Patinformatics Studies on Tomato Plant Disease

Shivali   Amit   Wagle; Harikrishnan      R.; Elvira      Volkova; Amit   Kumar   Tiwari
Abstract

Purpose: Recent advancements in artificial intelligence in agriculture have had excellent results. This paper aims to discuss the patent perspective of tomato plant disease using artificial intelligence and discover the global research scenario.
Methodology: Patent documents from Lens.org were extracted for this study. A total of 4793 patent documents were analyzed manually. It also provides state-of-the-art patent documents on the readymix of researchers involved in tomato plant disease. The application used for patent landscape is MS Excel, PatCite, and Origin.
Results: The study analyzed the research activities worldwide, top players in the field, and patent landscape analysis. This article focuses on the different facets of the competitive landscape process of intellectual property related to tomato plant disease.
Conclusion: This review familiarizes tomato plant diseases with new and changing trends and convinces them of the importance of applying for patents to protect their innovation and the industry’s use of those innovations to promote commercialization.
Keywords: Classification, deep learning, disease, machine learning, artificial intelligence, tomato, plant disease.
Graphical Abstract

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Recent Patents on Engineering

Patinformatics Studies on Tomato Plant Disease

Abstract

Graphical Abstract
