Abstract
Aims and Background: Agriculture plays a major role in the global economy, providing food, raw materials, and jobs to billions of people and driving economic growth and poverty reduction. Rice is the most widely consumed crop domestically, making it a particularly important crop for rural populations. The exact number of rice varieties worldwide is difficult to determine as new varieties are constantly being developed and marketed.
Objective: The most common method of rice variety identification is a comparison of its physical and chemical properties to a reference collection of known types.
Methodology: This is a relatively quick and cost-effective approach that can be used to accurately differentiate between distinct varieties. In some cases, genetic testing may be used to confirm the identity of a variety, although this technique is more expensive and time-consuming. However, we can also utilize efficient, precise, and cost-effective digital image processing and machine vision techniques.
Results: This study describes different types of ensemble methods, such as bagging (Decision Tree, Random Forest, Extra Tree), boosting (AdaBoost, Gradient Boost, and XGBoost), and voting classifiers to classify five different varieties of rice. Extreme Gradient Boosting (XGBoost) has achieved the highest average classification accuracy of 99.60% among all the algorithms.
Conclusion: The findings of the performance measurement indicated that the proposed model was successful in classifying the various varieties of rice.
Keywords: Rice grain, classification, bagging, boosting, voting, machine vision.
Graphical Abstract
Call for Papers in Thematic Issues
Advancements in Brain Tumor Detection and Treatment using Machine Learning, Deep Learning, and Blockchain Technology
Brain tumors are among the most challenging diseases to diagnose and treat, requiring specialized expertise and advanced technology for accurate detection and effective treatment. In recent years, machine learning and deep learning algorithms have shown promise in improving the accuracy and efficiency of brain tumor detection through medical imaging analysis. ...read more
Advances of Biometric Data Analysis to Boost Intelligent Applications
With the rapid development of artificial intelligence (AI) technology, more and more intelligent devices and applications are appearing in our daily lives, such as smart home, smart agriculture, health diagnosis, educational support, and environmental monitoring. Interaction with these devices or applications has become a pressing issue that can greatly improve ...read more
Attainment of SDGs through the Advancement in Solar Energy Systems
With less than a decade until we reach 2030, it is crucial to address the deep inequalities affecting not only our health but also our quality of life, and the economy of countries worldwide. Few of the UN's Sustainable Development Goals (SDGs) can be directly and indirectly achieved through the ...read more
Computational Intelligence theory and practices
Computational intelligence represents the extraordinary capacity of the human intellect to assert and understand in an environment of uncertainty and imprecision. Computational intelligence is new-age multidisciplinary branch of artificial intelligence which can reduce the human work. The main goal of computational intelligence is to develop intelligent systems to solve real-world ...read more
Related Journals
Recent Patents on Engineering
3