A Study on Weather based Crop Prediction System using Big Data Analytics and Machine Learning

• Main Authors: Sahu, Santhoshini, Daniya, T., Cristin, R.
• Format: Conference Proceeding
• Language: English
• Subjects: Agriculture; and Map Reduce; Big Data Analysis; Crops; K-Means Clustering; Production; Soil; Temperature distribution; Three-dimensional displays; Visual Representation; Wind speed
• ISSN: 2832-3017
• DOI: 10.1109/ICSSIT55814.2023.10060865

Agriculture plays a key role in the Indian economy. There are various challenges in the agricultural sector posed by climatic conditions, loss of biodiversity, soil erosion, traditional farming working with plants, diseases, and pests. The impact of using conventional methods of agriculture on the environment include deforestation, soil erosion and depletion of nutrients in the soil. Indian farmers without having any prior knowledge about the atmospheric conditions cannot choose their crops effectively. Incorporating modern agriculture can provide many solutions to these kinds of problems. Precision farming techniques can solve this issue by gathering and evaluating data on temperature, rainfall, soil, seed, crop production, humidity, and wind speed, which will assist farmers in raising agriculture production. The data is refined before being examined and processed using the map reduce architecture. Applying the K-means clustering method to the map reduce results yields a mean accuracy result for the data. Taking Consideration of regions, the relationship between different factors is analyzed using bar graphs and scatter plots which is hugely beneficial for crop prediction. This study gives an analysis of different crop prediction techniques.