Improving picture quality is essential to raising the rate of disease detection. Compared to noisy photos, high-quality images have a higher detection rate. The chaotic images are more challenging to extract features from, which makes it harder to detect illnesses in tea leaves. Before extracting characteristics from the image, pre-processing is carried out to address issues brought on by the low quality of the image. In order to detect diseases in tea leaves, the present work examine the impact of pre-processing before the feature extraction procedure. Additionally, this work also provides a qualitative explanation for the several feature extraction strategies and pre-processing methods used for improving the image quality for disease detection.