SeedNet: A Lightweight Fine-Tuned Deep Learning Framework for Plant Seedling Classification in Precision Agriculture

Developed a lightweight deep learning framework, EfficientNetB0, based on EfficientNet model, with transfer learning and selective fine-tuning to classify visually similar plant seedlings with accuracy. The proposed two-stage training technique is effective for domain adaptation and generalization, and achieves 95.40% test accuracy, 94.54% Macro-F1, and 99.90% micro-ROC-AUC on the V2 Plant Seedlings Dataset, without compromising the computation efficiency. The work highlights the possibility of practical and automated weed identification for precision sustainable agriculture.