Prediction of Wheat Yield by Novel SDC-LSTM Framework

Abstract

Agriculture is the primary source of income for each country, serving as its mainstay. A promising study topic has been predicting wheat production based on environmental, soil, and water characteristics. Deep-learning-based algorithms are widely employed in crop prediction to extract significant crop traits. Wheat is linked to a variety of economic, societal, and health-related factors. Wheat yield forecasting and estimation on a regional scale, on the other hand, remains difficult. Two strategies for estimating wheat yield using deep learning (DL) models are presented in this study. To solve the limitations of regional forecasting, Convolutional Neural Networks (CNN) and Deep Learning Long Short-Term Memory (LSTM) technology are utilized to anticipate agricultural yields in a timely and reliable manner.

Keywords:
Wheat-yield prediction; Machine learning (ML); Deep learning; Convolution Neural Networks (CNN); Long Short-Term Memory (LSTM)

HIGHLIGHTS

Research explores deep learning methods for predicting wheat yield.

CNN and LSTM technology improves regional crop yield forecasting reliability.

Deep learning-based wheat yield forecasting method for constraint resolution.

CNN, LSTM technology improve regional agricultural forecasting reliability.