Towards robust crop disease detection for complex real field background images

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.

Plant phenotyping relevance

複雑な野外画像から葉を抽出し、画像特徴により健全葉と病害葉を分類する手法が中心で、植物の病害状態を直接推定している。

Code and data availability

The paper uses PlantVillage images (a cited third-party repository, not a paper-specific deposit) and describes cepstral feature extraction and classification, but provides no author code, trained models, or dataset deposit with availability language or URL. No qualifying paper-specific public asset exists.

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