Deep Learning Approach for Seed Disease Prediction Using VGG16 with Transfer Learning and Optuna Optimization

Early detection of seed diseases helps produce healthy crops and create less agricultural loss. Seed disease detection methodologies currently in existence are time-consuming, labour-intensive, and heavily dependent on expert knowledge. Traditional techniques under threat of being unavailable for large-scale agricultural practices. They do not avoid challenges and quick and accurate results hindering their timely interventions. These methods also lack scalability and are poorly adaptable to diverse datasets or indeed of the complexity of disease patterns. Hence, there is a necessity of automating more efficient solutions on scale and thus bring the much-needed shift in seed disease detection from early on. This research proposes a model that makes efficient seed disease prediction using VGG16 architecture under transfer learning and Optuna optimization. The VGG16 model was fine-tuned with trained data from big datasets toward the classification of such seed diseases as fungal and bacterial infections. For optimal performance, Optuna hyperparameter optimization was applied to fine-tune parameters such as learning rate, batch size, and training epochs. Among key performance metrics are accuracy, recall, precision, and F1-score, which would establish the effectiveness of the model in classifying healthy to diseased seeds. The ROC curve yielded high true positive and low false positive, indicating model robustness and adaptability for real-world purposes in agriculture. Overall, this results into accurate, quick, and scalable approaches toward early seed disease detection, which will contribute more to good management of crops and food security