A Mathematical Analysis of Feature Extraction Methodologies with Application on Leaf Disease Detection

Abstract

Akshat Kishore

This study compares novel and established feature extraction techniques for classifying leaf diseases. Conventional methods like Transfer Learning and DCT are contrasted with novel techniques like Segmentation using a heterogeneous data set of healthy and diseased leaf images. With little training data, transfer learning is beneficial, but with large data, more efficient algorithms may be more suitable, such as the custom segmentation model described in this work. The development of efficient plant disease detection systems using the information from these discoveries would improve agricultural practises and increase food security.

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