Paddy Plant Disease Recognition, Risk Analysis, and Classification Using Deep Convolution Neuro-Fuzzy Network
DOI:
https://doi.org/10.13052/jmm1550-4646.1829Keywords:
Multimedia, Convolution Neuro-Fuzzy, Accuracy, Disease ClassificationAbstract
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.
Downloads
References
Y. Deng, Z. Ren, Y. Kong, F. Bao and Q. Dai (2017). A hierarchical fused fuzzy deep neural network for data classification. IEEE Transactions on Fuzzy Systems, 25(4), 1006–1012.
L.S. Dutt and M. Kurian (2013). Handling of uncertainty – a survey. International Journal of Scientific and Research Publications, 3(1), 2250–3153.
Endangsuryawati, Rika Sustika, R. Sandrayuwana, Agussubekti, Hilman F. Pardede, (2018). “Deep Structured Convolutional Neural Network for Tomato Disease Detection”, Advanced Computer Science and Information Systems(ICACSIS) 2018 International Conference.
Farhana Tazmim, Nipa Khatun, S.M. Mohidul Islam, (2017). “Content based paddy leaf disease recognition and remedy prediction using support vector machine”, Computer and Information Technology(ICCIT) 2017 20th International conference.
Halildurmus, Eceolcaygunes, Murvetkirci, (2017). “Disease detection on the leaves of the tomato plants by using deep learning”, Agro-Geoinformatics 2017 6th International Conference.
Islam, Md. Ashiqul, Md. Nymur, Muhammad Shamsojjaman, Shazid Hasan, Md. Shahadat, and Tania Khatun. (2021). “An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection.” International Journal of Advanced Computer Science and Applications 12, No. 1, doi:10.14569/ijacsa.2021.0120134.
K. M. Karthick Raghunath and G. R. Anantha Raman (2020). “Neuro-Fuzzy-Based Smart Irrigation System and Multimodal Image Analysis in Static-Clustered Wireless Sensor Network for Marigold Crops.” Advances in Bioinformatics and Biomedical Engineering, pp. 237–255. doi:10.4018/978-1-7998-3591-2.ch015.
M. Kholis, Yeni Herdiyeni and Aunu Rauf. (2013). “I-PEDIA: Mobile Application for Paddy Disease Identification Using Fuzzy Entropy and Probabilistic Neural Network.” 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS) doi:10.1109/icacsis.2013.6761609.
Kouser, R. Ruhin, T. Manikandan, and V. Vinoth Kumar. (2018). “Heart Disease Prediction System Using Artificial Neural Network, Radial Basis Function and Case Based Reasoning.” Journal of Computational and Theoretical Nanoscience 15, no. 9, pp. 2810–2817. doi:10.1166/jctn.2018.7543.
A. Kumar and G.K.H. Pang (2002). Defect detection in textured materials using gabor filters. IEEE Transactions on Industry Applications, 38(2), 425–440. http://dx.doi.org/10.1109/28.993164.
Mercelin Francis, C. Deisy, (2019). “Disease Detection and Classification in Agricultural Plants using Convolutional Neural Networks – A Visual Understanding”, Signal Processing and Integrated Networks (SPIN) 2019 6th International Conference.
Monzurul Islam, Anh Dinh, Khan Wahid and Pankaj Bhowmik, (2017). “Detection of potato diseases using image segmentation and multiclass support vector machine”, 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE).
Nguyen, Tuan-Linh, Swathi Kavuri, and Minho Lee. (2019). “A Multimodal Convolutional Neuro-Fuzzy Network for Emotion Understanding of Movie Clips.” Neural Networks 118, pp. 208–219. doi:10.1016/j.neunet.2019.06.010.
P. R. Rothe and R. V. Kshirsagar. (2015). “Cotton Leaf Disease Identification Using Pattern Recognition Techniques.” 2015 International Conference on Pervasive Computing (ICPC), doi:10.1109/pervasive.2015. 7086983.
S. Poria, E. Cambria, N. Howard, G.-B. Huang and A. Hussain. (2016). Fusing audio, visual and textual clues for sentiment analysis from multimodal content. IEEE Transactions on Affective Computing, 174, 50–59.
Rajleen Kaur and Sandeep Singh Kang, (2015). “An enhancement in classifier support vector machine to improve plant disease detection”, moocs, Innovation and Technology ineducation (MITE) 2015 IEEE 3rd International Conference.
S. Ramesh and D. Vydeki (2019). “Application of machine learning in detection of blast disease in South Indian rice crops”, Journal of Phytology, pp. 31–37.
Rong Zhou, Shunichi Kaneko, Fumio Tanaka, Miyuki Kayamori, Motoshige Shimizu, (2013). “Early Detection and Continuous Quantization of Plant Disease Using Template Matching and Support Vector Machine Algorithms”, First International Symposium on Computing and Networking, doi:10.1109/candar.2013.52.
Surbhi Jain, Joydip Dhar, (2017). “Image based search engine using deep learning”, Contemporary Computing (IC3) 2017 Tenth International Conference.
Viraj A. Gulhane, Maheshkumar H. Kolekar, (2014). “Diagnosis Of Diseases On Cotton Leaves Using Principal Component Analysis Classifier”, Annual IEEE India Conference.
Yusuke Kawasaki, Hiroyuki Uga, Satoshi Kagiwada, Hitoshi Iyatomi, (2015). “Basic Study of Automated Diagnosis of Viral Plant Diseases Using Convolutional Neural Networks”, 11th International Symposium, ISVC 2015, Las Vegas, NV, USA.
Z. Guo, (2019). “Rice Carrying Capacity and Sustainable Produce of Rice in Resources-Limited Regions”, International Journal of Agricultural Science and Food Technology 5, no. 1 (July 23, 2019): pp. 054–057.