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Due to uncertain environmental conditions such as untimely rainfall, hailstorms, draught, fog the agriculture sector faces huge loss in crop yield. One of the biggest reason is plant leaf diseases. Therefore the need arises to diagnose the plant leaf diseases beforehand so that the diseases could be avoided and crop yield loss could be minimized. The paper represents the bibliometric study of plant leaf disease diagnosis using Artificial Intelligence. The study focuses on 472 scientific documents such as journals, articles, book chapters publicized in various journals. These documents are extracted from Scopus database after querying it with keywords related to plant diseases classification and Artificial Intelligence. The articles are analyzed for the time period of 2010 to 2018. The analysis was done using tools such as VOSviewer, NodeXL and Gephi which are open source tools. The survey prominently focuses on the type of publications, language used in publicizing the documents, year wise count of the publications the field, based on geographical locations of the publications, trends of keywords found in the articles, based on discipline area, top authors contributing to the area, institutions and universities contributing to the area and the number of citations the documents received. It was observed that the English language is primarily used for publication of the documents. India contributes maximum numbers of documents in the field of plant leaf disease followed by China. The study also discovered that the journal that has the maximum number of publications count in this research field is Computer and Electronics in Agriculture.