Application of computer vision and low-cost artificial intelligence for the identification of phytopathogenic factors in the agro-industry sector

The article describes the approach for developing the proposed algorithm based on artificial intelligence and computer vision for phytopathogenic detection. The methodology for the development and validation stages is specifically discussed.
Authors: J J Castro-Maldonado, J A Patiño-Murillo, A E Florian-Villa and O E Guadrón-Guerrero
 
Published under licence by IOP Publishing Ltd
 
Journal of Physics: Conference Series, Volume 1126, conference 1
 
Abstract
 
This work presents a perspective of the processes of phytosanitary control in crops, starting with traditional methods and strategies applied for precision agriculture and the use of artificial intelligence and computer vision in this area. Then, the article describes the approach for developing the proposed algorithm based on artificial intelligence and computer vision for phytopathogenic detection. The methodology for the development and validation stages is specifically discussed. Several tests were carried out with the different image processing algorithms studied. Results show how the selected method with Haar filters and Gradient-oriented Histograms performs effectively for identification of phytopathogenic factors, from both qualitative and quantitative analysis.

Photo: Experimental work developed for the validation of the proposed algorithm. The detected spot can be appreciated by red boxes in the pictures. (credits: J J Castro-Maldonado, J A Patiño-Murillo, A E Florian-Villa and O E Guadrón-Guerrero)

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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