Predicting the Outbreak of Tikka and Rust in Grountnut (Arachis Hypogaea)
R. Ruba Mangala1, A. Padmapriya2
1R. Ruba Mangala*, Research Scholar, Department of Computer Science, Alagappa University, Karaikudi, India.
2Dr. A. Padmapriya, Associate Professor, Department of Computer Science, Alagappa University, Karaikudi, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1862-1867 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6431018520/2020©BEIESP | DOI: 10.35940/ijrte.E6431.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Groundnut (Arachis hypogaea) is one among the most important oil seed crop cultivated in India. Tikka leaf spot and Rust are the major disease of groundnut that effects on production and productivity. The prediction was made based on factors such as minimum and maximum temperature, morning and evening humidity, wind speed, sunshine hours that quantifies the disease infestation in groundnut. The relationship between the weather, pest and disease infestation are identified which supports the model to predict the occurrence of the disease. The observations were recorded at an interval of one week from the occurrence of tikka and rust. The percent disease intensity is calculated based on the scale explained by Mayee and Data. The favourable climatic conditions for tikka and rust disease development ranges between 26OC – 31OC and 25OC – 30OC respectively, relative humidity greater than 85%, prolonged heavy rainfall, wind speed and rain. The rules are generated based on the recorded observation and the weather parameters. The main objective is to diagnose the existence of tikka and rust disease coupled with weather parameters.
Keywords: Groundnut, Tikka Leaf Spot, Rust, Weather Parameters, Rule Generation, Prediction.
Scope of the Article: Regression and Prediction.