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

Abstract

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.

“Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development

“…The phenotype monitoring technology uses an internal gradient algorithm to accurately measure the target region and diameter of maize stems. The technology uses color images captured during the small bell stage, extracting color information and applying a morphological gradient algorithm [15,16]. A simple detection system is created for automated yield estimation and picking in small-target apple orchards using the public MinneApple dataset.…”

Predicting Sugarcane Yield via the Use of an Improved Least Squares Support Vector Machine and Water Cycle Optimization Model

“…At present, many researchers have applied machine learning algorithms to continuously improve and update climate prediction models [19][20][21], but improving yield prediction models is not ideal. This is attributed to the fact that fewer studies have been conducted on combining machine learning algorithms with climate prediction models to predict regional yields of sugarcane [22]. In addition, the accuracy of sugarcane-related models is generally low (RMSE from 19.7 to 20.0 ton/ha), which needs to be improved.…”