Evaluation of Image Segmentation and Filtering With ANN in the Papaya Leaf

Precision agriculture is area with lack of cheap technology. The refinement of the production system brings large advantages to the producer and the use of images makes the monitoring a more cheap methodology. Macronutrients monitoring can to determine the health and vulnerability of the plant in sp...

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Bibliographic Details
Published in:arXiv.org 2014-03
Main Authors: Sartin, Maicon A, Alexandre C R da Silva
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Image filters
Image segmentation
Markov analysis
Neural networks
Noise levels
Signs and symptoms
Online Access:Get full text
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Summary:Precision agriculture is area with lack of cheap technology. The refinement of the production system brings large advantages to the producer and the use of images makes the monitoring a more cheap methodology. Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In this paper is analyzed the method based on computational intelligence to work with image segmentation in the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for image segmentation and filtering, several variations of parameters and insertion impulsive noise were evaluated too. Satisfactory results are achieved with artificial neural for segmentation same with high noise levels.
ISSN:2331-8422
DOI:10.48550/arxiv.1403.3057