A Novel Integration of Analytical and Simulation Approaches for Wheat Yield Prediction with Deep Learning Models
Keywords:
Agriculture, Deep Learning, Decision support system for agrotechnology transfer, Convolutional Neural Network, long short-term memory.Abstract
Food, water, and air are necessary for everyone’s survival. Since the population is increasing continuously, in the same way, the agricultural industry needs to adopt various new techniques to fulfil the requirements. Changes in climate conditions have an adverse effect on the crops with the healthy existence of agriculture. Traditional farming methods are not fulfilling the needs. Since machine learning and various simulation models are used for early prediction of crop yield. Intelligent algorithms detect at early stages the production of crops and for the same, we can use the required material to increase the healthy production. Also, there are various simulation models which help in forecasting the yield based on the previous data. In this paper, there is a discussion about DSSAT and WOFOST simulation model for yield prediction. Also, here in this paper a new model is introduced using CNN and LSTM for better yield prediction named as Supervised Deep convolutional long-short term memory which gives R2 =0.91, MAPE=1, MSE=,8 RMSE=2, MAE= 0.01.
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