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

Asian Journal of Biological and Life Sciences,2020,9,3,274-285.
Published:January 2021
Type:Review Article
Author(s) affiliations:

Gulbir Singh1,2,*, Kuldeep Kumar Yogi1

1Department of Computer Science, Banasthali Vidyapith, Rajasthan, INDIA.

2MMICT & BM, M.M. (Deemed to be University), Ambala, Haryana, INDIA.


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.