Hybrid CNN-SVM Classifier Approaches to Process Semi-Structured Data in Sugarcane Yield Forecasting Production

Information communication technology (ICT) breakthroughs have boosted global social and economic progress. Most rural Indians rely on agriculture for income. The growing population requires modern agricultural practices. ICT is crucial for educating farmers on how to be environmentally friendly. It...

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Published in:Agronomy (Basel) 2023-04, Vol.13 (4), p.1169
Main Authors: Bhattacharyya, Debnath, Joshua, Eali Stephen Neal, Rao, N. Thirupathi, Kim, Tai-hoon
Format: Article
Language:English
Subjects:
Agricultural economics
Agricultural practices
Agricultural production
Agriculture
Analysis
Artificial neural networks
Classifiers
convolutional neural network
Crop growth
Crop yield
Crop yields
Crops
Education
ensemble learning
Evaporation
Farmers
Fertilizers
Forecasting
Gaussian probabilistic method function
Information communication technology
Information technology
Mathematical models
Methods
Moisture effects
Neural networks
Probabilistic methods
Soil classification
Soil moisture
Soil water
Structured data
Sugar industry
Sugarcane
Support vector machines
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Summary:Information communication technology (ICT) breakthroughs have boosted global social and economic progress. Most rural Indians rely on agriculture for income. The growing population requires modern agricultural practices. ICT is crucial for educating farmers on how to be environmentally friendly. It helps them create more food by solving a variety of challenges. India’s sugarcane crop is popular and lucrative. Long-term crops that require water do not need specific soil. They need water; the ground should always have adequate water due to the link between cane growth and evaporation. This research focuses on forecasting soil moisture and classifying sugarcane output; sugarcane has so many applications that it must be categorized. This research examines these claims: The first phase model predicts soil moisture using two-level ensemble classifiers. Secondly, to boost performance, the proposed ensemble model integrates the Gaussian probabilistic method (GPM), the convolutional neural network (CNN), and support vector machines (SVM). The suggested approach aims to correctly anticipate future soil moisture measurements affecting crop growth and cultivation. The proposed model is 89.53% more accurate than conventional neural network classifiers. The recommended models’ outcomes will assist farmers and agricultural authorities in boosting production.
ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy13041169