Smart agriculture: deep transfer learning frameworks for high-accuracy cotton crop disease classification

In recent years, the agricultural industry has seen significant progress in precision and accuracy, due to the remarkable improvement and development of new artificial intelligence (AI), machine learning (ML), and deep learning algorithms especially for monitoring the crop health in cotton industry. However, previous research has some limitations in terms of generalizability and computational cost. These challenges have been addressed by considering the top ten deep learning frameworks such as VGG16, VGG19, InceptionV3, Xception, ResNet50, MobileNetV2, ResNet101, ResNet152, ShuffleNetV2 and EfficientNetB0 leveraging transfer learning for high-accuracy classification of cotton leaf health conditions. Utilizing the field-based SAR-CLD-2024 dataset comprising 9,137 standardized images across seven distinct leaf condition categories. In this research, we systematically examines the tradeoffs between static architectural complexity, dynamic training convergence, and generalization performance. Experimental results demonstrate that structural design and optimization strategies significantly affect diagnostic effectiveness, even within a highly imbalanced real-world test environment. Among the models tested, EfficientNetB0 achieved the highest mean accuracy of 91.29%, while ResNet50 and ResNet101 achieved strong mean accuracies of 90.48% and 89.55%, respectively. Conversely, VGG19 yielded the lowest overall accuracy of 57.63%, indicating a clear tradeoff between architectural simplicity and representation capacity for multi-class agricultural tasks. Furthermore, confusion matrix and qualitative analyses highlight that early stopping was essential for mitigating overfitting in deeper networks. While features such as herbicide damage and leaf variegation were readily captured, complicated color and textural overlays correlated with higher misclassification vulnerability in shallower models compared to deeper residual architectures. Overall, this work provides valuable insights for developing edge-oriented smart agriculture diagnostic systems withlow resource consumption.