This comparative research examines the effectiveness of two machine learning models built for plant disease classification, emphasizing their merits, shortcomings, and applicability in transfer learning settings. Model 1, recognized for its comprehensive pattern recognition, has a high validation accuracy of 84.76% but suffers from overfitting, as indicated by rising gaps between training and validation accuracy. Model 2, on the other hand, displays computational efficiency and a better generalization capability, with a peak validation accuracy of 83.57%, as well as faster convergence and improved stability in the learning process. In testing settings, Model 2 marginally outperforms Model 1, with an overall accuracy of 92% versus Model 1’s 91%. Both models had nearly identical macro averages for accuracy, recall, and F1-scores, indicating balanced performance across classes. The transfer learning evaluation demonstrates Model 2’s superior adaptability, as it consistently achieves high accuracy rates across different crops: 95% in potatoes, 94% in apples, 93% in corn, and an impressive 96% in bell peppers, demonstrating its ability to generalize to new data. This starkly contrasts with Model 1, which, despite its sophistication, shows symptoms of overfitting and a significant decline in test accuracy across various crops, indicating poor transfer learning ability. To summarize, Model 2 not only provides computational efficiency and a strong defence against overfitting, but it also excels in transfer learning scenarios, making it an excellent choice for applications that require flexibility across varied datasets. This work emphasizes the necessity of choosing models based on specific application requirements and the possibility for additional optimization to improve predicted accuracy in plant disease categorization.