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

• 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
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy13041169

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.