jrnls.org logo
Submit

BROWSE ALL JOURNALS

SEE ALL 19 JOURNALS
Asian Journal of Biological and Life sciencesNOW IN
Review

A Review on Recognition of Plant Disease using Intelligent Image Retrieval Techniques

Gulbir Singh1,2★, Kuldeep Kumar Yogi1★ Corresponding author

  1. 1Department of Computer Science, Banasthali Vidyapith, Rajasthan, INDIA.
  2. 2MMICT & BM, M.M. (Deemed to be University), Ambala, Haryana, INDIA.

CORRESPONDENCE

Gulbir Singh

Department of Computer Science, Banasthali Vidyapith, Rajasthan, INDIA.

gulbir.rkgit@gmail.com

Received: 04-11-2020; Revised: 30-11-2020; Accepted: 10-12-2020.

Volume 9, Issue 3 · pp. 274–285 · PUBLISHED 30 December 2020 · DOI: 10.5530/ajbls.2020.9.42

View on AJBLS original site ↗

ABSTRACT

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/ diseases. Insecticides are not always effective because they can be toxic to some birds. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomatoes and potatoes. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated method used to diagnose diseases of tomato plants.

KEYWORDS

Open in new tab

REFERENCES

As published

Showing references and in-text citations exactly as published.

    Cite this article

    SELECT FORMAT

    Singh, G., & Yogi, K. K. (2020). A Review on Recognition of Plant Disease using Intelligent Image Retrieval Techniques. Asian Journal of Biological and Life sciences, 9(3), 274–285. https://doi.org/10.5530/ajbls.2020.9.42