Abstract
Powdery mildew is the most commonly observed disease of tomato plants, which affects its quality and productivity. On-time treatment with an optimized amount of fungicides spray can improve the yield and quality of tomato fruit and reduce the effect of powdery mildew disease. Thus, an automated spray prediction model can be developed to deal with the tomato powdery mildew disease. In the current study, three spray prediction models have been proposed using different machine learning models viz. k-Nearest Neighbor (kNN), Support Vector Machine (SVM) and Naïve Bayes (NB). This experiment was conducted using an imbalanced dataset named as tomato powdery mildew disease (TPMD) dataset. Synthetic minority over-sampling technique (SMOTE) has been used to balance the TPMD dataset. Results show that SVM based spray prediction model performed the best with 98.65% accuracy amongst all three models. An application version of spray prediction models can help the farmers for spraying fungicides in an efficient manner.
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References
Avenot HF, Michailides TJ (2010) Progress in understanding molecular mechanisms and evolution of resistance to succinate dehydrogenase inhibiting (SDHI) fungicides in phytopathogenic fungi. Crop Prot 29:643–651
Bakeer ART, Abdel-Latef MAE, Afifi MA, Barakat ME (2013) Validation of tomato powdery mildew forecasting model using meteorological data in Egypt. Int J Agric Sci 5:372
Bhatia A, Chug A, Singh AP (2020a) Plant disease detection for high dimensional imbalanced dataset using an enhanced decision tree approach. Int J Future Gener Commun Netw 13:71–78
Bhatia A, Chug A, Prakash Singh A (2020) Application of extreme learning machine in plant disease prediction for highly imbalanced dataset. J Stat Manag Syst 23:1059–1068. https://doi.org/10.1080/09720510.2020.1799504
Bhatia A, Chug A, Singh AP (2020c) Hybrid SVM-LR classifier for powdery mildew disease prediction in tomato plant. In: 2020 7th International conference on signal processing and integrated networks (SPIN), pp 218–223
Bhatia A, Chug A, Singh AP (2021) Statistical analysis of machine learning techniques for predicting powdery mildew disease in tomato plants. Int J Intell Eng Inform 9:24–58
Braun U (1987) A monograph of the Erysiphales (powdery mildews). Beih Nova Hedwigia 89:1–700
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357
Chellemi DO, Marois JJ (1992) Population dynamics of the plant pathogenic fungus Uncinula necator. Can J Bot 70:942–946
Friedrich S (1995) Modeling infection probability of powdery mildew in winter-wheat by meteorological-input variables. Z Fur Pflanzenkrankh Und Pflanzenschutz J Plant Dis Prot 102:354–365
Guzman-Plazola RA (1997) Development of a spray forecast model for tomato powdery mildew (Leveillula taurica (Lev). Arn.). Ph.D. Thesis, University of California, Davis
Holiday P (1980) Fungus diseases of tropical crops. Australas Plant Pathol 9:120. https://doi.org/10.1007/bf03213662
Jones WB, Thomson SV et al (1987) Source of inoculum, yield, and quality of tomato as affected by Leveillula taurica. Plant Dis 71:266–268
Liao Y, Vemuri VR (2002) Use of k-nearest neighbor classifier for intrusion detection. Comput Secur 21:439–448
Naik HS, Zhang J, Lofquist A, Assefa T, Sarkar S, Ackerman D, Singh A, Singh AK, Ganapathysubramanian B (2017) A real-time phenoty** framework using machine learning for plant stress severity rating in soybean. Plant Methods 13:23
Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. In: 2016 Conference on advances in signal processing (CASP), pp 175–179
Palti J (1988) TheLeveillula mildews. Bot Rev 54:423–535. https://doi.org/10.1007/BF02858418
Reis A, Boiteux LS, Paz-Lima ML, Silva PP, Lopes CA (2004) Powdery mildew of Allium species caused by Oidiopsis taurica in Brazil. Hortic Bras 22:758–760
Rish I et al (2001) An empirical study of the naive Bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, pp 41–46
Sabrol H, Kumar S (2016) Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. Int J Comput Sci Inf Secur 14:622
Sahu P, Chug A, Singh AP, Singh D, Singh RP (2020) Implementation of CNNs for crop diseases classification: a comparison of pre-trained model and training from scratch. Int J Comput Sci Netw Secur 20:206–215
Vankuik AJ (1995) A weather-based forecasting model of powdery mildew in rose seedlings. Meded Landbouwkd En Toegepaste Biol Wet Univ Gent 60:439–445
Verma S, Chug A, Singh AP (2018) Prediction models for identification and diagnosis of tomato plant diseases. In: 2018 International Conference on advances in computing, communications and informatics (ICACCI), pp 1557–1563
Verma S, Chug A, Singh AP, Sharma S, Rajvanshi P (2019) Deep learning-based mobile application for plant disease diagnosis: a proof of concept with a case study on tomato plant. In: Applications of image processing and soft computing systems in agriculture, IGI Global, pp 242–271
Verma S, Chug A, Singh AP (2020) Application of convolutional neural networks for evaluation of disease severity in tomato plant. J Discret Math Sci Cryptogr 23:273–282
Xu XM, Butt DJ (1993) PC-based disease warning systems for use by apple growers 1. EPPO Bull 23:595–600
Acknowledgements
Department of Science and Technology (DST) have provided financial support for the execution of this research work under the project entitled “Application of Internet of Things (IoT) in Agriculture Sector”, Reference.No.T-319. We are obliged to them for their immense support.
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Bhatia, A., Chug, A., Singh, A.P. et al. A machine learning-based spray prediction model for tomato powdery mildew disease. Indian Phytopathology 75, 225–230 (2022). https://doi.org/10.1007/s42360-021-00430-3
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DOI: https://doi.org/10.1007/s42360-021-00430-3