## 3 Result and Discussion
### Computational Efficiency  
The main target of this research was to create a new lightweight model to enhance its implementation potential in real-life scenarios. This section focuses on assessing the computational cost and efficiency of the proposed model using key metrics, including number of parameters [46], computational complexity [47], memory footprint [48], and inference speed [49]. Analyzing these aspects helps determine the model's suitability for deployment in resource-constrained environments and its overall performance efficiency compared to other models. This evaluation identifies the trade-offs between accuracy and computational requirements, ensuring that the model not only performs well but is also computationally feasible for practical use.  
The result in Table 4 shows the comparison of computational efficiency across different models, which reveals notable differences in parameters, memory usage, inference speed, and FLOPs, highlighting the strengths and weaknesses of each.  
VGG-16 and ResNet-50, though accurate, are computationally intensive and slow. VGG-16 has the longest inference speed (6.33 ms) and the highest FLOPs (2507.7 Mega). ResNet-50 has the highest parameters (24.64 million) and memory usage (93.99 MB), making it memory-expensive and less ideal for efficient applications.  
In contrast, MobileNetV2 is highly efficient with 2.91 million parameters, 11.12 MB memory usage, the fastest inference speed (0.77 ms), and low FLOPs (50.22 Mega). This balance makes it suitable for real-time applications on limited hardware.  
The MobileNetV2-LSTM hybrid adds sequence learning while maintaining efficiency. It has a slightly slower inference speed (0.89 ms) but fewer parameters (2.60 million), lower memory usage (9.93 MB), and the lowest FLOPs (49.60 Mega), enhanced by its capability to handle sequential data efficiently, showing its great potential as applied in previous studies [15; 17].  
The primary goal of this project was to reduce the number of parameters and memory usage of the model, addressing the key challenges faced by complex models when deployed in IoT applications [50]. The MobileNetV2-CfC-NCP model successfully meets this target, making it particularly well-suited for deployment on low-cost IoT devices. With the fewest parameters (2.48 million) and the lowest memory usage (9.45 MB) among the models compared, it is ideal for environments with limited computational resources. Although its inference speed (0.80 ms) is slightly higher than that of the basic MobileNetV2 model, this  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Parameters** & \begin{tabular}{c} **Memory** \\ **(Mega Bytes)** \\ \end{tabular} & \
\begin{tabular}{c} **Inference** \\ **Speed (ms)** \\ \end{tabular} &
\begin{tabular}{c} **FLOPs (Mega)** \\ \end{tabular} \\ \hline VGG-16 & 14,978,379 & 57.14 & 6.33 & 2507.7 \\ ResNet-50 & 24,637,835 & 93.99 & 3.14 & 633.1 \\ MobileNetV2 & 2,914,891 & 11.12 & 0.77 & 50.22 \\ MobileNetV2-LSTM & 2,603,019 & 9.93 & 0.89 & 49.60 \\ MobileNetV2-CfC-NCP & 2,475,959 & 9.45 & 0.80 & 49.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational efficiency comparison of the baseline and proposed models.  
minor increase in inference time is not a significant drawback. The ability to process images directly on the device, rather than sending data to a cloud server, compensates for the slight speed difference. Additionally, the model achieves a low FLOPs value of 49.71 Mega, well within the operational requirements for most current IoT devices.  
In summary, this section demonstrates that the proposed model is the most computationally efficient, showcasing significant improvements in performance. This is achieved through the integration of advanced neural network components like CfC and NCP, which play a crucial role in enhancing the effectiveness of agricultural image classification tasks.