Implementation of Deep CNN Model for the Detection of Plant Leaf Disease
Article Sidebar
Main Article Content
Abstract
The potato is the most important tuber crop in the world, and it is grown in about 125 different nations. Potato is the crop that is most commonly consumed by a billion people worldwide, virtually every day, behind rice and wheat. However, a number of bacterial and fungal diseases are causing the potato crop's quality and yield to decline. Potato Leaf diseases must be promptly identified and prevented to increase production. Various researchers look for solutions to protect plants instead of traditional processes which take more time. Recent technological developments have thrown up many alternates to traditional methods which are labour intensive. The application of AlexNet model Deep Convolutional Neural Network(CNN) to recognise diseases in potato plants avoids the disadvantages of selecting disease spot features artificially and makes more objective the plant disease feature extraction. It improves research efficiency and speeds up technology transformation. Accuracies ranging from 85% - to 95% were obtained using AlexNet model Deep.
Article Details
References
ShaziaSulthana, ByppanahalliNarayana Reddy Manjunatha Reddy, “Machine learning algorithms for privacy preserving in vehicular ad hoc network”, Indonesian Journal of Electrical Engineering and Computer Science ,vol. 30, no. 2, pp. 1021-1028,2023.
Ramapraba, P.S., Epsiba, P., Umapathy, K., Sivanantham, E.(2023), " Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis", Intelligent Automation and Soft Computing, 36(2), pp. 2177–2189
ChetanaRachappa , MahanteshKapanaiah , VidhyashreeNagaraju, “Hybrid ensemble learning framework for epileptic seizure detection using electroencephalograph signals”, Indonesian Journal of Electrical Engineering and Computer Science,vol. 28, no. 3, pp. 1502-1509,2022.
Ranjani, M., “A robust STATCOM controller using particle swarm optimization”, International Journal of Engineering, Transactions B: Applications, Vol.27, No(5), pp. 731–738,2014.
AissaHaidar, TarikAhajjam, ImadZeroual, YousefFarhaoui, “Application of machine learning algorithms for predicting outcomes of accident cases in Moroccan courts” ,Indonesian Journal of Electrical Engineering and Computer Science, Vol. 26, No. 2, pp. 1103-1108, 2022.
Ashok Kumar Kolluru, MalliguntaKiran Kumar, “Artificial neural network vector controlled common high-side switch asymmetric converter fed switched reluctance motor drive” , Indonesian Journal of Electrical Engineering and Computer Science,Vol. 24, No. 2, pp. 697-703,2021.
Alisha Sikri, N. P. Singh, Surjeet Dalal. (2023). Chi-Square Method of Feature Selection: Impact of Pre-Processing of Data. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 241–248. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2680
ChanidaKaewphet, NawapornWisitpongpun, “Algorithm for extracting product feature from e-commerce comment”, Indonesian Journal of Electrical Engineering and Computer Science,vol. 22, no. 2, pp. 1199-1207,2021.
Kawasaki, Y., Uga, H., Kagiwada, S,”Basic study of automated diagnosis of viral plant diseases using convolutional neural networks”, International Symposium on Visual Computing, Springer. pp. 638–645,2015.
M. Ali Fauzi, “Random Forest Approach for Sentiment Analysis in Indonesian Language,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 1, pp. 46-50,2018.
Lavanya Dhanesh,, S.Deepa, (2020), “Enhanced and Energy-Efficient Program Scheduling for Heterogeneous Multi-Core Processors System”, Lecture Notes in Electrical Engineering(Advances in Electrical Control and Signal Systems) , 665, pp. 737-747
C. Shang and F. You, “Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era,” Engineering, vol. 5, no. 6, pp. 1010–1016, 2019.
Pic´o, B., D´?ez, M.J., Nuez, F.,”Viral diseases causing the greatest economic losses to the tomato crop”, ScientiaHorticulturaevol67, pp.151–196,1996.
Prof. Nitin Sherje. (2017). Phase Shifters with Tunable Reflective Method Using Inductive Coupled Lines. International Journal of New Practices in Management and Engineering, 6(01), 08 - 13. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/50
Rangarajan, A.K., Purushothaman, R, Tomato crop disease classification using pre-trained deep learning algorithm, Procedia computer science, vol133, pp.1040–1047,2018.
Manikandan,J., Sivanesan, Gunasekaran, Ramapraba, P.S., "Machine Learning and Wireless Sensor Network based Library Automation System",3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022 - Proceedings, 2022
R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, “Deep Learning Approach for Intelligent Intrusion Detection System,” IEEE Access, vol. 7, pp. 41525–41550, 2019.
S. Jayaprakash, , B. Gopi , Murugananth Gopal Raj , S. Sujith , S. Deepa and S. Swapna,(2022), "Development of Energy Management System for Micro Grid Operation", computer systems science and Engineering, Vol.45, issue no.3, pp.2537-2551.
J. K. Alwan, D. S. Jaafar, and I. R. Ali, “Diabetes diagnosis system using modified Naive Bayes classifier,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 28, no. 3, pp. 1766–1774, Dec. 2022.
Moore, B., Clark, R., Muñoz, S., Rodríguez, D., & López, L. Automated Grading Systems in Engineering Education: A Machine Learning Approach. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/125
H. N. Mhaskar, S. V. Pereverzyev, and M. D. van der Walt, “A deep learning approach to diabetic blood glucose prediction,” Frontiers in Applied Mathematics and Statistics, vol. 3, Jul. 2017.
] C. S. Yu, Y. J. Lin, C. H. Lin, S. Y. Lin, J. L. Wu, and S. S. Chang, “Development of an online health care assessment for preventive medicine: A machine learning approach,” Journal of Medical Internet Research, vol. 22, no. 6, Jun. 2020.
J. K. Alwan, D. S. Jaafar, and I. R. Ali, “Diabetes diagnosis system using modified Naive Bayes classifier,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 28, no. 3, pp. 1766–1774, Dec. 2022.
D. Rethage, J. Wald, J. Sturm, N. Navab, and T. Tombari, “Fully-convolutional point networks for large-scale point clouds,” Proceedings of the European Conference on Computer Vision (ECCV), 2018.
S. Deepa, Lavanya Dhanesh, Danusha V, Divya Dath K, Pavadhaarini G K and Shobana Sri C,(2021), “ Dementia People Tracking System”,Advances in Parellel computing(Recent trends in intensive computing), pp.744-748
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” International Conference on Machine Learning, 2015, pp. 448-456.
Renato Costa, Deep Reinforcement Learning for Autonomous Robotics , Machine Learning Applications Conference Proceedings, Vol 2 2022.
S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between capsules,” Advances in Neural Information Processing Systems, 2017.
Anitha, R., Sundaramoorthy, K., Suseela, D., ...Selvi, S., Aljanabi, M.(2023), "Naïve Bayesian Classification Based Glioma Brain Tumor Segmentation Using Grey Level Co-occurrence Matrix Method",International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 11(4), pp. 203–208