Abstract
In the current scenario, plant disease detection is seeking attention from many agricultural scientists. Plant diseases are deeply influenced by the weather conditions, and each disease has its individual weather requirements. The changes in weather parameters such as humidity, temperature, wind speed, etc., can cause many diseases in tomato plants. In the current empirical study, we have taken specific disease powdery mildew whose fungus is named as Leveillula Taurica which belongs to Leotiomycetes class, and it is responsible for the occurrence of this specific disease in tomatoes. In this research, three weather-based prediction models have been developed using k-nearest neighbor (kNN), decision tree (DT), and random forest (RF) algorithm for powdery mildew disease prediction in tomatoes at an early stage. Results indicate that the proposed model, based on RF algorithm, shows the best accuracy of 93.24% for tomato powdery mildew disease (TPMD) dataset. A real-time version of the proposed model can be used by the agricultural experts to take preventive measures in the most sensitive areas that are prone to powdery mildew disease based on the weather conditions. Hence, timely intervention would help in reducing the loss in productivity of tomato crops which will further benefit the global economy, agricultural production, and the food industry.
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References
Jones, W. B., & Thomson, S. V. (1987). Source of inoculum, yield, and quality of tomato as affected by Leveillula taurica. Plant disease, 71(3), 266–268.
Bakeer, A. R. T., Abdel-Latef, M. A. E., Afifi, M. A., & Barakat, M. E. (2013). Validation of tomato powdery mildew forecasting model using meteorological data in Egypt. International Journal of Agriculture Sciences, 5(2), 372.
Verma, S., Bhatia, A., Chug, A., & Singh, A. P. (2020). Recent advancements in multimedia big data computing for IoT applications in precision agriculture: opportunities, issues, and challenges. In Multimedia big data computing for IoT applications (pp. 391–416). Springer, Singapore.
Fuentes, A., Yoon, S., Kim, S. C., & Park, D. S. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9), 2022.
Verma, S., Chug, A., & Singh, A. P (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, A. P. (2020). Application of convolutional neural networks for evaluation of disease severity in tomato plant. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), 273–282.
Verma, S., Chug, A., & Singh, A. P. (2020). Exploring capsule networks for disease classification in plants. Journal of Statistics and Management Systems, 23(2), 307–315.
Verma, S., Chug, A., Singh, A. P., 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 (pp. 242–271). IGI Global.
Guzman-Plazola, R. A. (1997). Development of a spray forecast model for tomato powdery mildew (Leveillula Taurica (Lev). Arn.). University of California, Davis.
Bhatia, A., Chug, A., & Singh, A. P. (2020). 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). IEEE.
Bhatia, A., Chug, A., & Singh, A. P. (2020). Plant disease detection for high dimensional imbalanced dataset using an enhanced decision tree approach. International Journal of Future Generation Communication and Networking, 13(4), 71–78.
Bhatia, A., Chug, A., & Singh, A. P. (2020). Application of extreme learning machine in plant disease prediction for highly imbalanced dataset. Journal of Statistics and Management Systems, 23(6), 1059–1068. https://doi.org/10.1080/09720510.2020.1799504
Ghaffari, R., Zhang, F., Iliescu, D., Hines, E., Leeson, M., Napier, R., & Clarkson, J. (2010). Early detection of diseases in tomato crops: an electronic nose and intelligent systems approach. In The 2010 International Joint Conference on Neural Networks (IJCNN) (pp. 1–6). IEEE.
Rumpf, T., Mahlein, A.-K., Steiner, U., Oerke, E.-C., Dehne, H.-W., & Plümer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91–99.
Prince, G., Clarkson, J. P., & Rajpoot, N. M. (2015) Automatic detection diseases tomato plants using thermal stereo visible light images. PLoS One, 10(4), e0123262.
Mokhtar, U., Ali, M. A. S., Hassenian, A. E., & Hefny, H. (2015). Tomato leaves diseases detection approach based on support vector machines. In 2015 11th International Computer Engineering Conference (ICENCO) (pp. 246–250). IEEE.
Vishwakarma, V. P., & Dalal, S. (2020). A novel non-linear modifier for adaptive illumination normalization for robust face recognition. Multimedia Tools and Applications, 1–27.
Kotsiantis, S. B. (2013). Decision trees: A recent overview. Artificial Intelligence Review, 39(4), 261–283.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18–22.
Sabrol, H., & Kumar, S. (2016). Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. International Journal of Computer Science and Information Security, 14(9), 622.
Acknowledgements
This work is financially supported by the Department of Science and Technology (DST) under a project with reference number “DST/Reference.No.T-319/2018-19.” We are grateful to them for their immense support.
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Bhatia, A., Chug, A., Singh, A.P., Singh, R.P., Singh, D. (2022). A Forecasting Technique for Powdery Mildew Disease Prediction in Tomato Plants. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Second Doctoral Symposium on Computational Intelligence . Advances in Intelligent Systems and Computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_41
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