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Leaf Disease Detection Based on Machine Learning

  • Anish Polke
  • Kavita Joshi
  • Pramod Gouda
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Farming is a standout among the most imperative elements in light of which a nation’s economy is chosen. Alignments in crops are very regular, which is one of the prominent factors that leads to the disease location and detection in plant’s parts which is of high importance in agroindustry. In this way, it is imperative to effectively distinguish the maladies from the harvest to specifically shower herbicides and treat to diminish wastage utilization of concoction. In this work, we display an approach that coordinates picture handling and machine figuring out how to permit diagnosing infections from leaf pictures and to contemplate quantitative plant physiology imaging and PC vision which is utilized. Wavelet is extremely mainstream apparatus in picture preparing calculation. Surface highlights are utilized for recognition of yield. These features were mean, standard deviation, skewness, and kurtosis which we have used in this paper. We are proposing an approach, which used to identify the plant infection, i.e., plant disease. Here, we are using minimum distance classifier for the classification of the disease. The proposed approach displays a way toward robotized plant sicknesses finding on a gigantic scale.

Keywords

Image segmentation Plant disease Minimum distance classifier 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electronics and TelecommunicationG.H. Raisoni College of Engineering and ManagementPuneIndia

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