Pre-processing Techniques and Model Aggregation for Plant Disease Prevention

Cédric Marco-Detchart, Jaime Andres Rincon, Vicente Julián, Carlos Carrascosa

Abstract: Image processing techniques have become increasingly popular in plant disease classification. However, one of the major challenges in this field is accurately identifying and classifying different diseases based on plant images. Pre-processing techniques such as smoothing and sharpening can play a crucial role in improving the accuracy of disease classification. These techniques can enhance the quality of the images and reduce noise, making it easier for machine learning algorithms to extract meaningful features from the images. In this context, these techniques can significantly improve the overall accuracy of plant disease classification systems. This paper aims to explore the potential of preprocessing techniques such as smoothing and sharpening in enhancing the quality of plant images for disease classification.