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An automatic crop yield prediction framework designed with two-stage classifiers: a meta-heuristic approach

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Abstract

Agriculture is the main source of income for 75% of the Indian people. Weather, climate, and other natural factors have a higher influence on agricultural productivity. Forecasting crop yields is a very challenging issue for global food security. Forecasting crop yield based on the soil, environment, water, as well as crop parameters, is an important research field. Recently, many existing methods are presented to solve crop yield prediction. But, the performances of those models are inaccurate and suffered from errors. Hence, in this article, a deep learning-based system for agricultural production prediction based on environmental data is established. This research includes three stages, like pre-processing, feature extraction, and as well as prediction. The obtained raw data (environmental data) is pre-processed using a data cleaning technique to improve data quality prediction performance. The most dependable properties, such as statistical features, improved correlation, and mutual information-based features, are then retrieved from the pre-processed data. The yield predictors in the yield prediction phase are trained using these characteristics. Two-stage classifiers, stage-1 pre-prediction, and stage-2 final classification are used to represent the yield prediction phase. “Deep Belief Networks (DBN), Long Short Term Memory Networks (LSTM), and Recurrent Neural Networks (RNN)” are all used in the pre-prediction stage. The DBN, LSTM, and RNN outputs are sent into the final classification step, and it contains an improved Convolutional Neural Network (CNN). The CNN's weight function is fine-tuned via the novel Dingo Optimized Sand Piper (DOSP) model as it makes final judgments about crop production. Eventually, the efficiency of the anticipated model (two-stage classifier with DOSP) is validated by a comparative examination in terms of various measures. This research showed the efficiency of the proposed work in different types of datasets. In particular, for dataset 1, the MAE of the developed method at the 70th training rate is 50%, 62.5%, 57.1%, and 40% improved over the existing SOA, DOX, BOA, BMVO, GWO and PRO, respectively. For dataset 2, the mean of the suggested work is 2.76%, 2.57%, 2.57%, and 2.85% better than existing SOA, DOX, BOA, BMVO, GWO and PRO, schemes respectively.

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Data availability

The data were generated by using https://www.kaggle.com/prasadkevin/crops-prediction-indian-dataset and https://www.kaggle.com/prasadkevin/crops-prediction-indian-dataset

Abbreviations

ANN:

Artificial Neural Network

Bi-GRU:

Bidirectional Gated Recurrent Unit

Bi-LSTM:

Bidirectional Long Short-Term Memory

BP:

Back Propagation

BPNN:

Backpropagation neural networks

BR:

Bayesian regularization

CNN:

Convolutional Neural Network

DBN-FNN:

Deep Belief Network—Fuzzy Neural Networks

DOSP:

Dingo Optimized Sand Piper

DOX:

Dingo Optimizer

FAO:

Food and Agriculture Organization

FDEA:

Fuzzy Data Envelopment Analysis

GWR:

Geographically weighted regression

IndRNN:

Independently Recurrent Neural Network

LM:

Levenberg–Marquardt

LR:

Logistic Regression

LSTM:

Long Short Term Memory

MAE:

Mean Absolute Error

ML:

Machine Learning

MLR:

Multiple Linear Regression

MSE:

Mean Square Error

OBL:

Opposition-based Learning

OLS:

Ordinary Least Squares

PCA:

Principal Component Analysis

PRO:

Poor and Rich Optimization

RF:

Random Forest

RL-RF:

Reinforcement Learning – Random Forest

RMSE:

Root Mean Square Error

RNN:

Recurrent Neural Network

RQ:

Research Question

SCG:

Scaled Conjugated Gradient

SOA:

Sandpiper Optimization Algorithm

SVM:

Support Vector Machine

SVR:

Support Vector Regression

WHO:

World Health Organization

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

    Venkata Rama Rao Kolipaka & Anupama Namburu

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  1. Venkata Rama Rao Kolipaka
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  2. Anupama Namburu
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All authors have made substantial contributions to the conception and design, revising the manuscript, and the final approval of the version to be published. Additionally, it was agreed that each author would be responsible for all aspects of the work and ensure that any concerns about the truthfulness or integrity of any part of the work would be duly investigated and addressed.

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Correspondence to Venkata Rama Rao Kolipaka.

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Kolipaka, V.R.R., Namburu, A. An automatic crop yield prediction framework designed with two-stage classifiers: a meta-heuristic approach. Multimed Tools Appl 83, 28969–28992 (2024). https://doi.org/10.1007/s11042-023-16612-2

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