Department of Computer Science and Engineering & IT, SCIT, Manipal University Jaipur, Jaipur, Rajasthan, India
Shahreen Kasim
Center of Intelligent and Autonomous System, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
Zirawani Baharum
Malaysian Institute of Industrial Technology, Universiti Kuala Lumpur, Bandar Seri Alam, Johor Bahru, Malaysia
Hairulnizam Mahdin
Center of Intelligent and Autonomous System, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
Salama A. Mostafa
Center of Intelligent and Autonomous System, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
This paper aims at collecting and analysing temperature, rainfall, soil, seed, crop production, humidity and wind speed data (in a few regions), which will help the farmers improve the produce of their crops. Firstly, we pre-process the data in a Python environment and then apply the MapReduce framework, which further analyses and processes the large volume of data. Secondly, k-means clustering is employed on results gained from MapReduce and provides a mean result on the data in terms of accuracy. After that, we use bar graphs and scatter plots to study the relationship between the crop, rainfall, temperature, soil and seed type of two regions (Ahmednagar, Maharashtra and, Andaman and Nicobar Islands). Further, a self-designed recommender system has been used to predict the crops and display them on a Graphic User Interface designed in a Flask environment. The system design is scalable and can be used to find the recommended crops of other states in a similar manner in the future.
