@article {10.3844/jcssp.2014.1084.1093,
article_type = {journal},
title = {IMAGE SEGMENTATION WITH ARTIFICIAL NEURAL NETWORK FOR NUTRIENT DEFICIENCY IN COTTON CROP},
author = {Sartin, Maicon A. and Silva, Alexandre C.R. Da and Kappes, Claudinei},
volume = {10},
number = {6},
year = {2014},
month = {Feb},
pages = {1084-1093},
doi = {10.3844/jcssp.2014.1084.1093},
url = {https://thescipub.com/abstract/jcssp.2014.1084.1093},
abstract = {The leaf analysis in a crop can present the need of a nutrient determined in the plant. The macronutrients deficiency in the cotton crop can be identified by specific type of colors variation by leaves images. Early identification of macronutrients deficiency can help in the growing suitable of the crop and reduce the use of agricultural inputs. This study investigates the image segmentation of the cotton leaves with deficiency of the phosphor. The segmentation is performed by difference of leaf pigmentation, according with the pattern related to macronutrient type in deficit and the cultivate. The image segmentation is made by an artificial neural network and the Otsu method. The results show satisfactory values with an optimized artificial neural network and better than the Otsu method. The results are presented by images and distinct parameters of quality analysis in the segmentation.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}