Exploring AI-Based Approaches for Multi-Class Order of Farming Information: A Comprehensive Analysis
Abstract
The agricultural sector generates a vast amount of data, containing valuable information that can be effectively analyzed through data mining techniques. Classification prediction is a crucial method in agricultural data mining. This study presents three AI algorithms - Decision Tree, Random Forest, and SVM aimed at enhancing the classification accuracy for multi-class agricultural data. The performance of these algorithms was evaluated using a standard agricultural multi-class dataset of Eucalyptus. The experimental results demonstrate that the Random Forest approach achieved significant improvements in classification accuracy for the Eucalyptus dataset, achieving a precision of 95.84%.
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Introduction
Machine Learning algorithms are iterative processes or sets of methods that assist a model in adapting to data with a specific objective. The application of AI in modern agricultural practices has the potential to significantly enhance agriculture by automating processes and providing insights into agricultural production [4]. In the context of actual farming operations, the use of computer-based information technology in precision agriculture has become increasingly widespread, resulting in the collection of large amounts of relevant spatial and contextual data associated with precision farming practices. Effectively extracting hidden relationships from massive agricultural data and aiding decision-makers in formulating precise farming strategies and optimizing agricultural production are critical and pressing challenges [11]. The classification of valuable agricultural data often serves as a crucial step in extracting meaningful insights from agricultural information. Therefore, the automatic classification of agricultural data is one of the primary objectives in the field of precision agriculture. To address the challenge of multi-class classification in agriculture, particularly using the Eucalyptus dataset, this paper introduces three AI algorithms: Decision Tree, Random Forest, and SVM.
Conclusion
In conclusion, the experimental results demonstrate the effectiveness of the proposed algorithms, namely Decision Tree, Random Forest, and SVM, in efficiently classifying the Eucalyptus agricultural dataset. The results indicate that Random Forest achieved the highest accuracy of 95.84%, surpassing the accuracy of other classifiers. It also exhibited high precision and recall scores of 95%, demonstrating its capability to accurately predict and classify agricultural data. SVM also performed well with an accuracy of 94.64% and high precision and recall scores of 94.6% and 94%, respectively.
These findings highlight the potential of machine learning algorithms in analyzing and categorizing agricultural data, leading to improved decision-making and efficient agricultural strategies. The successful application of these algorithms in the Eucalyptus agricultural dataset showcases their effectiveness in addressing the multi-class classification problem in precision agriculture.
Overall, this research contributes to the field of precision agriculture by providing insights into the performance of various classifiers. The results emphasize the importance of leveraging AI and machine learning techniques to unlock the hidden patterns and relationships within agricultural data, ultimately enhancing agricultural productivity and sustainability.