IMPORTANCE OF BIG DATA ANALYTICS IN THE AGRICULTURAL SECTOR

Abstract

Generation of data and processing of it has been increasing day by day and reached beyond the limitations of traditional data analyzing techniques almost in every sector including agriculture sector. Applying big data strategies for agriculture sector could contribute to economic growth in terms of improving productivity and reducing environmental impacts. Agricultural products include plant, livestock and fisheries. Genetics such as variety, age, sex, etc. and environmental factors such as climate, soil fertility, pest and disease attack etc. are affecting the agriculture production. The data generated from above are having high volume, velocity, variety and variability which are classic examples of big data sources. Further, stakeholders of agriculture, such as government, private organizations and researchers generate, preserve and exploit the huge amount of data related to agricultural production, weather and climate, marketing, supply chain, etc. which need the help of big data strategies for further analysis. The big data, combined from the above sources, are collected not only to predict the future events but also to interpret past events. Adoption of big data would help to decrease crop failure, suggest the soil sensing, and improve farmers’ profits while reducing the excessive use of chemicals and their impacts on ecosystems. However, revealing hidden patterns by using big data strategies requires huge mobilizations of technologies, infrastructure, and expertise, which are much complicated for an individual farmer at the moment. Nevertheless adopting change on the farm in small level can lead to significant long-term success. Therefore, the authors suggested applying big data strategies at least in agricultural research fields found in Sri Lanka for the prospective development of agriculture in the future.