Abstract
Potatoes are a widely recognized vegetable that has been cultivated in India for several decades. However, diseases like early blight and late blight have negatively impacted potato production, increasing production costs. We intend to create an automated and effective disease diagnosis mechanism utilizing potato leaf images to overcome this issue and digitize the system. Our primary goal is to employ a convolutional neural network (CNN) algorithm for diagnosing potato diseases. This research paper proposes a machine learning and image processing-based automated system for detecting and classifying potato leaf diseases. Image processing techniques provide an excellent approach for disease detection and analysis. In this analysis, we divide the pictures into categories: healthy and unhealthy potato leaves. We have collected over 2152 pictures from various sources, including Kaggle, and utilized pre-trained models for accurate recognition and classification of healthy and diseased leaves. The program achieves an impressive accuracy of 99.13%. By making use of advanced technology and machine learning algorithms, we can significantly improve potato production and combat the negative impact of diseases. In the end, this method increases productivity and yields in the potato farming sector by providing a scalable and effective means of detecting diseases in potato crops.
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References
Yahya A, Marriam N, Tahira N, Ali J, Fathe J, Ali T (2023) An improved deep learning approach for localization and recognition of plant leaf diseases. Expert Syst Appl 230:120717. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2023.120717
Jayashree LS, Rajathi N, Thirumal A (2016) Precision agriculture: on the accuracy of multilevel and clustered ANFIS models for sugarcane yield categorization. In: 2016 IEEE region 10 conference (TENCON), Singapore, pp 1983–1987. https://doi.org/10.1109/TENCON.2016.7848371
Waghmare H et al (2016) Detection and classification of diseases of Grape plant using opposite colour local binary pattern feature and machine learning for automated decision support system. In: 2016 3rd international conference on signal processing and integrated networks (SPIN), pp 513–518
Ramesh S et al (2018) Plant disease detection using machine learning. In: 2018 international conference on design innovations for 3Cs compute communicate control (ICDI3C), Bangalore, India, pp 41–45. https://doi.org/10.1109/ICDI3C.2018.00017
Shruthi U, Nagaveni V, Raghavendra BK (2019) A review on machine learning classification techniques for plant disease detection. In: 2019 5th international conference on advanced computing & communication systems (ICACCS), Coimbatore, India, pp 281–284. https://doi.org/10.1109/ICACCS.2019.8728415
Kaur R, Kang SS (2015) An enhancement in classifier support vector machine to improve plant disease detection. In: 2015 IEEE 3rd international conference on MOOCs, innovation and technology in education (MITE), Amritsar, India, pp 135–140. https://doi.org/10.1109/MITE.2015.7375303
Pooja V, Das R, Kanchana V (2017) Identification of plant leaf diseases using image processing techniques. In: 2017 IEEE technological innovations in ICT for agriculture and rural development (TIAR), Chennai, India, pp 130–133. https://doi.org/10.1109/TIAR.2017.8273700
Prajapati HB, Shah JP, Dabhi VK (2017) Detection and classification of rice plant diseases. Intell Decis Technol 11(3):357–373
Akhtar AK, Khan SA, Shaukat A (2013) Automated plant disease analysis (APDA): performance comparison of machine learning techniques. In: 2013 11th international conference on frontiers of information technology, FIT, pp 60–65
Reza ZN, Nuzhat F, Mahsa NA, Ali MH (2016) Detecting jute plant disease using image processing and machine learning. In: 2016 3rd international conference on electrical engineering
Kumar S, Kaur R (2015) Plant disease detection using image processing—a review. Int J Comput Appl 124(6):9. https://doi.org/10.5120/ijca2015905789
Das A, Mallick C, Dutta S (2020) Deep learning-based automated feature engineering for rice leaf disease prediction. In: Das A, Nayak J, Naik B, Dutta S, Pelusi D (eds) Computational intelligence in pattern recognition. advances in intelligent systems and computing, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-2449-3_11
Al-Adhaileh MH, Verma A, Aldhyani THH, Koundal D (2023) Potato blight detection using fine-tuned CNN architecture. Mathematics 11:1516. https://doi.org/10.3390/math11061516
Jung M, Song JS, Shin AY et al (2023) Construction of deep learning-based disease detection model in plants. Sci Rep 13:7331. https://doi.org/10.1038/s41598-023-34549-2
Alzubaidi L, Zhang J, Humaidi AJ et al (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8:53. https://doi.org/10.1186/s40537-021-00444-8
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Goon, S., Chakraborty, R., Dalui, I., Obaid, A.J. (2024). Detecting Common Diseases of Potato Leaf Applying Deep Learning Techniques. In: Bhattacharya, A., Dutta, S., Dutta, P., Samanta, D. (eds) Innovations in Data Analytics. ICIDA 2023. Lecture Notes in Networks and Systems, vol 1005. Springer, Singapore. https://doi.org/10.1007/978-981-97-4928-7_35
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