Abstract
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.
Highlights
Due to sudden changes in weather conditions, farmers and agriculture throughout the country suffer as they fail to produce enough crops
When it comes to the relation between humidity, crop and production per area it can be noticed from Fig. 18 that most crops thrive in 74 %-78% humidity, whereas a few crops require higher humidity between 80%-84%
The model focuses on a wide range of crops and their produce per area along with the soil type and seed types depending on the varieties used in FIGURE 19. 3D scatter plot showing the relationship between crop, production per area and humidity for Andaman and Nicobar Islands that all these parameters combine together to form a suitable environment to give a good produce per area for a major variety of crop
Summary
Due to sudden changes in weather conditions, farmers and agriculture throughout the country suffer as they fail to produce enough crops This leads them to take serious steps as they are unable to provide for their family and make ends meet. A lot of research work has been conducted in identifying how weather as a factor affects agriculture, but most of these studies require large complex information which is not directly available This leads to the collection of data by estimation which can have either a negative or a positive effect. The combination of all the data provides an elaborated view of the system and serves as the source of the big data In this project, a MapReduce framework for data processing and a K-means clustering algorithm along with a recommendation function is carried out in the hope to propose.
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