Numerical Modeling and Design of Machine Learning Based Paddy Leaf Disease Detection System for Agricultural Applications
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Abstract
In order to satisfy the insatiable need for ever more bountiful harvests on the global market, the majority of countries deploy cutting-edge technologies to increase agricultural output. Only the most cutting-edge technologies can ensure an appropriate pace of food production. Abiotic stress factors that can affect plants at any stage of development include insects, diseases, drought, nutrient deficiencies, and weeds. On the amount and quality of agricultural production, this has a minimal effect. Identification of plant diseases is therefore essential but challenging and complicated. Paddy leaves must thus be closely watched in order to assess their health and look for disease symptoms. The productivity and production of the post-harvest period are significantly impacted by these illnesses. To gauge the severity of plant disease in the past, only visual examination (bare eye observation) methods have been employed. The skill of the analyst doing this analysis is essential to the caliber of the outcomes. Due to the large growing area and need for ongoing human monitoring, visual crop inspection takes a long time. Therefore, a system is required to replace human inspection. In order to identify the kind and severity of plant disease, image processing techniques are used in agriculture. This dissertation goes into great length regarding the many ailments that may be detected in rice fields using image processing. Identification and classification of the four rice plant diseases bacterial blight, sheath rot, blast, and brown spot are important to enhance yield. The other communicable diseases, such as stem rot, leaf scald, red stripe, and false smut, are not discussed in this paper. Despite the increased accuracy they offer, the categorization and optimization strategies utilized in this work lead it to take longer than typical to finish. It was evident that employing SVM techniques enabled superior performance results, but at a cost of substantial effort. K-means clustering is used in this paper segmentation process, which makes figuring out the cluster size, or K-value, more challenging. This clustering method operates best when used with images that are comparable in size and brightness. However, when the images have complicated sizes and intensity values, clustering is not particularly effective.
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References
Sun, Y, Jiang, Z, Zhang, L, Dong, W & Rao, Y 2019, ‘SLIC_SVM based leaf diseases saliency map extraction of tea plant’, Computers and Electronics in Agriculture, vol. 157, pp. 102-109.
Tian, K, Li, J, Zeng, J, Evans, A & Zhang, L 2019, ‘Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm’, Computers and Electronics in Agriculture, vol. 165, pp. 1-7.
Hernández-Rabadán, DL, Ramos-Quintana, F & Guerrero Juk, J 2014, ‘ Integrating SOMs and a Bayesian classifier for segmenting diseased plants in uncontrolled environments’, The Scientific World Journal, pp.1-13.
Shinde, RC, Mathew, CJ & Patil, CY 2015, Segmentation technique for soybean leaves disease detection’, International Journal of Advanced Research (IJAR), vol. 3, no. 5, pp.522-528.
Gui, J, Hao, L, Zhang, Q & Bao, X 2015, ‘A new method for soybean leaf disease detection based on modified salient regions’, International Journal of Multimedia and Ubiquitous Engineering, vol. 10, no. 6, pp. 45-52.
Xu, L, Xu, X, Hu, M, Wang, R, Xie, C & Chen, H 2015, ‘Corn leaf disease identification based on multiple classifiers fusion’, Transactions of the Chinese Society of Agricultural Engineering, vol. 31, no. 14, pp. 194-201.
Lin, Z, Mu, S, Shi, A, Pang, C & Sun, X 2018, ‘A novel method of maize leaf disease image identification based on a multichannel convolutional neural network’, Transactions of the ASABE, vol. 61, no. 5, pp. 1461-1474.
Wahab, AHBA, Zahari, R & Lim, TH 2019, ‘Detecting diseases in Chilli Plants Using K-Means Segmented Support Vector Machine’, 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC), pp. 57-61.
Pinki, FT, Khatun, N & Islam, SM 2017, ‘Content based paddy leaf disease recognition and remedy prediction using support vector machine’, 20th International Conference of Computer and Information Technology (ICCIT), pp. 1-5.
Oo, YM & Htun, NC 2018, ‘Plant leaf disease detection and classification using image processing’, International Journal of Research and Engineering, vol. 5, no. 9, pp. 516-523.
Karmokar, BC, Ullah, MS, Siddiquee, MK & Alam, KMR 2015, ‘Tea leaf diseases recognition using neural network ensemble’, International Journal of Computer Applications, vol. 114, no. 17, pp. 27-30.
Sardogan, M, Tuncer, A & Ozen, Y 2018, ‘Plant leaf disease detection and classification based on CNN with LVQ algorithm’, 3rd International Conference on Computer Science and Engineering (UBMK), pp. 382-385.
Amara, J, Bouaziz, B & Algergawy, A 2017, ‘A deep learning-based approach for banana leaf diseases classification’, Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband, pp. 1-10.
Liu, B, Zhang, Y, He, D & Li, Y 2018, ‘Identification of apple leaf diseases based on deep convolutional neural networks’, Symmetry, vol. 10, no. 1, pp. 1-16.
Mia, MR, Roy, S, Das, SK & Rahman, MA 2020, ‘Mango leaf disease recognition using neural network and support vector machine’, Iran Journal of Computer Science, pp. 1-9.
Trang, K, TonThat, L & Thao, NGM 2020, ‘Plant Leaf Disease Identification by Deep Convolutional Autoencoder as a Feature Extraction Approach’, 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 522-526.
Chen, J, Liu, Q & Gao, L 2019, ‘Visual tea leaf disease recognition using a convolutional neural network model’, Symmetry, vol. 11, no. 3, pp. 1-13.
Xie X, Ma Y, Liu B, He J, Li S and Wang H (2020) A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks. Front. Plant Sci. 11:751. doi: 10.3389/fpls.2020.00751
Liu B, Ding Z, Tian L, He D, Li S and Wang H (2020) Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks. Front. Plant Sci. 11:1082. doi: 10.3389/fpls.2020.01082
Zhang, X, Qiao, Y, Meng, F, Fan, C & Zhang, M 2018, ‘Identification of maize leaf diseases using improved deep convolutional neural networks’, IEEE Access, vol. 6, pp. 30370-30377.
Omrani, E, Khoshnevisan, B, Shamshirband, S, Saboohi, H, Anuar, NB & Nasir, MHNM 2014, ‘Potential of radial basis function-based support vector regression for apple disease detection’, Measurement: Journal of the International Measurement Confederation, vol. 55, pp. 512-519.
Zhu, J, Wu, A, Wang, X & Zhang, H 2020, ‘Identification of grape diseases using image analysis and BP neural networks’, Multimedia tools and applications, vol. 79, no. 21, pp. 14539-14551.
Jiang, P, Chen, Y, Liu, B, He, D & Liang, C 2019, ‘Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks’, IEEE Access, vol. 7, pp. 59069-59080.
Zhang, S, Zhang, S, Zhang, C, Wang, X & Shi, Y 2019, ‘Cucumber leaf disease identification with global pooling dilated convolutional neural network’, Computers and Electronics in Agriculture, vol. 162, pp. 422-430.
Adeel, A, Khan, MA, Akram, T, Sharif, A, Yasmin, M, Saba, T & Javed, K 2020, ‘Entropy-controlled deep features selection framework for grape leaf diseases recognition’, Expert Systems, pp. 1-17.
Barbedo, JGA 2019, ‘Plant disease identification from individual lesions and spots using deep learning’, Biosystems Engineering, vol. 180, pp.96-107.
Damayanti, F, Muntasa, A, Herawati, S, Yusuf, M & Rachmad, A 2020, ‘Identification of Madura Tobacco Leaf Disease Using Gray- Level Co-Occurrence Matrix, Color Moments and Naïve Bayes’, Journal of Physics: Conference Series, pp. 1-8.
Halder, M, Sarkar, A & Bahar, H 2019, ‘Plant Disease Detection by Image Pro-cessing: A Literature Review’, Image, vol. 3, no. 6, pp. 534-538.
Islam, M, Dinh, A, Wahid, K & Bhowmik, P 2017, ‘Detection of potato diseases using image segmentation and multiclass support vector machine’, IEEE 30th Canadian conference on electrical and computer engineering (CCECE), pp. 1-4.
Manik, FY, Saputra, SK & Ginting, DSB 2020, ‘Plant Classification Based on Extraction Feature Gray Level Co-Occurrence Matrix Using k-nearest Neighbour’, Journal of Physics: Conference Series, pp. 1-9.
Wahyuni, S, Zarlis, M & Widia Sembiring, R 2020, ‘Digital Image Processing For Detecting Yellow Lines (Patch Yellow) In Palm Oil Plant Using Sobel Algorithm’, Journal of Physics: Conference Series, vol. 1471, no. 1, pp. 1-7.
Zhang, S, Huang, W & Zhang, C 2019, ‘Three-channel convolutional neural networks for vegetable leaf disease recognition’, Cognitive Systems Research, vol. 53, pp. 31-41.
Zhang, S, You, Z & Wu, X 2019, ‘Plant disease leaf image segmentation based on superpixel clustering and EM algorithm’, Neural Computing and Applications, vol. 31, no. 2, pp. 1225-1232.
Vyas, S., Mukhija, M.K., Alaria, S.K. (2023). An Efficient Approach for Plant Leaf Species Identification Based on SVM and SMO and Performance Improvement. Intelligent Systems and Applications. Lecture Notes in Electrical Engineering, vol 959. Springer, Singapore. https://doi.org/10.1007/978-981-19-6581-4_1
Koul, Deepali, and Satish Kumar Alaria. "A new palm print recognition approach by using PCA & Gabor filter.“." International Journal on Future Revolution in Computer Science & Communication Engineering 4 (2018): 38-45.
Dogiwal, Sanwta Ram, Y. S. Shishodia, Abhay Upadhyaya, Hanuman Ram, and Satish Kumar Alaria. "Image Preprocessing Methods in Image Recognition." International Journal of Computers and Distributed Systems 1, no. 3 (2012): 96-99.
Alaria, S. K. "A.. Raj, V. Sharma, and V. Kumar.“Simulation and Analysis of Hand Gesture Recognition for Indian Sign Language Using CNN”." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 4 (2022): 10-14.