Hybrid Classification Model with Tuned Weights for Crop Yield Prediction

One of the difficult problems in agriculture is predicting the crop production. At the international, regional, and crop level, it is crucial to make decisions on this. In Most of the cases, agricultural, land, climatic, atmospheric, and other characteristics are used to forecast crop production. ML...

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Bibliographic Details
Published in:Wireless personal communications 2023-11, Vol.133 (2), p.1325-1347
Main Authors: Kolipaka, Venkata Rama Rao, Namburu, Anupama
Format: Article
Language:English
Subjects:
Adaptive algorithms
Agricultural production
Classifiers
Communications Engineering
Computer Communication Networks
Crop production
Crop yield
Engineering
Feature extraction
Networks
Performance evaluation
Prediction models
Predictions
Preprocessing
Signal,Image and Speech Processing
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Summary:One of the difficult problems in agriculture is predicting the crop production. At the international, regional, and crop level, it is crucial to make decisions on this. In Most of the cases, agricultural, land, climatic, atmospheric, and other characteristics are used to forecast crop production. ML is a crucial decision-support model for estimating agricultural yields, enabling choices about which crops to cultivate and what to do while they are in the growing season. Numerous ML and DL algorithms have been applied to support studies on agricultural yield prediction. In this paper, a new crop yield prediction model is proposed which includes preprocessing, feature extraction and yield prediction phase. In preprocessing, data cleaning will takes place. Higher order statistical feature, information gain and improved entropy based features are extracted in feature extraction phase. The prediction is done by the hybrid model that combines Bi-GRU model and Maxout classifiers. To enhance the performance of this hybrid classifier, a new Self Adaptive Archimedes Optimization Algorithm (SAAOA) is introduced for training the weight parameters optimally. Finally the overall performance is evaluated and the better result is determined.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-023-10781-x