Open AccessData science and analytics in agricultural development
Author(s) -
Karamveer Singh Sidhu,
Ravinder J. Singh,
Snehdeep Singh,
Gunjot Singh
Publication year - 2021
Publication title -
environment conservation journal/environment conservation journal
Language(s) - English
Resource type - Journals
eISSN - 2278-5124
pISSN - 0972-3099
DOI - 10.36953/ecj.2021.se.2202
Subject(s) - agriculture , analytics , big data , population , data science , business , sustainability , marketing , computer science , geography , sociology , data mining , demography , ecology , archaeology , biology
The consistent advancement of innovation has implied information and data being created at a rate, not at all like ever previously, and it's just on the ascent. The world makes an extra 2.5 quintillion bytes of information every year. The demand for individuals talented in investigating, deciphering, and utilising this information is now high and is set to become exponential over the coming years. The total populace is relied upon to arrive at 9.7 billion by 2050 from the current population of 7.8 billion. The Food and Agriculture Organization (FAO) has predicted that the development of farming must be expanded by 70% to provide for the extended interest. Data-driven agriculture choices can be a potent technology to manage the needs of this much high population, as this technology gives higher efficiency, rehearses support-ability, and even assists with giving straightforwardness to purchasers and consumers needing to find out about their food as reported in the studies. The current and future interests will require more data researchers, data engineers, data specialists, and chief data Officers. This paper tries to examine the need, use, role, and issues faced by data science and data analytics to improve the quality as well as quantity of Agricultural produce thereby leading to an increase in production, a decrease in costs, and overall sustainability.