Crop yield prediction in India based on mayfly optimization empowered attention-bi-directional long short-term memory (LSTM)

被引：0
作者：
M. Vamsi Krishna
K. Swaroopa
G. SwarnaLatha
V. Yasaswani
机构：
[1] Aditya Engineering College,
[2] Jawaharlal Nehru Technological University Kakinada,undefined
来源：
Multimedia Tools and Applications | 2024年 / 83卷
关键词：
Crop yield prediction; Cultivation; Feature selection; Bidirectional long short-term memory;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Accurate crop yield prediction is extremely useful to global food production. On the basis of precise forecasts, timely import and export choices should be made. The model of crop yield prediction facilitates the farmers for making better decision regarding the suitable time for crop cultivation. In this study, the prediction of major crops in India is focused by using weather, soli and rainfall data.This study uses pre-processing, feature selection (FS) and prediction model. Initially, the dataset is normalized and the necessary features are selected by three FS models. The FS models are Lasso Based Feature Selection (LFS), Correlation Based Feature Selection (CFS) and Mutual Information Based Feature Selection (MIFS). Then deep learning (DL) based optimization (Attention with Bidirectional Long Short-Term Memory (A-BiLSTM)-MayFlyAlgorithm (MFA) is used for crop prediction. This optimization is used to minimize the loss function; thereby achieving better prediction. In India, the crops like Rice, sugarcane, wheat andmaize are the most cultivatable. Hence, in this work, these crops are considered for prediction. The performance of the BiLSTM- MFA is compared with certain DL models on the basis of error measures.
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页码：29841 / 29858
页数：17
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