A Comprehensive Analysis of Deep Learning Models in Agricultural Crop Yield Prediction

Abstract

In comparison to machine learning algorithms, deep learning algorithms are more effective in terms of their ability to make accurate predictions about agricultural data. When trying to attain better outcomes, the accuracy and efficiency of the algorithm are highly crucial factors to consider. In order to help the farmers to accurately predict the yields of their crops, the study of agricultural data is of great assistance. This resulted in a significant amount of study, and the researchers used a variety of data to make predictions on the yields of the various crops. The primary objective of this study is to locate a more effective deep learning algorithm that may be used to forecast crop production. Hence the SLR was carried out and finally identified 20 opted research articles related to deep learning models utilized in agricultural data prediction.  As a result, this SLR discovered that the LSTM-based deep learning models provide more accurate predictions when applied to agricultural data sets. A great number of research papers made use of the same model for better results.