Precision agriculture is a transformative field aimed at maximizing crop
yield while optimizing resource efficiency and sustainability. This study compares
unsupervised learning techniques—Principal Component Analysis (PCA), K-Means,
DBSCAN, and Hierarchical Clustering—for clustering crop data and generating
actionable insights. Using a dataset of 1,000 records with soil properties such as pH,
organic matter, and nutrient concentrations, PCA was applied for dimensionality
reduction to retain variance while improving clustering efficiency. Clustering
performance was evaluated using the Silhouette Score, Calinski-Harabasz Index, and
Davies–Bouldin Index. K-Means performed superior with spherical clusters, DBSCAN
identified outliers and non-globular structures efficiently, and Hierarchical Clustering
showed hierarchical relations of clusters. Using t-SNE improved the visualization and
interpretability. This study assesses the potential of unsupervised learning methods in
precision agriculture to help optimize resources, sustain the environment, and increase
crop yields via soil nutrient information and hotspot cluster monitoring to further plans
to optimize nitrogen applied in the environment. Emerging paradigms like quantum
computing can further accelerate unsupervised learning techniques by enabling faster
and more complex clustering on large-scale agricultural datasets. In the future,
adaptations can include larger datasets, time-series analysis, and raw data diving with
advanced clustering, self-organizing maps, and deep learning models to create scalable,
data-driven decision-making tools for precision agriculture.
Keywords: Agricultural data, Clustering algorithms, Dimensionality reduction, K-means clustering, Machine learning models, Principal component analysis (PCA), Self-organizing maps (SOM), Support vector machines (SVM), SVM for clustering, Time-series analysis, Unsupervised classification, Unsupervised learning, Variable selection.
