Two-Dimensional Discriminant Locality Preserving Projections for Crop Leaf Disease Detection

Volume 4 - Issue 2

Ping Li Hao Yuan Qingqing Zhang*

Received: November 11, 2019;   Published: December 02, 2019

DOI: 10.32474/OAJESS.2019.04.000182

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Abstract

There are many kinds of crop diseases, which directly affect the yield and quality of crops and cause immeasurable losses. Using image processing and pattern recognition technology, it is simple and fast to identify crop diseases and provide necessary information for taking prevention measures in time. A crop disease recognition method is proposed based on two-dimensional discriminant locality preserving projections (2D-DLPP). 2D-DLPP tries to find a mapping matrix to reduce the dimensionality of the original diseased leaf images, so that the intra-class samples in low-dimensional mapping subspace are closer to each other, while the inter-class samples are far from each other, which can improve the recognition rate of the algorithm. The experiments on the common cucumber disease leaf image dataset are carried on and compared with other plant disease recognition algorithms. The results show that the 2D-DLPP based method is effective and feasible for crop disease identification.

Keywords:Crop disease identification; Dimensional reduction; Discriminant locality preserving projections (DLPP); Twodimensional DLPP (2D-DLPP)

Abstract| Introduction| 2D-DLPP| Experiments and analysis| Conclusion| Acknowledgments| References|