Abstract
Agriculture is the science and practice of growing plants and animals. Agriculture is India's second largest
industry, accounting for 60.45% of the country's land area. Agriculture and agro-industry products account for
the majority of the Indian economy. Crop rotation, soil clamminess, air and surface temperature, precipitation,
and other factors all have a part in the cultivation process. This project seeks to gather and analyze data on
temperature, rainfall, soil, seed, crop productivity, humidity, and wind speed (in a few places) in order to assist
farmers in improving agricultural yield. Preprocess the data in a Python environment first, then use the
MapReduce framework to examine and process the massive amount of data. Second, k-means clustering is
applied to MapReduce results, yielding a mean result on the data in terms of accuracy. Then, using bar graphs
and scatter plots, investigate the link between two locations' crops, rainfall, temperature, soil, and seed type.
Furthermore, a self-designed recommender system was utilized to forecast the crops and present them on a
Flask-based Graphic User Interface. Some fundamental information is necessary to increase crop quality, such
as plant selection and soil parameters like pH, which play a vital influence in obtaining a decent yield. It's also
crucial to choose the correct seeds and calculate how much fertilizer and insecticide you'll need.
Modules
Algorithms
Software And Hardware