This study focuses on enhancing the classification of problematic soybean seeds using a modified InceptionV3 model with transfer learning techniques. The dataset consists of 5513 images of five classes of soybean seeds: intact, spotted, immature, broken, and skin-damaged. The InceptionV3 architecture is enhanced by adding five supplementary layers, including Average Pooling, Flatten, Dense, Dropout, and Softmax, to improve efficiency and performance. Techniques such as transfer learning, adaptive learning rate adjustment (0.001), and model checkpointing are integrated to optimize accuracy. Initial evaluation showed an accuracy of 88.07% in training and 86.67% in validation. Subsequent model tuning strategies significantly improved performance, achieving an overall accuracy of 98.73%. Evaluation metrics, including precision, recall, and F1-score, demonstrated high effectiveness, with precision ranging from 0.9706 to 1.0000, recall values indicating a high capture rate across all classes, and F1-scores ranging from 0.9851 to 1.0000. Comparative analysis with existing studies revealed competitive accuracy, highlighting the model's potential in soybean seed classification and contributing to advancements in agricultural technology for crop health assessment and management.This study focuses on enhancing the classification of problematic soybean seeds using a modified InceptionV3 model with transfer learning techniques. The dataset consists of 5513 images of five classes of soybean seeds: intact, spotted, immature, broken, and skin-damaged. The InceptionV3 architecture is enhanced by adding five supplementary layers, including Average Pooling, Flatten, Dense, Dropout, and Softmax, to improve efficiency and performance. Techniques such as transfer learning, adaptive learning rate adjustment (0.001), and model checkpointing are integrated to optimize accuracy. Initial evaluation showed an accuracy of 88.07% in training and 86.67% in validation. Subsequent model tuning strategies significantly improved performance, achieving an overall accuracy of 98.73%. Evaluation metrics, including precision, recall, and F1-score, demonstrated high effectiveness, with precision ranging from 0.9706 to 1.0000, recall values indicating a high capture rate across all classes, and F1-scores ranging from 0.9851 to 1.0000. Comparative analysis with existing studies revealed competitive accuracy, highlighting the model's potential in soybean seed classification and contributing to advancements in agricultural technology for crop health assessment and management.