Deep Learning-Based Approach to Identify the Potato Leaf Disease and Help in Mitigation Using IOT
Abstract
The major reason for minimizing crop productivity is various diseases in plants. To eliminate the disease-induced losses in plants during growth as well as to increase crop productivity, former disease detection and prevention on the crop are the most challenging factors. Thus, it is a suitable decision that can be taken by the farmers or villagers to avoid further losses. The automated detection of crop disease with images has been done using many classification techniques, such as k-Nearest Neighbor Classifier, Probabilistic Neural Network, Genetic Algorithm, Support Vector Machine, Main Component Analysis, Artificial Neural Network, and Fuzzy Logic. The works on the technique of deep learning which identifies the various diseases in plants. Here, we use an efficient convolution neural network algorithm (CNN) algorithm which can detect the type of diseases in leaves. Our proposed paper includes implementation steps such as datasets gathering, training, testing, classification, and using CNN to classify the leaves which are diseased or healthy based on data. This work identifies the potato leaf disease and mitigation using alerts with IoT and CNN. This work identifies the potato leaf disease using the KNN and CNN methods, and the results are compared in this paper. The developed scheme also provides help in mitigation using email for the solution of disease using IoT. An accuracy of ~ 90% has been achieved using CNN-based classification.
References
[1]
Bashir S and Sharma N Remote area plant disease detection using image processing IOSR J Electron Commun Eng. 2012 2 6 31-34
[2]
Kosamkar PK, Kulkarni VY, Mantri K, Rudrawar S, Salmpuria S, and Gadekar N Leaf disease detection and recommendation of pesticides using convolution neural network IEEE Fourth International Conference on Computing Communication Control and automation (ICCUBEA) 2018 New Jersey USA IEEE 978-981
[3]
Pinto LS, Argha Ray M, Reddy U, Perumal P, and Aishwarya P Crop disease classification using texture analysis IEEE international conference on recent trends in electronics information communication technology 2016 New Jersey USA IEEE 825-828
[4]
Santhosh Kumar, BK Raghavendra (2019) “Diseases Detection of Various Plant Leaf Using Image Processing Techniques: A Review”. 5th International Conference on Advanced Computing & Communication Systems. In IEEE. 978–1.
[5]
Vijai S et al. Detection of plant leaf diseases using image segmentation and soft computing techniques, information processing in agriculture Elsevier 2017 4 1 41-49
[6]
Godliver O, et al. “Machine learning for plant disease incidence and severity measurements from leaf images.” In 15th IEEE international conference on machine learning and applications. 2016.
[7]
Yuxia U et al. Spatial pyramid-oriented encoder- decoder cascade convolution neural network for crop disease leaf segmentation 2021 New Jersey, USA IEEE
[8]
Suhaili BK Classification of watermelon leaf diseases using neural network analysis 2013 Jersey USA IEEE business engineering and industrial applications colloquium. IEEE. New 459-464
[9]
Sachin P, et al. 2015 “Plant disease detection using image processing.” In IEEE 2015 international conference on computing communication control and automation. Pune.
[10]
Athanikar Girish, Priti Badar “Potato Leaf Diseases Detection and Classification System Mr.” At semantic scholar 2016
[11]
Kumari CU, Jeevan Prasad S, Mounika G 2016 “Leaf Disease Detection: Feature Extraction with K-means clustering and Classification with ANN.” 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). Erode, India. 1095–1098.
[12]
Islam M, Anh Dinh, Wahid K, Bhowmik P, “Detection of potato diseases using image segmentation and multiclass support vector machine,” 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE). Windsor. ON. 2017
[13]
Guanlin Li, Zhanhong Ma, and Haiguang W Image recognition of grape downy mildew and grape powdery mildew based on support vector machine IFIP Advanc Inform Commun Technol 2012 370 151-162
[14]
Jing C, Qi L, and Lingwang G Visual tea leaf disease recognition using a convolutional neural network model Symmetry 2019 11 343
[15]
Divyansh Tiwari [2020] 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, 2020.
[16]
A Survey on Disease Detection of a potato Leaf Using CNN, SindhujaBangari Department of Computer Science and Engineering New Horizon College of Engineering. IEEE. Bangalore, India. 23–25. 2022
[17]
A Review of Different Plant Leaf Diseases and an Analysis of Different Plant Leaf Diseases Identification Techniques Research and Reviews Journal of Agriculture and Allied Sciences, RRJAAS | Volume 10| Issue 4 | December, year 2021
[18]
Hemant Kumar Gupta et al 2022 Potato Plant Disease Detection Using Convolution Neural Network NEURO-QUANTOLOGY | OCTOBER 2022 | VOLUME 20 |ISSUE 11| PAGE 7244–7250| / year 2022
Recommendations
Deep Learning Algorithm for Classifying Dilated Cardiomyopathy and Hypertrophic Cardiomyopathy in Transport Workers
Internet of Things, Smart Spaces, and Next Generation Networks and SystemsAbstractAutomatic classification of the different types of cardiomyopathies is desirable, but has been done less with a convolutional neural network (CNN). The aim of this study was to evaluate currently available CNN models for the classification of cine ...
Classification of Weeds Using Neural Network Algorithms and Image Classifiers
Intelligent Human Computer InteractionAbstractIn agriculture it is very important to differentiate between crop seedling and weed from the farms, the traditional means to classify were mostly relies on manual methods which is not time and cost efficient. Automated Deep learning-based Weed ...
Deep learning model for detection of brown spot rice leaf disease with smart agriculture
AbstractGiven that it provides nourishment for more than half of humanity, rice is regarded as one of the most significant plants in the world in agriculture. The quantity and quality of the product may be impacted by diseases that can damage rice plants ...
Graphical abstractProposed CNN-VGG19 model for detection of rice leaf disease.
Display Omitted
Highlights- The CNN-VGG19 model is developed for the recognition of brown spot rice leaf diseases.
- The CNN-VGG19 is based on transfer learning for the precise identification of the brown spot leaf disease.
- The highest accuracy, precision, and ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 15 April 2023
Accepted: 25 February 2023
Received: 29 January 2023
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Dec 2024