Potato Leaf Disease Detection Using Convolution Neural Network Model
Keywords:
Leaf Disease, CNN, Potato, Accuracy, Agriculture, Deep Learning.Abstract
Potatoes, being one of the most widely consumed vegetables globally, have increasingly become a focus for agricultural departments worldwide. However, despite their popularity, potato leaf diseases pose a significant threat to potato crops. A range of diseases, including early blight, late blight, and Septoria blight, can affect potato plants, manifesting symptoms in their leaves. Detecting and addressing these outbreaks early on is crucial to prevent major economic losses for farmers.
In this research paper, we propose a model that utilizes image processing techniques to identify and detect diseases in potato leaves. Our approach relies on a Convolution Neural Network (CNN), chosen for its effectiveness in image classification tasks. By leveraging CNN technology, we aim to provide accurate and efficient detection of potato leaf diseases, thereby enabling timely intervention and mitigation measures
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Soma Ghosh, Renu Rameshan and and Dileep A. D. “An Empirical Study on Machine Learning Models for Potato Leaf Disease Classification using RGB Images”, ICPRAM 2021 - 10th International Conference on Pattern Recognition Applications and Methods.
Divyansh Tiwari, Mritunjay Ashish etl.” Potato Leaf Diseases Detection Using Deep Learning”, Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS 2020) IEEE Xplore Part Number:CFP20K74-ART; ISBN: 978-1-7281- 4876-2
Sumit Kumar et., Plant Disease Detection Using CNN, Turkish Journal of Computer and Mathematics Education Vol.12 No.12 (2021), 2106-2112
PrakanshuShrivastav , Vaibhav Avsthietl, “Plant Disease Detection using Convolutional Neural Network”, Internation Journal of Advanced Research(IJAR) January 2021
Garima Shrestha, Deepsikha etl. “Plant Disease Detection using CNN”, Proceedings of 2020 IEEE Applied Signal Processing Conference (ASPCON), ISBN: 978-1-7281-6882-1
Gokaraju Rangaraju, VNR Vignana Jyothi, Plant Disease Detection Using Convolutional Neural Networks , International Journal of Advanced Trends in Computer Science and Engineering, ISSN 2278-3091, June 2020
S k Mahmudul Hassan , Arnab Kumar Maji etl. “Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach”, Electronics 2021, 10, 1388. https://doi.org/10.3390/electronics10121388, June 2021
Garlapati Pranay, Aditya Kurubaetl. Detection of Disease and Damage Control for Crops using Convolution Neural Networks, Journal of Xi'an University of Architecture & Technology, ISSN No : 1006-7930, Volume XII, Issue XI, 2020
Sakshi Mangal and Pratiksha Meshram, PLANT DISEASE IDENTIFICATION USING DEEP LEARNING CLASSIFICATION MODEL: CNN, Journal of University of Shanghai for Science and Technology, ISSN: 1007-6735, Volume 23, Issue 1, January – 2021
Varsha P. Gaikwad and Dr. Vijaya Musande,Wheat Disease Detection using Image Processing, 2017 1st International Conference on Intelligent Systems and Information Management(ICSIM),978-1-5090-4264-7/17/$31.00©2017IEEE, 10.1109/ICISIM.2017.8122158
Shrad P. Mohanty, David P. Hughes and Marcel Salathe, Sept 2016 ”Using Deep Learning for Image-Based Plant Disease Detection” Front. Plant Sci 7:1419 doi: 10.3389/fpls.2016.01419, Volume 7.
Varsha P. Gaikwad, Vijaya Musande, 2020, “Plant leaf damage detection based in colour and texture using deep learning – ALEXNET”, Solid State Technology Volume: 63 Issue: 2 Publication
Manya Afonso, Pieter etl. Blackleg Detection in Potato Plants using Convolutional Neural Networks, https://doi.org/10.1016/j.ifacol.2019.12.481
Soma Ghosh, Renu Rameshan and Dileep A. D ,An Empirical Study on Machine Learning Models for Potato LeafDisease Classification using RGB Images, , ICPRAM 2021 - 10th International Conference on Pattern Recognition Applications and Methods.
Islam, M.; Dinh, A.; Wahid, K.; Bhowmik, P. Detection of potato diseases using image segmentation and multiclass support vector machine. In Proceedings of the 2017 IEEE 30th canadian conference on electrical and computer engineering (CCECE), Windsor, ON, Canada, 30 April–3 May 2017; pp. 1–4.
Hu YH, Ping XW, Xu MZ, Shan WX, He Y, “Detection of Late Blight Disease on Potato Leaves Using Hyperspectral Imaging Technique,” PubMed, 36(2), 2016, pp. 515-519.
Xiaofei, ,Xutao li, Y.K. Lau, Xiaofeng Zhang, “Hyperspectral Image Classification With Deep Learning Models” IEEE(2018), 1-16.
Asif, M.K.R.; Rahman, M.A.; Hena, M.H. CNN based disease detection approach on potato leaves. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2020, pp. 428–432
Rashid, J.; Khan, I.; Ali, G.; Almotiri, S.H.; AlGhamdi, M.A.; Masood, K. Multi-level deep learning model for potato leaf disease recognition. Electronics 2021, 10, 2064.
Singh, S.; Agrawal, V. Corrigendum: Development of ecosystem for effective supply chains in 3D printing industry–an ISM approach (2021 IOP Conf. Ser.: Mater Sci Eng. 1136 012049). IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2021, Vol. 1136, p. 012078.
Sholihati, R.A.; Sulistijono, I.A.; Risnumawan, A.; Kusumawati, E. Potato leaf disease classification using deep learning approach. 2020 international electronics symposium (IES). IEEE, 2020, pp. 392–397.
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