Integrating Temporal Fluctuations in Crop Growth with Stacked Bidirectional LSTM and 3D CNN Fusion for Enhanced Crop Yield Prediction

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Published: Oct 27, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i9.8543
Keywords:
Crop yield prediction, Machine learning, Deep learning, Neural networking, CNN, RNN, LSTM, Bi GRU, Maxout classifiers

Main Article Content

Venkata Rama Rao Kolipaka
School of Computer Science and Engineerig, VIT-AP University, Amaravati, Andhra Pradesh 522237, India
Anupama Namburu
School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh 522237, India

Abstract

Optimizing farming methods and guaranteeing a steady supply of food depend critically on accurate predictions of crop yields. The dynamic temporal changes that occur during crop growth are generally ignored by conventional crop growth models, resulting in less precise projections. Using a stacked bidirectional Long Short-Term Memory (LSTM) structure and a 3D Convolutional Neural Network (CNN) fusion, we offer a novel neural network model that accounts for temporal oscillations in the crop growth process. The 3D CNN efficiently recovers spatial and temporal features from the crop development data, while the bidirectional LSTM cells capture the sequential dependencies and allow the model to learn from both past and future temporal information. Our model's prediction accuracy is improved by combining the LSTM and 3D CNN layers at the top, which better captures temporal and spatial patterns. We also provide a novel label-related loss function that is optimized for agricultural yield forecasting. Because of the relevance of temporal oscillations in crop development and the dynamic character of crop growth, a new loss function has been developed. This loss function encourages our model to learn and take advantage of the temporal trends, which improves our ability to estimate crop yield. We perform comprehensive experiments on real-world crop growth datasets to verify the efficacy of our suggested approach. The outcomes prove that our unified strategy performs far better than both baseline crop growth prediction algorithms and cutting-edge applications of deep learning. Improved crop yield prediction accuracy is achieved with the integration of temporal variations via the merging of bidirectional LSTM and 3D CNN and a unique loss function. This study helps move the science of estimating crop yields forward, which is important for informing agricultural policy and ensuring a steady supply of food.

Article Details

How to Cite
Kolipaka, V. R. R. ., & Namburu, A. . (2023). Integrating Temporal Fluctuations in Crop Growth with Stacked Bidirectional LSTM and 3D CNN Fusion for Enhanced Crop Yield Prediction. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 376–383. https://doi.org/10.17762/ijritcc.v11i9.8543
Issue
Vol. 11 No. 9 (2023)
Section
Articles

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