## 2 Related Work  
Seed classification in smart agriculture has attracted attention as an important research area related to the powerful integration of image processing, computer vision, and machine learning techniques. This integration accelerates the evaluation process, enhances accuracy (minimizing human error and subjectivity) [24], improves the quality of agricultural products, and serves farmers/consumers as well. Recent research highlights the potential benefits of machine learning techniques in the agricultural industry. In 2021, [PERSON] [25] applied Convolutional Neural Networks (CNNs) to identify species of mushrooms. The most successful model was presented with an accuracy of 97.62%. In 2022, [PERSON] et al. [26] conducted a study on a total of 120 images with a 512 \(\times\) 512 resolution from six apple varieties. The experiments were performed with AlexNet and GoogleNet pre-trained models. GoogleNet (91.67%) demonstrated better performance than AlexNet (83.33%). The same year, [PERSON] et al. [27] carried out their study on an image dataset (balanced) of paddy seed varieties. They achieved overall accuracy values of 80%, 83.33%, and 83.33%, respectively, with the VGG16, InceptionV3, and MobileNetV2 pre-trained models. In [28], pistachio images were classified according to their outer shell color. The image dataset was first balanced and experiments were then conducted for different training, validation, and test subsets. AlexNet presented the best results, with an accuracy of 98.44%. In [29], images of five different corn types were classified with various machine learning algorithms. The highest accuracy rates were 96.26% with Multi-Layer Perceptron (MLP) and 96.46% with Support Vector Machine (SVM). In a research study conducted on bread wheat seeds [30], InceptionV3, MobileNetV2, and ResNet-18 produced similar results, with ResNet-18 achieving the highest classification accuracy of 97.7%. [PERSON] et al. [31] employed the InceptionV3, VGG16, and VGG19 models to classify images of dry bean types and achieved a classification success rate of 84.48% with the InceptionV3 model. Secondly, they extracted image features from the layer immediately before the classification layer of each model and then used them as inputs for the Logistic Regression (LR) and SVM algorithms. The images were classified using six different hybrid models, and the best results were obtained using VGG19+LR (83.54%), VGG16+LR (83.71%), and InceptionV3+LR (82.35%). [PERSON] and [PERSON] [6] proposed an Artificial Neural Network (ANN) integrated computer vision system and classified chickpea quality as good, acceptable, or defective. Chickpea seed classification, in particular, presents special challenges and opportunities owing to the variety of shapes, sizes, and colors of chickpea seeds. Researchers aim to accurately classify chickpea seeds utilizing CNNs, pre-trained models, and machine learning methods. In 2019, [PERSON] et al. [1] automatically classified five different types of chickpea seeds using two approaches. In the first approach, chickpea images were segmented, color and texture features were extracted, feature selection was conducted, and classification was performed using a hybrid method called ANN-PSO. In the second approach, patches of chickpea images were directly used as inputs to the ANN. Accuracies of 99.35% and 98.04% were achieved with the ANN and ANN-PSO methods, respectively. In 2021, [PERSON] et al. [32] studied four types of chickpea seeds and used the pre-trained VGG16 model by changing the last three layers in a problem-specific manner. The new model presented an average accuracy of 94.21% as a result of the training. In 2023, [PERSON] et al. [33] performed classification experiments on the images of six certified chickpea (cicer arietinum) seed varieties. Accuracy values of 92.3% (MobileNetV2) and 92.97% (MobileNetV2+LSTM) were achieved. The same year, [PERSON] et al. [34] classified chickpea varieties with NasNet-A, MobileNetV2, and EfficientNet-B0.