## 1 Introduction  
Rapid developments in smart agriculture are redefining how modern farming addresses the challenges of resource optimization and sustainability [1, 2]. With the growing global demand for food, precision agriculture has become visible as a key solution, leveraging advanced technologies to improve productivity and efficiency [3, 4]. Imagery data analysis is essential in this domain, as it allows one to perform activities such as weed identification, disease detection, and fruit quality evaluation [5, 6]. Imagery data are also used to address agricultural challenges of land use and land cover change [7]. These analytical capabilities give farmers detailed information that helps them make informed decisions. This can help in improving crop yields and economic outcomes [8].  
Smart agriculture now relies heavily on machine learning (ML), which provides strong capabilities for the processing of vast amounts of complex agricultural data [2]. Deep learning models have shown impressive effectiveness in processing visual data, identifying patterns, and producing classification results that are quite precise [9]. These advances enable diverse applications, from pest and disease detection [10] to resource management [11]. Although ML models have great potential, their performance is highly dependent on the quality of input features, particularly when working with high-dimensional imagery datasets.  
For machine learning implementations, feature selection is an essential step that provides the most relevant and important characteristics from data that can used for model training [12]. It improves the robustness and efficiency of classification models by reducing the overfitting risk and decreasing computational complexity [13]. Feature selection is essential for producing consistent and comprehensible findings in agricultural applications, as datasets frequently show diversity in class distributions and resolution.  
By presenting a unique hybrid feature selection algorithm, the Hybrid Predator Algorithm for Classification (HPA-C), and a customized Convolutional Neural Network (CNN), this study addresses the issues of smart agricultural classification. HPA-C combines a basic framework with position update equations modeled after a variety of natural activities, including cooperative hunting, foraging, and echolocation, integrating different optimization strategies.This special hybridization allows for better feature selection by improving the algorithm's exploration and exploitation capabilities. By applying these techniques to smart agricultural datasets (dataset details in Section 3.1), the study demonstrates significant improvements in classification accuracy within the 98.6% to 99.8% range. The outcomes directly benefit farmers by providing actionable insights for sustainable and efficient farming practices.  
The primary contributions of this work are described as follows:  
1. A hybrid algorithm named HPA-C, combining GWO, ABC, and BAT, is presented to provide important features.
2. A custom CNN architecture is developed for classification tasks in agricultural imagery datasets.
3. The proposed methods are empirically validated and demonstrate superior performance compared to benchmark approaches.  
The remainder of this paper is organized as follows: Background and related work are covered in Section 2. Section 3 details the datasets and algorithms used in this study. The proposed framework is described in Section 4. Simulation results and statistical analyses are provided in Section 5, and the paper concludes with insights and future directions in Section 6.