## 3 Methods  
### Dataset Description  
With a particular focus on smart agriculture, this study integrates nine different RGB image datasets from various fields. These datasets address a range of classification difficulties with differences in class count, image distribution, resolution, and scope. Class annotations are applied to all datasets, and the effects of class balance are not taken into consideration for the current study. The datasets are summarized in Table 2.  
The datasets illustrate the development of image categorization research from 2010 to 2024. The purpose of this work is to gain insights into smart agricultural issues by utilizing various labeled datasets, although some datasets are naturally balanced and others are imbalanced. Incorporating additional resources for broader insight while concentrating on datasets directly related to agriculture, this study highlights the potential of data-driven solutions to improve agricultural production and decision making.  
Dataset Diversity and Generalization  
The datasets utilized in this study were carefully selected to cover a broad spectrum of challenges in smart agriculture. This selection ensures a diverse and representative evaluation of the proposed HPA-C method.  
In terms of dataset characteristics, our selection includes variations in resolution, image quality, class distributions, and environmental conditions. These make the evaluation more robust. The plant disease and weed detection datasets use field conditions with real-world variations in lighting, background, and plant health status. The fruit classification datasets further introduce controlled yet challenging classification tasks due to similarities between different fruit categories. The remote sensing datasets contribute large-scale imagery variations, demonstrating the applicability of the method beyond agricultural tasks.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **\# Classes** & \begin{tabular}{l} **Images per** \\ **Class** \\ \end{tabular} & \begin{tabular}{l} **Total** \\ **Images** \\ \end{tabular} & \begin{tabular}{l} **Resolution** \\ **Imbalanced** \\ \end{tabular} & \begin{tabular}{l} **Scope** \\ \end{tabular} & \begin{tabular}{l} **Version** \\ \end{tabular} \\ \hline Plant Leaf & 39 & \(\sim\)1000 & 54,306 & \begin{tabular}{l} Variable \\ (256 \(\times\) 256 \\ common) \\ \end{tabular} & Imbalanced & \begin{tabular}{l} Plant disease \\ detection \\ \end{tabular} & 2019 \\ \hline Weed Detection in & 12 & \(\sim\)(1191 to 7356) & 15,336 & Variable & Imbalanced & \begin{tabular}{l} Weed \\ classification \\ \end{tabular} & 2017 \\ \hline Fruits-360 [33] & 141 & \(\sim\)490 & 94,110 & \(100\times 100\) & Balanced & \begin{tabular}{l} Fruit \\ recognition \\ \end{tabular} & 2024 \\ \hline Fresh n Rotten [34] & 16 & \(\sim\)740 & 12,335 & \(1020\times 1020\) & Balanced & \begin{tabular}{l} Food freshness \\ \end{tabular} & 2018 \\ \hline UC MERCED [35] & 21 & 100 & 2100 & 256 \(\times\) 256 & Balanced & \begin{tabular}{l} Scene \\ classification \\ \end{tabular} & 2010 \\ \hline AID [36] & 30 & \(\sim\)(220 to 400) & 10,000 & \begin{tabular}{l} Variable \\ (600 \(\times\) 600 \\ common) \\ \end{tabular} & Imbalanced & Aerial imagery & 2017 \\ \hline NWPU- & 45 & \(\sim\)700 & 31,500 & \
\begin{tabular}{l} Variable \\ (256 \(\times\) 256 \\ common) \\ \end{tabular} & Balanced & Remote sensing & 2023 \\ \hline SIRI-WHU [38] & 12 & 200 & 2400 & \(200\times 200\) & Balanced & Remote sensing & 2016 \\ \hline RSSCN7 [39] & 7 & 400 & 2800 & \(224\times 224\) & Balanced &
\begin{tabular}{l} Scene \\ classification \\ \end{tabular} & 2015 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of datasets used in research.  
The results indicate strong generalization across these diverse datasets, but we also acknowledge that additional validation on other agricultural datasets, such as multi-season imagery, hyperspectral data, or multi-modal datasets, could further strengthen the conclusions.