AI-BASED-FORECASTING-MODEL-TO-PREDICT-YIELD

Weather had a major influence in the prediction of crop yield. The influence of weather can be considered as a major priority role in the prediction of crop yield. Due to sudden changes in weather conditions, farmers and agriculture throughout the country suffer as the farmers are failed to produce enough crops. So, by predicting the values of environmental parameters like soil moisture, humidity, temperature, pressure, and luminosity(sunlight), collected through on ground sensors which helps farmers to predict the final yield of the crop. In traditional yield forecasting,manual surveys were conducted and it requires the exact information regarding the crop (e.g: assessing their health, counting the plants, damage from pest and non-favourable environment etc). However, manual surveys are difficult and expensive. This task becomes easier with the enforcement of remote sensing techniques where the decision makers can imagine all the crop lands with other related information and present state. By using these technologies, yield estimation can be accomplished within half or even in smaller time frame with minimum number of resources and elevated accuracy. So, it is easier and helps farmers to plan well in terms of irrigation needs and any other precautions to be made that can improve the quantity and quality of the crops