Abstract
Pesticides are used abusively mainly used to control plant diseases and pests, which lead to reduced quality of vegetables and endangering the life of the living beings. So we propose a model that can predict the presence of diseases with the fulfillment of speed and accuracy. And here for the successful predictions it mainly depends on the selected parameters which we use for the prediction. The parameters we use are predictable. Predictable computer applications that predicts diseases under favourable conditions will be of great help to all farmers. Such applications would reduce problems related to plant protection. In order to create this prediction model we need to consider different prediction variables. Prediction is done using the weather variables such as humidity, temperature and soil conditions such as soil type and data that represents specific disease characteristics. Through this model we check the presence of diseases present in Cassava plants. The proposed solution will take current weather details and soil conditions as input and would predict the diseases, if present any, along with some suggestions to overcome or suppress these diseases.
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References
Ilic, M., Ilic, S., Jovic, S., Panic, S.: Early cherry fruit pathogen disease detection based on data mining prediction. Comput. Electron. Agric. 150, 418–425 (2018)
Predic, B., Ilic, M., Spalevic, P., Trajkovic, S., Jovic, S., Stanic, A.: Data mining based tool for early prediction of possible fruit pathogen infection. Comput. Electron. Agric. 154, 314–319 (2018)
Golhani, K., Balasundram, S.K., Vadamalai, G., Pradhan, B.: A review of neural networks in plant disease detection using hyperspectral data. Inf. Process. Agric. 5, 354–371 (2018)
Ghosh, S., Biswas, S., Sarkar, D., Sarkar, P.P.: A novel Neuro-fuzzy classification technique for data mining. Egypt. Inform. J. 15, 129–147 (2014)
Kaur, K., Kaur, M.: Prediction of plant disease from weather forecasting using data mining. Int. J. Future Revolut. Comput. Sci. Commun. Eng. (2018)
Ahmed, N., Khan, M.A., Khan, N.A., Ali, M.A.: Prediction of potato late blight disease based upon environmental factors in Faisalabad, Pakistan. J. Plant Pathol. Microbiol. (2015)
Pavan, W., Fraisse, C.W., Peres, N.A.: Development of a web-based disease forecasting system for strawberries Comput. Electron. Agric. 75, 169–175 (2011)
Chung, C.-L., Huang, K.-J., Chen, S.-Y., Lai, M.-H., Chen, Y.-C., Kuo, Y.-F.: Detecting Bakanae disease in rice seedlings by machine vision. Comput. Electron. Agric. 121, 404–411 (2016)
Dixit, A., Nema, S.: Wheat leaf disease detection using machine learning method. Int. J. Comput. Sci. Mob. Comput. 7, 124–129 (2018)
Sabareeswaran, D., Guna Sundari, R.: A hybrid of plant leaf disease and soil moisture prediction in agriculture using data mining techniques. Int. J. Appl. Eng. Res. 12, 7169–7175 (2017)
Predic, B., Ilic, M., Spalevic, P., Trajkovic, S., Jovic, S., Stanic, A.: Data mining based tool for early prediction of possible fruit pathogen infection. Comput. Electron. Agric. 154, 314–319 (2018)
Dhomse Kanchan, B., Mahale Kishor, M.: Study of machine learning algorithms for special disease prediction using principal of component analysis. In: International Conference on Global Trends in Signal Processing, Information Computing and Communication (2016)
Kiania, E., Mamedovba, T.: Identification of plant disease infection using soft-computing: application to modern botany. Faculty of Engineering, Near East University, Nicosia, North Cyprus, National Academy of Science, Azerbayjan, Mardakan Dendrary
Sabareeswaran, D.: A hybrid of plant leaf disease and soil moisture prediction in agriculture using data mining techniques. Research Scholar, Department of Computer Science, Karpagam Academy of Higher Education (KAHE), Karpagam University, Coimbatore, Tamilnadu, India
Sonare, Ms.B., Zarkar, S., Talele, P., Deshmukh, R., Shelake, U.: Review on crop pests forewarning with weather factors using machine learning. Department of Information Technology, Pimpri Chinchwad College of Engineering
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Anand, A., Joseph, M., Sreelakshmi, S.K., Sreenu, G. (2020). Cassava Disease Prediction Using Data Mining. In: Karrupusamy, P., Chen, J., Shi, Y. (eds) Sustainable Communication Networks and Application. ICSCN 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-030-34515-0_71
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DOI: https://doi.org/10.1007/978-3-030-34515-0_71
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