Authors: K. Rajkumar, A. Swetha
Abstract: Food security and economic development are two of the most basic human necessities that many countries, India included, depend substantially on agriculture to meet. Variations in temperature, precipitation, humidity, soil type, wind speed, and seasons all have a role in determining harvest success. while farmers don't take these climatic variables into consideration while selecting crops, agricultural production drops and they lose money. This study presents a weather-based crop prediction system that utilises Big Data Analytics and Machine Learning. It has the potential to assist with smart crop recommendation and agricultural decision-making. This proposed system is able to make use of data from large agricultural databases, which include information on crop production, weather, regions, and seasons. By eliminating duplicate or incorrect records, preprocessing ensures that the data is consistent and of high quality. The enhanced dataset is processed using the MapReduce programming paradigm, which allows for efficient handling and analysis of large volumes of agricultural data. After that, we'll use the K-Means Clustering technique to group crops with comparable production characteristics and then figure out the average crop yield in different places. Bar charts and scatter plots are examples of graphical visualisation techniques used to study the relationships between climate conditions and crop production; these tools further enhance understanding of weather-dependent agricultural trends. In addition to assisting farmers in selecting climate-appropriate crops, the developed framework improves agricultural planning and resource utilisation. The system is further implemented via a Django-based web application that provides an interactive platform for weather-based crop suggestion, visualisation, and decision help. Improved crop forecasting and less harmful farming techniques are two outcomes of the proposed method's integration of visual analytics, machine learning, and big data analytics, which contributes to the advancement of precision agriculture.
DOI: http://doi.org/10.5281/zenodo.21375319
