Paddy Plant Disease Recognition, Risk Analysis, and Classification Using Deep Convolution Neuro-Fuzzy Network


  • V. Vinoth Kumar School of Computer Science and Engineering, Jain (Deemed to be University), Bangalore, India
  • K. M. Karthick Raghunath Department of Computer Science and Engineering, MVJ College of Engineering, Bangalore, India
  • N. Rajesh University of Technology and Applied Science, Shinas, Sultanate of Oman
  • Muthukumaran Venkatesan Department of mathematics, School of Applied Sciences, REVA University, Bengaluru, Karnataka
  • Rose Bindu Joseph Department of Mathematics at Christ Academy Institute for Advanced Studies, Bangalore, India
  • N. Thillaiarasu School of Computing and Information Technology, REVA University, Bengaluru, India



Multimedia, Convolution Neuro-Fuzzy, Accuracy, Disease Classification


A significant number of the world’s population is dependent on rice for survival. In addition to sugarcane and corn, rice is said to be the third most growing staple food in the world. As a consequence of intensive usage of man-made fertilizers, paddy plant diseases have also risen at a faster pace in current history. Exploring the possible disease spread and classifying to detect the consequent impact at an early stage will prevent the loss and improve rice production. The core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts. Both detection and recognition are carried out based on the risk analysis of paddy crop leaf images. We suggest a Deep Convolutional Neuro-Fuzzy Method (DCNFM) that combines one of the advanced machine learning variant, namely deep convolutional neural networks (DCNNs) and uncertainty handler called fuzzy logic. The synthesis has the benefits of both fuzzy logic and DCNNs when dealing with unstructured data, extracting essential features from imprecise and ambiguous datasets. From the crop field, continuous image data are captured through image sensors and fed as a primary input to the proposed model to analyze the risk and then later to classify them for precise recognition/detection of the disease. The detection/recognition rate of the DCNFM is found to be 98.17% which is comparatively found to be effective in comparison with the traditional CNN model.


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Author Biographies

V. Vinoth Kumar, School of Computer Science and Engineering, Jain (Deemed to be University), Bangalore, India

V. Vinoth Kumar is an Associate Professor at Department of Computer Science, MVJ College of Engineering, India. His current research interests include Big Data Analytics, Internet of Things, machine learning and wireless networks. He is the author/co-author of papers in international journals and conferences including SCI indexed papers. He has published as over than 35 papers in IEEE Access, Springer, Elsevier, IGI Global, Emerald etc. He is a reviewer for Elsevier, IEEE Access, IEEE Transactions, and Springer journal. He has demonstrable experience in leading large-scale research projects and has achieved many established research outcomes that have been published and highly cited in many significant Journals and Conferences. He is the Associate Editor of International Journal of e-Collaboration (IJeC) and Editorial member of various journals. He has also been a guest editor of several international journals including, Journal of Intelligent Manufacturing (Springer), International Journal of Intelligent Computing and Cybernetics, International Journal of e-Collaboration (IJeC), International Journal of Pervasive Computing and Communications(IJPCC), International Journal of System of Systems Engineering(IJSSE), International Journal Speech Technology (IJST)-Springer, Journal of Reliable Intelligent Environments (JRIE), International journal of Information Technology and Web Engineering (IJITWE),International Journal of Machine Learning and Computing (IJMLC),International Journal of Cloud Computing (IJCC),International Journal of Information Quality (IJIQ) ,Journal of Computational and Theoretical Nanoscience and International Journal of Intelligent Enterprise (IJIE). He has been professional society member of ISTE, IACIST and IAENG. He has co-chaired major Conferences Program Committees such as: ICACB’18, ICAIIS’19 etc. He has filed 3 IPR patents in IOT applications and currently doing funding project to CSIR and ISRO.

K. M. Karthick Raghunath, Department of Computer Science and Engineering, MVJ College of Engineering, Bangalore, India

K. M. Karthick Raghunath is an Associate Professor in the Computer Science and Engineering Department in MVJ College of Engineering, Bangalore, India. He has received his B. Tech., in Information Technology from Anna University in 2008 and M.E., in Pervasive Computing Technology from Anna University (BIT Campus) in 2011. In 2019, he completed his Ph.D. degree from Anna University, Chennai. With nearly a decade of experience in teaching, his areas of specialization include pervasive computing, Artificial Intelligence, IoT, Data Science, and WSN. He has five patents in IPR and has published approximately more than ten papers in reputed international journals. He has authored ”Response Time Optimization in Wireless Sensor Networks,” published in LAP LAMBERT Academic Publishing, U.K. He has published numerous papers in National and International Conferences and has served as editor/reviewer for Springer, Elsevier, Wiley, IGI Global, Emerald, ACM, and many more. He is an active member of IEEE, ACM, I.E.(I), IACSIT, and IAENG. He has organized several National Workshops and Technical Events. He is regularly invited to deliver lectures in various programs for imparting skills in research methodology to students and research scholars.

N. Rajesh, University of Technology and Applied Science, Shinas, Sultanate of Oman

N. Rajesh completed his PhD in Computer Science from Bharathiar University, MCA from Thiruvalluvar University and BSc Computer science from Madras University. He is currently working as a Lecturer at University of Technology and Applied Science, Shinas, Sultanate of Oman. His research interest includes data mining, machine learning, big data analytics, and Privacy preserving algorithms. He published articles in the National conferences, International Indexed journals, including SCI, WoS, SCOPUS.

Muthukumaran Venkatesan, Department of mathematics, School of Applied Sciences, REVA University, Bengaluru, Karnataka

Muthukumaran Venkatesan was born in Vellore, Tamilnadu, India, in 1988. He received the B.Sc. degree in Mathematics from the Thiruvalluvar University Serkkadu, Vellore, India, in 2009, and the M. Sc. degrees in Mathematics from the Thiruvalluvar University Serkkadu, Vellore, India, in 2012. The M. Phil. Mathematics from the Thiruvalluvar University Serkkadu, Vellore, India, in 2014 and Ph.D. degrees in Mathematics from the School of Advanced Sciences, Vellore Institute of Technology, Vellore in 2019. At present, he has a working Assistant Professor in the Department of Mathematics, REVA University Bangalore, India. His current research interests include Algebraic cryptography, Fuzzy Image Processing, Machine learning, and Data mining. His current research interests include Fuzzy Algebra, Fuzzy Image Processing, Data Mining, and Cryptography. Dr. V. Muthukumaran is a Fellow of the International Association for Cryptologic Research (IACR), India; He is a Life Member of the IEEE. He has published more than 30 research articles in peer-reviewed international journals. He also presented 19 papers presented at national and international conferences.

Rose Bindu Joseph, Department of Mathematics at Christ Academy Institute for Advanced Studies, Bangalore, India

Rose Bindu Joseph is currently working as an Associate Professor in the Department of Mathematics at Christ Academy Institute for Advanced Studies, Bangalore. She received her Ph.D. in Mathematics from VIT University, Vellore in the field of Interval Type-2 Fuzzy Theory. She has qualified NET for lectureship by CSIR-UGC. She holds a Master’s degree and bachelor’s degree in Mathematics from Mahatma Gandhi University, Kerala. She has more than 15 years of experience in academia and research. She has published more than 15 research papers in Scopus indexed journals and presented papers in many international conferences. Her research interests include fuzzy theory, machine learning, soft computing and artificial intelligence.

N. Thillaiarasu, School of Computing and Information Technology, REVA University, Bengaluru, India

N. Thillaiarasu currently working as an Associate Professor in the School of Computing and Information Technology, REVA University, Bengaluru, He has also served as an Assistant Professor at Galgotias University, Greater Noida from July 2019 to December 2020. He worked 7.3 Years as an Assistant Professor in the Department of Computer Science and Engineering, SNS College of Engineering, Coimbatore. Obtained his B.E., in Computer Science and Engineering from Selvam College of Technology in 2010 and received his M.E., in Software Engineering from Anna University Regional Center, Coimbatore in 2012. He received his Ph.D., Degree from Anna University, Chennai in 2019, he has published more than 22 research papers in refereed, Springer, and IEEE Xplore conferences. he has organized several workshops, summer internships, and expert lectures for students. He has worked as a session chair, conference steering committee member, editorial board member, and reviewer in Springer Journal and IEEE Conferences. he is an Editor board Member of editing books titled “Machine Learning Methods for Engineering Application Development” Bentham Science. He is also working as editor for the title, “Cyber Security for Modern Engineering Operations Management: Towards Intelligent Industry”, Design Principle, Modernization and Techniques in Artificial Intelligence for IoT: Advance Technologies, Developments, and Challenges” CRC Press Tylor and Francis, His area of interest includes Cloud Computing, Security, IoT, and Machine Learning.


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Computer Vision and its Application in Agriculture