The ISD4L (In-field Small Data Disease Detection with Deep Learning) methodology can be adapted to detect other crop diseases by following a systematic approach. First, the methodology can be applied to different crops by creating high-resolution image datasets specific to those crops, similar to the TelevitisPotatoDiseases dataset used for potato late blight. For instance, crops like grapevines, tomatoes, or wheat could be targeted, each requiring tailored datasets that capture the unique symptoms of diseases affecting those plants.
Next, the data augmentation techniques employed in ISD4L can be modified to account for the specific characteristics of the new crops. This includes adjusting the patch extraction process to focus on the relevant features of the new crop images, such as leaf shape, color variations, and symptom size. The focal loss function can also be fine-tuned to address the class imbalance that may arise with different diseases, ensuring that the model remains sensitive to the early symptoms of the new diseases.
Furthermore, the integration of domain knowledge about the specific diseases affecting the new crops can enhance the model's training process. This could involve incorporating expert annotations or using transfer learning from models trained on similar diseases to improve the initial performance of the model on the new crop datasets. By leveraging these strategies, the ISD4L methodology can be effectively extended to detect a wide range of crop diseases, thereby enhancing agricultural productivity and sustainability.