## 2 Materials and Methods
### Feature Extraction: MobileNet Model  
During the feature extraction process, the HSIs were passed into the MobileNet model to generate feature vectors. MobileNet is a CNN-based technique that is extensively applied in classifier procedures. The most important benefit of utilizing the presented method is that the model needs moderately low computation work in comparison with the CNN, which makes it appropriate to operate with a mobile device and a computer that operates with lower computational capabilities. The presented method is a fundamental architecture that combines convolution layers that are applied to efficiently distinguish details according to two controllable attributes that change between parameter precision and potential. The presented method is valuable in diminishing the size of the system. The MobileNet structure is very effective with the least amount of attributes, namely Palmprint detection. This concerns a depth-wise convolution. The fundamental architecture is dependent on discrete abstracted layers, i.e., a module of dissimilar convolution layers that seem to be the quantal structure that measures a typical in-depth complication [19]. The resolution multiplier variable \(\omega\) is added to minimize the measurement of the input dataset and inner layer representation with the analogous variable. Figure 2: Sample images: (**a**) water spinach, (**b**) soybean, (**c**) strawberry, (**d**) corn, (**e**) sesame, and (**f**) broad-leaf soybean. The feature vector map of size \(F_{m}\times F_{m}\), and the filter is of size \(F_{s}\times F_{s}\). The input variable is embodied by \(p\), and the output variable is denoted by \(q\). For the basic abstract layer of the structure, the whole computation work is considered as variable \(c_{e}\), and it could be evaluated as follows:  
\[c_{e}=F_{s}\cdot F_{s}\cdot\omega\cdot\alpha F_{m}\cdot\alpha F_{m}+\omega\cdot \rho\cdot\alpha F_{m}\cdot\alpha F_{m} \tag{1}\]  
The \(\omega\) multiplier value can be considered within one to \(n\). The variable resolution multiplier is known as \(\alpha\). The computational effort is recognized as the variable \(cost_{e}\) and is evaluated by the following equation:  
\[cost_{e}=F_{s}\cdot F_{s}\cdot\omega\cdot\rho\cdot F_{m}\cdot F_{m} \tag{2}\]  
The proposed approach incorporates the pointwise and depth-wise convolutions that are circumscribed by the reduction variable known as the variable \(d\), which is evaluated in the following:  
\[d=\frac{F_{s}\cdot F_{s}\cdot\omega\cdot\alpha F_{m}\cdot\alpha F_{m}+\omega \cdot\rho\cdot\alpha F_{m}\cdot\alpha F_{m}}{F_{s}\cdot\epsilon\cdot\omega \cdot\rho\cdot F_{m}F_{m}} \tag{3}\]  
The two hyper characteristics, resolution and width multipliers, enable changing the optimal window size for accurate prediction based on the context. The third values suggest that it contains three input channels. The principle under the MobileNet structure replaced the complicated convolutional layer, which comprises a convolutional layer with \(3\times 3\) buffers for the input dataset, along with a pointwise convolutional layer of size \(1\times 1\) that combines the filtered variable to construct an element. To optimally tune the hyperparameters related to the MobileNet model, the Adam optimizer is exploited. Furthermore, the hyperparameter optimized by the MobileNetv2 approach utilizes the Adam optimizer. It can be utilized for estimating an adoptive learning value, whereas the parameter was implemented for training the parameter of the DNN approach [20]. It can be a well-designed and effective approach for the 1 st-order gradient with constraints stored for stochastic optimization. At this point, the newly presented approach was utilized to resolve the ML problem with the maximum dimensional parameter space, and the massive data set measures the rate of learning for different features with approximations of 1 st and 2 nd order moments. Additionally, the Adam optimizer was heavily utilized depending upon the gradient descent (GD) and momentum technique and a variety of intervals.