This research paper proposes an enhanced soybean seed classification model based on the InceptionV3 architecture, utilizing transfer learning and model tuning techniques to improve accuracy and performance. The study focuses on classifying five types of problematic soybean seeds: intact, spotted, immature, broken, and skin-damaged, using a dataset of 5513 images. The InceptionV3 model is modified by adding five additional layers—Average Pooling, Flatten, Dense, Dropout, and Softmax—before the classification layer to enhance efficiency and performance. Techniques such as transfer learning, adaptive learning rate adjustment (set to 0.001), and model checkpointing are integrated to optimize accuracy and prevent overfitting. The model achieves an overall accuracy of 98.73% during testing, with precision, recall, and F1-score values ranging from 0.9706 to 1.0000 across all classes. The model's performance is compared with existing studies, demonstrating competitive accuracy and effectiveness in soybean seed classification. The study highlights the potential of deep learning in agricultural applications, particularly in crop health assessment and management. The proposed model contributes to advancements in agricultural technology by providing a reliable and efficient solution for identifying problematic soybean seeds. The research underscores the importance of model tuning and architecture optimization in achieving high accuracy in image classification tasks. The findings suggest that the proposed model is a promising approach for improving soybean seed classification, with potential applications in precision agriculture.This research paper proposes an enhanced soybean seed classification model based on the InceptionV3 architecture, utilizing transfer learning and model tuning techniques to improve accuracy and performance. The study focuses on classifying five types of problematic soybean seeds: intact, spotted, immature, broken, and skin-damaged, using a dataset of 5513 images. The InceptionV3 model is modified by adding five additional layers—Average Pooling, Flatten, Dense, Dropout, and Softmax—before the classification layer to enhance efficiency and performance. Techniques such as transfer learning, adaptive learning rate adjustment (set to 0.001), and model checkpointing are integrated to optimize accuracy and prevent overfitting. The model achieves an overall accuracy of 98.73% during testing, with precision, recall, and F1-score values ranging from 0.9706 to 1.0000 across all classes. The model's performance is compared with existing studies, demonstrating competitive accuracy and effectiveness in soybean seed classification. The study highlights the potential of deep learning in agricultural applications, particularly in crop health assessment and management. The proposed model contributes to advancements in agricultural technology by providing a reliable and efficient solution for identifying problematic soybean seeds. The research underscores the importance of model tuning and architecture optimization in achieving high accuracy in image classification tasks. The findings suggest that the proposed model is a promising approach for improving soybean seed classification, with potential applications in precision agriculture.