WB-CPI: Weather Based Crop Prediction in India Using Big Data Analytics
| Autor: | Shahreen Kasim, Hairulnizam Mahdin, Akhilesh Kumar Sharma, Krishna Modi, Zirawani Baharum, Salama A. Mostafa, Oorja Garg, Rishi Gupta |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Bar chart Computer science Big data Agricultural engineering Recommender system Wind speed law.invention Data modeling recommendation system law General Materials Science Cluster analysis computer.programming_language business.industry General Engineering Agriculture Python (programming language) k-means clustering TK1-9971 graphical visualization map reduce Scatter plot big data analysis Electrical engineering. Electronics. Nuclear engineering business computer |
| Zdroj: | IEEE Access, Vol 9, Pp 137869-137885 (2021) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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