Contagious disease detection in cereals crops and classification as 'solid' or 'undesirable': an application of pattern recognition, image processing and machine learning algorithms

  • Abstract
  • Keywords
  • References
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  • Abstract

    Illnesses in plants diminish the profitability and economy of a nation. Building up a robotization framework for location and arrangement of illnesses in tainted plants is a thriving exploration territory in the field of exactness farming. Oats crops are generally developed temperate product on the planet. Observing of these yields, particularly amid development, empowers us to lessen the harm at the soonest and exact conclusion of these maladies can diminish the sickness spread which will bring about ecological assurance and better return. By utilizing design acknowledgment and picture preparing calculations, the advancement of choice emotionally supportive network for plant security turns out to be more proficient. This paper shows a way to deal with recognize parasitic maladies in three oats trims in particular Maize, Rice and Wheat, utilizing design acknowledgment, machine-learning and picture handling strategies and arrange them as 'Solid' or 'Unfortunate'. It is finished by separating distinctive highlights like shading, shape and surface from the tainted areas of these plant pictures. 227 parasitic infection pictures of three oat crops i.e. Maize (71), Rice (92) and Wheat (64) were downloaded from different sources and considered in this exploration. Some solid pictures of same harvests were additionally downloaded for characterization reason. According to the calculation took after, after the pre-handling step, K-implies grouping strategy was utilized to section the unhealthy zone from the plant and in view of that three bunches of pictures (K=3) were created. Highlight extraction was performed trailed by include decrease utilizing diverse techniques lastly seven diminished highlights for maize, three highlights for rice and five highlights for wheat were chosen which brought about most extreme grouping precision of 87.60% for maize utilizing Naive Bayes classifier, 92.30% for rice utilizing both Naive Bayes and LibSVM classifiers, and 94.18% for wheat utilizing Multilayer Perceptron. On a huge scale, it can be finished up from the outcomes that Naive Bayes classifier gave best characterization exactness of 90.97% for all the three grain crops consolidated.

  • Keywords

    GLCM; Gabor; Classification; K-Means Clustering Segmentation; Naive Bayes.

  • References

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Article ID: 9043
DOI: 10.14419/ijet.v7i1.2.9043

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