Abstract
Agriculture forms the backbone of a country's economy. Indian GDP mainly depends on agriculture yield growth and their products from these agro-industry. As it largely depends on monsoons, which affects the yield to a huge extend, for which agriculture yield analysis and prediction is the toughest task for different agricultural departments across the country. Other agriculture factors which can affect yield are pests attack, deviation in temperature, soil moisture, nutrient deficiency, global warming etc. As a developing economy, this can severely affect the country's GDP, hence predicting the yield and advising certain important measures to counter the ill effects on the growth of crop are important for a stable and effective contribution to the economy of the country. It can be done by monitoring, analysing, controlling and implementing accurate amount parameters such as required amount of irrigation, in limit use of chemical fertilizers, manure, choosing crop type according to weather, soil type suitability for crop, crop rotation, moisture amount, temperature etc. In our paper, IoT sensors are used for gathering data, and analysis is done to prepare a predictive model. Our model gives comparative analysis from real data and in turn gives a predictive model which predicts from the remarks of different production data with their environmental condition. This model is tested for the effective prediction and the estimate of the agribusiness yield for the different product in Odisha state.