International Journal of Electrical and Computer Engineering (IJECE) Vol. 10, No. 6, December 2020, pp. 6531~6540 ISSN: 2088-8708, DOI: 10.11591/ijece.v10i6.pp6531-6540  6531 Journal homepage: http://ijece.iaescore.com/index.php/IJECE An effective identification of crop diseases using faster region based convolutional neural network and expert systems P. Chandana 1 , G. S. Pradeep Ghantasala 2 , J. Rethna Virgil Jeny 3 , Kaushik Sekaran 4 , Deepika N. 5 , Yunyoung Nam 6 , Seifedine Kadry 7 1,3,4 Department of Computer Science and Engineering, Vignan Institute of Technology and Science, India 2 Department of Computer Science and Engineering, Malla Reddy Institute of Technology and Science, India 5 Research Scholar, Department of Computer Science and Engineering, Vignan's Foundation for Science Technology and Research, India 6 Department of Computer Science and Engineering, Soonchunhyang University, South Korea 7 Department of Mathematics and Computer Science, Faculty of Science, Beirut Arab University, Lebanon Article Info ABSTRACT Article history: Received Dec 19, 2019 Revised May 21, 2020 Accepted May 31, 2020 The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop. Keywords: Cognitive computing Image processing IoT Object recognition Smart agriculture Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Yunyoung Nam, Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, South Korea. Email: ynam@sch.ac.kr 1. INTRODUCTION Among various revenue generating sources for the economy of any country, agriculture is a sector which plays a vital role in the economic development of any country. In other words, we can say that agriculture is the backbone for economy by providing basic ingredients to the mankind and the raw material for the industrialization. As it acts as a backbone for the country economy, the advancements of technology used in agriculture need to be made in order to increase the output which is directly proportional to the country economy. The advancements in the techniques and technology used for agriculture is represented with certain names like smart agriculture or digital agriculture or climate-smart Agriculture, and the strategies followed under this scheme include activities with respect to the actions performed in the agriculture, which may represent percentage of moisture in soil, predicting the crop yield, and suggesting the crops basing on the parameters like moisture, durability and strength of the soil. The idea of the smart agriculture is to help the agriculture industry by guiding actions required to modify and reorient agricultural systems by supporting the development and providing the food security in spite of ever-changing climate helping the country by increasing the productivity and income. The area of