Abstract
Betel vine is a tropical plant that needs care and attention. Betel leaves are highly consumed because of their medicinal value and they are the chief yields of a betel vine. It is necessary to safeguard the betel leaves from diseases to prevent heavy loss in cultivation. In this work, a smart betel vine disease detection system is developed using Predictive data mining and Image processing techniques. From the field study, images of the betel leaves are captured and they are taken for the classification tasks. Classifiers such as K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), Random Forest (RF), Neural Network (NN), Naïve Bayes (NB), and Logistic Regression (LR) are used to build an efficient classification system. Further, to enhance the performance of the classifiers, feature selection techniques are employed independently to select the significant image features. Based on the experimentation, it is found that Logistic Regression outperforms the other classifiers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Iandola FN, Moskewicz MW, Ashraf K, Han S, Dally WJ, Keutzer K (2016) SqueezeNet. arXiv
Buttrey SE, Karo C (2002) Using k-nearest-neighbor classification in the leaves of a tree. Comput Stat Data Anal 40:27–37
Hossain MS, Mou RM, Hasan MM, Chakraborty S, Abdur Razzak M (2018) Recognition and detection of tea leaf’s diseases using support vector machine. In: Proceedings-2018 IEEE 14th international colloquium on signal processing and its application, CSPA 2018, pp 150–154
Patil P, Yaligar N, Meena S (2018) Comparision of performance of classifiers-SVM, RF and ANN in potato blight disease detection using leaf images. In: 2017 IEEE International conference on computational intelligence and computing research, ICCIC 2017, pp 1–5
Do T-N (2014) Parallel multiclass stochastic gradient descent algorithms for classifying million images with very-high-dimensional signatures into thousands classes. Vietnam J Comput Sci 1:107–115
Hsu SC, Chen IC, Huang CL (2016) Image classification using pairwise local observations based Naive Bayes classifier. In: 2015 Asia-Pacific signal and information processing association annual summit and conference, APSIPA ASC 2015, pp 444–452
Das A, Dey A, Sharma M (2018) Leaf disease detection. quantification and classification using digital image processing 8:423–432
Lu KC, Yang DL (2009) Image processing and image mining using decision trees. J Inf Sci Eng 25
Sabareeswaran D, Guna Sundari R (2017) A hybrid of plant leaf disease and soil moisture prediction in agriculture using data mining techniques. 7169–7175
Pandey S, Khetwat S (2017) A survey paper on image classification and methods of image mining. Int J Comput Appl 169:10–12
Pranckevičius T, Marcinkevičius V (2017) Comparison of naive bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification. Balt J Mod Comput 5:221–232
Meena Prakash R, Saraswathy GP, Ramalakshmi G, Mangaleswari KH, Kaviya T (2018) Detection of leaf diseases and classification using digital image processing. In: Proceedings of 2017 international conference on innovations in information, embedded and communication systems, ICIIECS 2017, pp 1–4
Shijie J, Peiyi J, Siping H, Haibo Sl (2017) Automatic detection of tomato diseases and pests based on leaf images. In: Proceedings-2017 Chinese automation congress, CAC 2017, pp 2537–2510
Shukla SV, Vala JA (2016) A survey on image mining, its techniques and application. Int J Comput Appl 133, pp 12–15
Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. Conference on Advances in Signal Processing, CASP 2016:175–217
Mucherino A, Papajorgji P, Pardalos PM (2009) A survey of data mining techniques applied to agriculture, 121–140
Dey AK, Sharma M, Meshram MR (2016) Image processing based leaf rot disease, detection of betel vine (Piper BetleL.). Proc Comput Sci 748–754
Anand R, Veni S, Aravinth J (2016) An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method. In: 2016 International conference on recent trends in information technology, ICRTIT 2016, pp 1–6
Francis J, Anto Sahaya Dhas D, Anoop BK (2016) Identification of leaf diseases in pepper plants using soft computing techniques. In: Presented at the (2016), pp 168–173
Sushma B, Suraksha IS (2016) Disease prediction of paddy crops using data mining and image processing techniques. Int J Adv Res Electr Electron Instrum Eng 5
Chouhan P, Tiwari M (2015) Image retrieval using data mining and image processing techniques. IJIREEICE 3
Subramani B, Arumugam S, Ragupathy B (2019) Computer aided classification and detection of leaf disease using ANN. Int J Innov Technol Explor Eng 8
Vijayakumar J, Arumugam S (2013) Certain investigations on foot rot disease for betelvine plants using digital imaging technique. In: Proceedings-2013 international conference on emerging trends in communication, control, signal processing and computing applications, IEEE-C2SPCA 2013, pp 1–4
Jane NS, Deshmukh A, Joshi M (2014) Review of study of different diseases on betelvine plant and control measure. Int J Appl Innov Eng Manag 3:560–563
Revathi P, Hemalatha M (2014) Classification of cotton leaf spot diseases using image processing edge detection techniques. IEEE proceedings of the international conference on emerging trends in science engineering and technology: recent advancements on science and engineering innovation, INCOSET 2012:169–173
Vibhute A, Bodhe SK (2012) Applications of image processing in agriculture: a survey. Int J Comput Appl 52
Raphel MR, Prabhakaran R, Thamilarasi C (2020) Automatic leaf parameter monitoring and analysis of irrigation system in agriculture using machine learning technique. Int J Res Adv Dev
Hasan MZ, Zeba N, Malek A, Reya SS (2021) A leaf disease classification model in betel vine using machine learning techniques. In: International conference on robotics, electrical and signal processing techniques, pp 362–366
Usha Kumari C, Jeevan Prasad S, Mounika G (2019) Leaf disease detection: feature extraction with k-means clustering and classification with ANN. In: Proceedings of the 3rd international conference on computing methodologies and communication, ICCMC 2019, pp 1095–1098
Ahmed K, Shahidi TR, Irfanul Alam SM, Momen S (2019) Rice leaf disease detection using machine learning techniques. In: 2019 International conference on sustainable technologies for industry 4.0, STI 2019, pp 1–5
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Fathima, S.A., Nandhini, M. (2023). Performance Comparison of the Classifiers for Betel Vine Disease Prediction. In: Doriya, R., Soni, B., Shukla, A., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. Lecture Notes in Electrical Engineering, vol 946. Springer, Singapore. https://doi.org/10.1007/978-981-19-5868-7_18
Download citation
DOI: https://doi.org/10.1007/978-981-19-5868-7_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5867-0
Online ISBN: 978-981-19-5868-7
eBook Packages: Computer ScienceComputer Science (R0)