WB-CPI: Weather Based Crop Prediction in India Using Big Data Analytics

• Published in: IEEE access Vol. 9; pp. 137869 - 137885
• Main Authors: Gupta, Rishi, Sharma, Akhilesh Kumar, Garg, Oorja, Modi, Krishna, Kasim, Shahreen, Baharum, Zirawani, Mahdin, Hairulnizam, Mostafa, Salama A.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agriculture; Big Data; big data analysis; Cluster analysis; Clustering; Crop production; Crops; Data mining; Data models; graphical visualization; k-means clustering; map reduce; Meteorology; Rainfall; recommendation system; Recommender systems; Soil; Soils; Systems design; Vector quantization; Wind speed
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3117247

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.