Abstract
Plant disease fore casting system is an important system as it can be used for prediction of disease, further it can be used as an alert system to warn the farmers in advance so as to protect their crop from being getting infected. Fore casting system will predict the risk of infection for crop by using the environmental factors that favor in germination of disease. In this study an artificial neural network based system for predicting the risk of powdery mildew in Picrorhiza kurrooa was developed. For development, Levenberg–Marquardt backpropagation algorithm was used having a single hidden layer of ten nodes. Temperature and duration of wetness are the major environmental factors that favor infection. Experimental data was used as a training set and some percentage of data was used for testing and validation. The performance of the system was measured in the form of the coefficient of correlation (R), coefficient of determination (R2), mean square error and root mean square error. For simulating the network an inter face was developed. Using this interface the network was simulated by putting temperature and wetness duration so as to predict the level of risk at that particular value of the input data.
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Acknowledgments
The research is subsidized by Council of Scientific and Industrial Research–Central Scientific Instruments Organization (CSIR-CSIO) under Grant Number PS0103-01. The authors thank the Director, Institute of Himalayan Bioresource Technology (CSIR-IHBT) Himachal Pradesh, India for their support. The authors are thankful to the Director CSIR-CSIO Chandigarh for helping in conducting the research work and funding. Authors are also thankful to all the staff members who are involved in the hardware, software and testing of the research work.
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Shivling, V.D., Ghanshyam, C., Kumar, R. et al. Prediction Model for Predicting Powdery Mildew using ANN for Medicinal Plant—Picrorhiza kurrooa . J. Inst. Eng. India Ser. B 98, 77–81 (2017). https://doi.org/10.1007/s40031-016-0225-9
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DOI: https://doi.org/10.1007/s40031-016-0225-9