn leaf, under homeopathic preparations, using the spectral properties of the culture. The work, carried out at the Faculty of Zootechnics and Food Engineering (FZEA) - University of São Paulo (USP), in Pirassununga / SP / Brazil studied the nutritional behavior of corn (Zeamays L.), hybrid Biogene 7049H. The experimental design used was a randomized block with 6 treatments: calcium and magnesium silicate, in the following dynamizations CH6 (dilution at 10-12), CH9 (dilution at 10-18), CH12 (dilution at 10-24) and CH15 (dilution to 10-30), a control treatment without application of silicon and a treatment with calcium and magnesium silicate at a dose of 1 t ha-1, with 10 repetitions. The corn leaves were digitized by "scanner" and the image processing was performed by the MATLAB® program. Three different sizes of image blocks were tested 9 x 9; 20 x 20 and 40 x 40 "pixels". The best results were achieved by the blocks of 40 x 40 "pixels".
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