Predictive analytics in agriculture can help anticipate agricultural yields effectively, by using a combination of historical data and current trends. It maximises production and profitability by analysing patterns and trends from previous yields, weather conditions, and other relevant aspects.One of the most common applications of predictive analytics in agriculture is crop yield prediction. This involves using data from various sources, such as soil, weather, satellite, and drone imagery.
Predictive analytics can help farmers anticipate challenges like pest infestations, diseases, and extreme weather events. Using predictive analytics, companies can effectively forecast inventory and required production rates. Additionally, your team can estimate and prevent potential production failures using past data. Organizations that use predictive data analysis can anticipate future outcomes, identify risk and are better positioned to make decision.
Presentation -Dibyabharati Nayak
