How can AI models like CNNs be optimized for leaf disease detection in crops with similar visual symptoms under different light conditions?
Dear Simranpreet Kaur
In my opinion,To optimize AI models like CNNs for leaf disease detection in crops with similar visual symptoms under varying light conditions, techniques such as data augmentation (e.g., adjusting brightness, contrast, and saturation), normalization (e.g., histogram equalization), and domain adaptation (e.g., using CycleGAN to unify lighting variations) can be employed. Additionally, incorporating attention mechanisms or multi-spectral imaging can help distinguish subtle disease features, while transfer learning from pre-trained models (e.g., ResNet, EfficientNet) fine-tuned on a diverse dataset with annotated lighting conditions can improve robustness. Post-processing methods like ensemble learning or test-time augmentation can further enhance accuracy across different environments.
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