Towards robust crop disease detection for complex real field background images

Radhika Bhagwat; Yogesh Dandawate

doi:10.15625/2525-2518/18914

Author affiliations

Authors

Radhika Bhagwat Department of Technology, Savitribai Phule Pune University, Pune, Maharashtra, India, 411007 https://orcid.org/0000-0001-5127-7980
Yogesh Dandawate Electronics and Telecommunication Engineering, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India, 411048

DOI:

https://doi.org/10.15625/2525-2518/18914

Keywords:

Crop disease detection, Machine Learning, Image Segmentation, Feature Extraction, Cepstral Coefficients

Abstract

Most of the work done in image processing-based crop disease detection focuses on images with plain background. This paper presents a technique for crop disease detection for complex real field background images. A segmentation technique is presented to extract leaf patches from the entire image. Transform domain cepstral analysis is proposed for obtaining cepstral coefficients, to attain two level classifications. The first level classifies the crop species while the second level classifies the species into healthy leaf or leaf with specific type of disease. The work is tested on three crops Banana, Soybean and Grape and is checked on plain background laboratory images and on complex real field images. Suggested technique give species level accuracy of 94.33 %, 94.11 % and 98.44 % and disease level average accuracy of 97.75 %, 96.66 % and 97.95 % for Banana, Soybean and Grape, respectively. Comparison with standard features like texture and shape indicate that the presented technique gives the best results for both plain and complex background images suggesting its utilization in crop disease detection to reduce the agricultural and economic losses.

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