India being an agricultural country, its economy primarily depends on agriculture yield growth. In India, agriculture is largely influenced by many factors which are unpredictable. One of those factors is Soil moisture, which is a key variable in controlling the exchange of water and heat energy between the land surface and the atmosphere through evaporation and plant transpiration. Hence, it plays an important role in the development of weather patterns and the production of precipitation. Agriculture productivity mainly depends on quality of soil, which is dependent on factors like soil moisture and pH values. The proposed project increases the productivity of the crop by determining the quality of soil by using pH values (a measure of acidity or alkalinity of water soluble substances), This work presents a system in which data analytics techniques are used in order to predict the most profitable crop in the current soil conditions. The proposed system uses few sensors that are connectedto a microcontroller and these sensors are inserted into the soil for retrieving the values. Now these values are stored in different devices which are connected to the WiFi modem around it. And then they are compared with the different pH values and analysis is done using KNN and Naïve bayes. Thus After the analysis, we conclude which type of crop can be harvested in which type of particular soil. Doing this, we can improve the crop yield productivity and increase the profit margin of farmer helping them over a longer run.

Additional Metadata
Keywords Crop detecting, Iot, Knn, Naïve bayes, Sensors
Persistent URL hdl.handle.net/1765/119550
Journal Journal of Advanced Research in Dynamical and Control Systems
Citation
Sriharsha, R. (Rapaka), Chennupati, M.R. (Mounish Rayudu), Malladi, S. (Sriram), Rajkumar, R. (Rajasekaran), & Masih, J. (Jolly). (2019). Prediction of cropping type using data mining algorithms based on different soil parameters. Journal of Advanced Research in Dynamical and Control Systems, 11(4 Special Issue), 1496–1502. Retrieved from http://hdl.handle.net/1765/119550