The International Arab Journal of Information Technology (IAJIT)


Crop Disease Prediction with Convolution Neural Network (CNN) Augmented With Cellular

Food security is the primary concern of any country, and crop diseases are the major threats to this. Each stage of the crop will be affected by various diseases starting from seeding to ripeness. The spread of the crop diseases is very rapid, and identification of this is challenging as the infrastructure is very less to monitor the same. After a thorough literature survey, we understood there are several ways of predicting the disease and yield prediction. We have developed two new and robust classifiers, one which processes images to predict the crop's diseases, and the second one uses the weather data to predict the same. Both classifiers use deep-learning technique Convolution Neural Networks (CNN) augmented with six neighborhood cellular automata to predict the crop disease and yield. This work will be first of its kind to develop two classifiers for six crop disease prediction. The average time to compute the yield of a particular crop is less than 0.5 nanoseconds. The first classifier is named as CNN-CA-I, which was trained/tested to process 245 different crop species and 132 diseases associated with these crops where image segmentation is done with higher accuracy, thus strengthening the disease recognition system. We gave collected public datasets of 12, 45,678 images diseases and leaves of healthy plants taken in ideal conditions. This model reports an accuracy of 92.6% on a tested standard dataset for disease and yield prediction. The second classifier is CNN-CA-W that predicts crop disease trained and tested with environment data.8,52.624 datasets are collected from ECMWF for processing the weather data to predict the crop's condition and thus reporting the yield of the crop. This model reports an accuracy of 90.1% on a tested standard dataset.

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[29] You J., Li X., Low M., Lobell D., and Ermon S., “Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data,” in Proceedings of in 31st AAAI Conference on Artificial Intelligence(IAAIC), San Francisco, pp. 4559-4565, 2017. Crop Disease Prediction with Convolution Neural Network (CNN) Augmented … 773 Pokkuluri Kiran Sree has received his B.Tech and M.E in Computer Science and Engineering from JNTU and Anna University, respectively. He has obtained his Ph.D. degree in the area of Artificial Intelligence from JNTU-Hyderabad. He has authored Six textbooks for UG and PG students of engineering in AI and published more than 96 research articles in various international journals and conferences. He has filed and published six patents in the area of Deep Learning. His biography was listed in Marquis Who’s Who in the World, 29th Edition (2012), USA. Prof Kiran is the Recipient of Bharat Excellence Award from Dr. G.V. Krishna Murthy, Former Election Commissioner of India for two times and recipient of Rashtrya Ratan Award. He was the BOS member of CSE&IT in some universities and autonomous colleges. He also worked as Principal of the N.B.K.R.Institute of Science & Technology (Second Oldest Private Engg College), Vidyanagar, for two years. He has got 18+ years of teaching experience and working as Head & Professor in the department of CSE at Shri Vishnu Engineering College for Women(A), Bhimavaram. He has delivered many technical talks on Deep Learning and AI in various International Conferences, FDP’S, Webinars. His research interests include Deep Learning, Big Data Analytics, Bioinformatics, and Cloud Computing. He is associated with various journals& conferences in various capacities as Editor in Chief, Editorial Member, and Reviewer. His the Global Vice President of WSA: World Statistical Data Analysis Research Association. SSSN Usha Devi Nedunuri has received her B.Tech degree from JNTU Hyderabad and M.Tech from JNTU Kakinada. She is pursuing her Ph.D from National Institue of Technology, Trichy in the area of Deep Learning. She has published 52 papers in various journals and conferences. She has filed a patent on Deep Learning integrated with IOT. She has acted as resource person for many AICTE sponsored FDP’S and Conferences.