## 3 Methodology  
We introduce a novel hybrid methodology for the successful classification of different chickpea seed varieties through this paper. The methodology adopted integrates three tasks: transfer learning for feature extraction, Relieff algorithm for feature selection, and classical machine learning methods for classification. This approach combines the power of transfer learning models in automated feature extraction and the generalization ability of classical machine learning methods. It also incorporates Relieff algorithm to identify/select the most discriminative features and thus provides a more robust and durable consistent structure. Furthermore, this methodology can provide a more effective solution, particularly in agricultural applications with limited training data that have complex traits such as seed varieties, compared to directly using transfer learning models. The utilization of classical machine learning methods instead of the final layers of transfer learning models can reduce the computational cost in the classification stage.  
In this study, four different hybrid models have been developed, and a novel dataset has been created. The dataset includes pre-processed images of eight different chickpea types. ResNet-50, EfficientNet-B0, and DenseNet-201 models are used to extract features from the images. Then, the most relevant features have been selected by Relieff algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Chickpea Varieties** & **Dataset** & **Total** & **Methods** & **Accuracy** \\ \hline
[1] & Adel, Arman, & Balanced & 1020 & ANN+PSO & 99.35 \\  & Azad, Bevanij, & & & ANN & 98.04 \\  & Hashem & & & & \\
[32] & [PERSON], Arman, & Balanced & 400 & VGG16 & 94.21 \\  & Azad, Saral & & & & \\
[33] & Atabey, Aydogan, & Unbalanced & 5192 & MobileNetV2 & 92.3 \\  & Bahadir, [PERSON], & & & MobileNetV2+LSTM & 92.7 \\  & [PERSON], [PERSON] & & & & \\
[34] & Alma, Orion, & Balanced & 2200 & NasNet-A & 96.8 \\  & Consul, Cory & & & MobileNetV2 & 99.9 \\  & & & & EfficientNet-B0 & 98.1 \\
[35] & Jam, ILC, & Balanced & 400 & ANN & 79 \\  & Piroz, Kaka & & & & \\
[36] & Alma, Leader, & Balanced & 2400 & ResNet-50 & 100 \\  & Frontier, Orion, & & & MobileNetV2 & 100 \\  & Palmer, Luna, & & & GoogleNet & 99 \\  & [PERSON] & & & & \\
[37] & Nihatbey, Ciftci, & Balanced & 1500 & CNN-1 & 94 \\  & Sar98, Aslanbey, & & & CNN-2 & 98 \\  & Aksu & & & & \\
[38] & Nihatbey, Ciftci, & Balanced & 1500 & VGG19 & 97 \\  & Sar98, Aslanbey, & & & VGG16 & 96.7 \\  & Aksu & & & & \\ This & Nihatbey, Ciftci, & Balanced & 7200 & TL+SVM & 94.4 \\  & Sari98, Inci, & & & TL+LDA & 94 \\  & Hisar, Aslanbey, & & & & \\  & Akca, Aksu & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of this work with the literature on the classification of chickpea seeds.  
The selected features have been fed into the classifier, for example, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Naive Bayes (NB). Each hybrid model has been called \"TL + classifier name\", where TL stands for transfer learning. The performance of the models has been evaluated using accuracy, recall, F1-score, precision, and AUC metrics. Figure 1 illustrates the sequential steps of this research. Each task is elucidated step by step in this section.