274 Asian Journal of Biological and Life Sciences, Vol 9, Issue 3, Sep-Dec, 2020 Review Artcle Correspondence: Mr. Gulbir Singh 1,2 1 Scholar, Department of Computer Science, Banasthali Vidyapith, (Rajasthan), INDIA. 2 MMICT & BM, M.M. (Deemed to be University), Ambala, (Haryana), INDIA. Phone no: +91-09719042509 Email: gulbir.rkgit@gmail.com A Review on Recognition of Plant Disease using Intelligent Image Retrieval Techniques Gulbir Singh 1,2, *, Kuldeep Kumar Yogi 1 1 Department of Computer Science, Banasthali Vidyapith, Rajasthan, INDIA. 2 MMICT & BM, M.M. (Deemed to be University), Ambala, Haryana, INDIA. Submission Date: 04-11-2020; Revision Date: 30-11-2020; Accepted Date: 10-12-2020 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 artifcial 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 identifcation 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 frst level of the lowest level. This is the most sophisticated method used to diagnose diseases of tomato plants. Key words: Artifcial Intelligence, Deep Learning, Tomato Leaf Disease, Potato Leaf Disease, Content-based Image Retrieval (CBIR). SCAN QR CODE TO VIEW ONLINE www.ajbls.com DOI: 10.5530/ajbls.2020.9.42 INTRODUCTION Agriculture is the backbone of the economy of India. The large-scale commercialisation of agriculture has had a very negative effect on our climate. The use of chemical pesticides creates a substantial build-up of chemicals in our atmosphere, soil, water, food, livestock and also in our own bodies. Artifcial fertilisers have a short-term effect on production but have a long-term detrimental impact on the environment. After leaching and loss, the chemical fertiliser will remain in the soil for many years and will pollute the groundwater. Another negative effect of this development is its effect on the situation of rural populations around the world. Despite the so-called rise in production, the plight of farmers in nearly every country in the world is deteriorating. It’s the root of organic farming. Natural farming can solve all these issues. The main practices in organic farming rely on fertilisation and control of pests. [1] Potato is one of the most signifcant food crops. The diseases causing substantial yield loss in potato are Phytophthora infestans (late blight) and Alternaria solani (early blight). Early detection of these diseases can allow to take preventive measures and mitigate economic and production losses. Over the last decades, the most practiced approach for the detection and identifcation of plant diseases is naked eye observation by experts. But in many cases, this approach proves unfeasible due to the excessive processing time and unavailability of experts at farms located in the remote areas. Hence,