Developing of CNN Model for Disease Detection on Cassava leaves using VGG-16 Algorithm
Keywords:
Cassava Leaves, Disease Detection, Identification, Performance, AccuracyAbstract
Sub-Saharan Africa is home to many important crops, one of which being cassava. For many people, it is their staple meal. Although cassava leaves are full of health advantages, the illnesses that have been affecting it, have caused a significant reduction in productivity. The lab testing may need more time and resources from cultivators than they have. In order to meet these challenges, farmers therefore require a fast and efficient problem identification approach. In an effort to maximize model performance, the offered deep learning model utilizes the advantages of the EfficientNet-B0 architecture, which has been enhanced with k-fold cross-validation. The primary objective of the research is to use picture classification to precisely detect the illnesses that specifically affect cassava plants via deep learning. Early intervention steps, such as the targeted use of pesticides or the quarantine of infected crops, may be made feasible by this identification. Every one of testing and training image originates from a natural environment in a farming region. To determine the model's authentic outcomes, it has been validated by employing a particular set of data. To sum up, this study promotes the practices of agriculture and food security by utilizing deep learning techniques to fight Cassava infections. The resilience of the cassava crop may be substantially improved through the establishment of an accurate disease identification and prevention model, which will eventually enhance food production and the daily lives of those who depend on this important commodity.
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References
Seksan Mathulaprangsan, Kitsana Lanthong. "Cassava Leaf Disease Recognition Using Convolutional Neural Networks", 2021 9th International Conference on Orange Technology (ICOT), 2021
Moinuddin Ahmed Shaik, M. V. Rama Sundari, Jyothi Yadla, V Priyadarshini, V. Narasimha, H. Manoj T. Gadiyar. "Optimizing Diabetes Prediction with Ensemble Learning with Voting and Cross-Validation: A Comprehensive Approach", 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), 2023.
V. Y, N. Billakanti, K. Veeravalli, A. D. R. N and L. Kota, "Early Detection of Casava Plant Leaf Diseases using EfficientNet-B0," 2022 IEEE Delhi Section Conference (DELCON), New Delhi, India, 2022
Charles Oluwaseun Adetunji, Muhammad Akram, Areeba Imtiaz, Ehis-Eriakha Chioma Bertha et al. "Chapter 8 Modified Cassava: The Last Hope That Could Help to Feed the World—Recent Advances", Springer Science and Business Media LLC, 2021
J. C. Lozano. "Diseases of Cassava (Manihot esculenta Crantz)", International Journal of Pest Management, 03/01/1974
C.N. Fokunang., T. Ikotun., A.G.O. Dixon., C.N Akem., E.A.Tembe., E.N. Nukenine.. "Efficacy of Antimicrobial Plant Crude Extracts on the Growth of Colletotrichum gloeosporioides f. sp. manihotis", Pakistan Journal of Biological Sciences, 2000
Emily J McCallum, Ravi B Anjanappa, Wilhelm Gruissem. "Tackling agriculturally relevant diseases in the staple crop cassava (Manihot esculenta)", Current Opinion in Plant Biology, 2017
Noor Ilanie Nordin, Wan Azani Mustafa, Muhamad Safiih Lola, Elissa Nadia Madi et al. "Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model", Bioengineering, 2023
Vinayakumar Ravi, Vasundhara Acharya, Tuan D. Pham. "Attention deep learning‐based large‐scale learning classifier for Cassava leaf disease classification", Expert Systems, 2021
M. K. Dharani, R. Thamilselvan, Smita P. Gudadhe, Manasi Arvindrao Joshi, Vipul Yadav. "Leaf Disease Detection using Deep Learning Models", 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), 2022
Konrad Banaś, Agnieszka Osiecka, Tomasz Lenartowicz, Agnieszka Łacka, Henryk Bujak, Marcin Przystalski."Assessment of Early, Mid-Early, and Mid-Late Soybean (Glycine max) Varieties in Northern Poland", Agronomy, 2023
Umesh Kumar Lilhore, Agbotiname Lucky Imoize, Cheng-Chi Lee, Sarita Simaiya et al. "Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification", Mathematics, 2022
Yiwei Zhong, Baojin Huang, Chaowei Tang. "Classification of Cassava Leaf Disease Based on a Non-Balanced Dataset Using Transformer-Embedded ResNet", Agriculture, 2022
R. Singh, A. Sharma, N. Sharma and R. Gupta, "Automatic Detection of Cassava Leaf Disease using Transfer Learning Model," 2022 6th International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, 2022, pp. 1135-1142, doi: 10.1109/ICECA55336.2022.10009338.
F. Gao, J. Sa, Z. Wang and Z. Zhao, "Cassava Disease Detection Method Based on EfficientNet," 2021 7th International Conference on Systems and Informatics (ICSAI), Chongqing, China, 2021, pp. 1-6, doi: 10.1109/ICSAI53574.2021.9664101.
Dharitri Tripathy, Rudrarajsinh Gohil, Talal Halabi. "Detecting SQL Injection Attacks in Cloud SaaS using Machine Learning", 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2020
"Cybersecurity and Secure Information Systems", Springer Science and Business Media LLC, 2019
M. K. Dharani, R. Thamilselvan, S. P. Gudadhe, M. A. Joshi and V. Yadav, "Leaf Disease Detection using Deep Learning Models," 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), Tashkent, Uzbekistan, 2022, pp. 646-650, doi: 10.1109/ICTACS56270.2022.9988660.
Ziyu Xu, Tianhe Gao, Zengcong Li, Qingjie Bi, Xiongwei Liu, Kuo Tian. "Digital Twin Modeling Method for Hierarchical Stiffened Plate Based on Transfer Learning", Aerospace, 2023.
Saini, K. Guleria and S. Sharma, "Cassava Leaf Disease Classification Using Pre-Trained EfficientN Et Model," 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2023, pp.675-680,doi: 10.1109/ICSSAS57918.2023.10331697.
Huy-Tan Thai, Nhu-Y Tran-Van, Kim-Hung Le. "Artificial Cognition for Early Leaf Disease Detection using Vision Transformers", 2021 International Conference on Advanced Technologies for Communications (ATC), 2021
Anand Shanker Tewari, Priya Kumari. "Lightweight modified attention based deep learning model for cassava leaf diseases classification", Multimedia Tools and Applications, 2023.
Shiva Mehta, Vinay Kukreja, Richa Gupta. "Decentralized Detection of Cassava Leaf Diseases: A Federated Convolutional Neural Network Solution", 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT), 2023
A. John, "Identification of Diseases in Cassava Leaves using Convolutional Neural Network," 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT), Sonepat, India, 2022, pp. 1-6, doi: 10.1109/CCiCT56684.2022.00013.
S. Mathulaprangsan and K. Lanthong, "Cassava Leaf Disease Recognition Using Convolutional Neural Networks," 2021 9th International Conference on Orange Technology (ICOT), Tainan, Taiwan, 2021, pp. 1-5, doi: 10.1109/ICOT54518.2021.9680655.
Olorunjube James Falana, Adesina Simon Sodiya, Saidat Adebukola Onashoga, Biodun Surajudeen Badmus. "Mal-Detect: An intelligent visualization approach for malware detection", Journal of King Saud University - Computer and Information Sciences, 2022
T. Vijaykanth Reddy, Sashi Rekha K. "Plant Disease Detection Using Advanced Convolutional Neural Networks with Region of Interest Awareness", Research Square Platform LLC, 2022
Alene, D. (2013). Economic impacts of cassava research and extension in Malawi and Zambia. Journal development. Agriculture. Economics, 5(11), 457–469.
H. Zhang, Y. Xu and J. Sun, "Detection of Cassava Leaf Diseases Using Self-Supervised Learning," 2021 2nd International Conference on Computer Science and Management Technology (ICCSMT), Shanghai, China, 2021, pp. 120-123, doi: 10.1109/ICCSMT54525.2021.00032.
R. Yadav, M. Pandey and S. K. Sahu, "Cassava plant disease detection with imbalanced dataset using transfer learning," 2022 IEEE World (AIC), Sonbhadra, India, 2022, pp. 220-225, doi: 10.1109/AIC55036.2022.9848882.
Yuanbo Ye, Houkui Zhou, Huimin Yu, Roland Hu, Guangqun Zhang, Junguo Hu, Tao He. "An Improved EfficientNetV2 Model Based on Visual Attention Mechanism: Application to Identification of Cassava Disease", Computational Intelligence and Neuroscience, 2022
"Biometric Recognition", Springer Science and Business Media LLC, 2017
Huy-Tan Thai, Kim-Hung Le, Ngan Luu-Thuy Nguyen. "Towards sustainable agriculture: A lightweight hybrid model and cloud-based collection of datasets for efficient leaf disease detection", Future Generation Computer Systems, 2023
M. K. Dharani, D. R. Thamilselvan, D. R. Rajdevi, M. K. Logeshwaran, A. J and D. R. S, "Analysis on Cassava leaf disease prediction using pre-trained models," 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2022, pp. 1-6, doi: 10.1109/ICCCNT54827.2022.9984351.
Methil, H. Agrawal and V. Kaushik, "One-vs-All Methodology based Cassava Leaf Disease Detection," 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2021,
Congyu Zou, Mikhael Djajapermana, Eimo Martens, Alexander Müller et al. "DWTCNNTRN: a Convolutional Transformer for ECG Classification with Discrete Wavelet Transform", 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023
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