An Exploratory Approach to Multi-Class Classification of Agricultural Data using AI
Abstract
Agricultural production and operations generate vast amounts of data, harboring crucial information. Data mining technology can analyze the relationships between various variables within the extensive agricultural dataset. Classification prediction is one of the most significant data mining techniques for agricultural data. This paper presents an exploratory study utilizing three AI algorithms: Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) to address the challenges of multi-class classification in agriculture. The algorithms were tested on the Eucalyptus standard agricultural multi-class dataset, and the results showed that the MLP method performed exceptionally well, achieving a significant increase in classification accuracy for the Eucalyptus dataset, with a precision of 89.97%.
Keywords
Download Options
Introduction
Machine learning algorithms are essential processes or sets of techniques that allow a model to adapt to the provided data with a specific objective. Applying machine learning to modern agricultural production can lead to the improvement of precision agriculture, automation, and intelligent agricultural production. In the real agricultural production process, the application of computer-related information technology in precision agriculture has become increasingly widespread, leading to the collection of vast amounts of natural and spatial data closely associated with the precision agriculture process. Extracting hidden relationships from this immense agricultural production data, enabling accurate agricultural strategies, and guiding efficient agricultural production have become critical and urgent challenges. Among the different tasks involved in mining valuable information from agricultural data, classification is often the most critical phase, particularly in the context of precision agriculture.
Conclusion
In conclusion, this exploratory approach to multi-class classification of agricultural data using AI showcases the potential of machine learning algorithms, particularly MLP, in enhancing precision agriculture and intelligent agricultural production. The study provides valuable insights into the effective utilization of AI for handling large and diverse agricultural datasets, facilitating better decision-making and strategy formulation in the agricultural domain. Further research and experimentation with different algorithms and datasets can contribute to the continuous improvement of precision agriculture and the optimization of agricultural production systems.