Hyperparameter Optimization for Leaf Infection Segmentation using Ensemble Techniques

Crop production is the primary source of supplying food to humans. But the process of crop production and increasing yield completely relies on the early identification of diseases. Machine learning and deep learning models are proven to be the most effective while identifying the leaf diseases at the earlier stage. But the models can also become inefficient in detecting the correct disease class due to the improper selection of hyperparameters during model development. The present study presents a novel approach for hyperparameter optimization in leaf disease segmentation, utilizing integration techniques. During experimentation it is observed that the proposed method enhances the precision of leaf disease diagnosis using the application of machine learning algorithms. By optimizing hyperparameters and incorporating integration methods, the model's robustness and segmentation accuracy are significantly improved. The proposed work accurately identifies diseased regions on leaves, ensuring the model's efficacy across various conditions. This approach advances agricultural technology by providing early detection tools for plant diseases, thereby supporting food security and improving agricultural outcomes.