Predictive Modeling of Crop Yield Using Deep Learning Based Transformer with Climate Change Effects DOI Open Access

Yash Pravesh S,

Navneet Garg, R. Arora

et al.

International Research Journal of Multidisciplinary Technovation, Journal Year: 2024, Volume and Issue: unknown, P. 223 - 240

Published: Nov. 30, 2024

Climate change is a significant global challenge concerning agriculture and food security. The understanding of climate effects on crop production necessary for developing an effective adaptation strategies predicting yield accurately. This paper suggests the combined Clustering Long Short Term Memory Transformer (CLSTMT) model prediction. CLSTMT hybrid that integrates clustering, deep learning based LSTM techniques. outliers from historical data are removed using k-means clustering. Followed by, predicted Transformer-based neural network with layers feed-forward (FNN) components. design effectively captures climate-influenced patterns, enhances precision comprehensiveness experiment conducted dataset yield, climate, pesticide details over 101 countries collected 1990 to 2013. comparative analysis reveals outperforms other regression models such as SGDRegressor (SGDR), Lasso Regression (LR), Support Vector (SVR), ElasticNet (EN) Ridge (RR). proposed enhancing predictions. findings indicate provides accurate prediction high R2 0.951 lesser Mean Absolute Percentage Error (MAPE) 0.195. value minimal average percentage deviation between actual yields. more compared others.

Language: Английский

Adaptive dynamic crayfish algorithm with multi-enhanced strategy for global high-dimensional optimization and real-engineering problems DOI Creative Commons

Mohamed Elhosseny,

Mahmoud Abdel-Salam,

Ibrahim M El-Hasnony

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 27, 2025

Language: Английский

Citations

0
South Indian agricultural crop yield prediction using deep learning and transfer learning models DOI

R. Anandavalli,

K. Karthigadevi,

Geeta Rani

et al.

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(5)

Published: May 1, 2025

Language: Английский

Citations

0
A Temporal–Geospatial Deep Learning Framework for Crop Yield Prediction DOI Open Access
Lei Wang, Zhou Chen, Weichun Liu

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(21), P. 4273 - 4273

Published: Oct. 31, 2024

With the rapid development of information technology, demand for digital agriculture is increasing. As an important agricultural production topic, crop yield has always attracted much attention. Currently, artificial intelligence, particularly machine learning, become leading approach prediction. a result, developing learning method that accurately predicts one central challenges in agriculture. Unlike traditional regression prediction problems, significant time correlation. For example, weather data each county show strong temporal correlations. Moreover, geographic from different regions also impacts to certain extent. if county’s neighboring counties have good harvest, then this likely high yields as well. This paper introduces novel hybrid deep framework combines convolutional neural network (CNN), graph attention (GAT) and long short-term memory (LSTM) modules enhance accuracy. Specifically, CNN employed extract features input year. GAT introduced model geographical relationships between counties, allowing capture spatial dependencies more effectively. LSTM used within many years. The proposed CNN-GAT-LSTM captures both relationships, thereby improving accuracy We conduct experiments on nationwide dataset includes 1115 soybean-producing 13 states United States covering years 1980 2018. evaluate performance our based three metrics, namely root mean squared error (RMSE), R-squared (R2) correlation coefficient (Corr). experimental results demonstrate achieves improvements over existing state-of-the-art model, with RMSE reduced by 5%, R2 improved 6% Corr enhanced 4%.

Language: Английский

Citations

2
A green hydrogen production model from solar powered water electrolyze based on deep chaotic Lévy gazelle optimization DOI Creative Commons
Heba Askr, Mahmoud Abdel-Salam, Václav Snåšel

et al.

Engineering Science and Technology an International Journal, Journal Year: 2024, Volume and Issue: 60, P. 101874 - 101874

Published: Nov. 11, 2024

Language: Английский

Citations

2
Predictive Modeling of Crop Yield Using Deep Learning Based Transformer with Climate Change Effects DOI Open Access

Yash Pravesh S,

Navneet Garg, R. Arora

et al.

International Research Journal of Multidisciplinary Technovation, Journal Year: 2024, Volume and Issue: unknown, P. 223 - 240

Published: Nov. 30, 2024

Climate change is a significant global challenge concerning agriculture and food security. The understanding of climate effects on crop production necessary for developing an effective adaptation strategies predicting yield accurately. This paper suggests the combined Clustering Long Short Term Memory Transformer (CLSTMT) model prediction. CLSTMT hybrid that integrates clustering, deep learning based LSTM techniques. outliers from historical data are removed using k-means clustering. Followed by, predicted Transformer-based neural network with layers feed-forward (FNN) components. design effectively captures climate-influenced patterns, enhances precision comprehensiveness experiment conducted dataset yield, climate, pesticide details over 101 countries collected 1990 to 2013. comparative analysis reveals outperforms other regression models such as SGDRegressor (SGDR), Lasso Regression (LR), Support Vector (SVR), ElasticNet (EN) Ridge (RR). proposed enhancing predictions. findings indicate provides accurate prediction high R2 0.951 lesser Mean Absolute Percentage Error (MAPE) 0.195. value minimal average percentage deviation between actual yields. more compared others.

Language: Английский

Citations

0