System and method for detecting plant diseases

Abstract

A system (100), method and computer program product for determining plant diseases. The system includes an interface module (110) configured to receive an image (10) of a plant, the image (10) including a visual representation (11)of at least one plant element (1). A color normalization module (120) is configured to apply a color constancy method to the received image (10) to generate a color-normalized image. An extractor module (130) is configured to extract one or more image portions (11e) from the color-normalized image wherein the extracted image portions (11e) correspond to the at leastone plant element (1). A filtering module (140) configured: to identify one or more clusters (C1 to Cn) by one or more visual features within the extracted image portions (11e) wherein each cluster isassociated with a plant element portion showing characteristics of a plant disease; and to filter one or more candidate regions from the identified one or more clusters (C1 to Cn) according to a predefined threshold, by using a Bayes classifier that models visual feature statistics which are always present on a diseased plant image. A plant disease diagnosis module (150) configured to extract, byusing a statistical inference method, from each candidate region (C4, C5, C6, Cn) one or more visual features to determine for each candidate region one or more probabilities indicating a particulardisease; and to compute a confidence score (CS1) for the particular disease by evaluating all determined probabilities of the candidate regions (C4, C5, C6, Cn).

Description

technical field [0001] The present invention relates generally to the determination of plant diseases, and more particularly to image-based diagnosis of plant diseases supported by statistical inference methods. Background technique [0002] Some plant diseases show disease-specific indicators on the surface of plant leaves. For example, fungal diseases such as early fungal diseases (for example, Septoria, S. tritici and S. nodorum), late fungal diseases (for example, wheat rust ( Fungal diseases of Wheat Rusts) and Helminthosporium often cause changes in plant leaves that show disease-specific spots or spots that can be analyzed visually. [0003] Computerized visual diagnostic methods have been proposed in the past. For example, the paper "Leaf Disease Grading by Machine Vision and Fuzzy Logic" (Arun Kumar R et al. in Int. J. Comp. Tech. Appl. Vol. A method for automatic grading of diseases. The proposed system uses image processing techniques to analyze color-specific...

AI Technical Summary

Problems solved by technology

However, the disclosed solutions do not address how to determine the global presence of a particular disease

Instead, several candidates are detected and analyzed separately, which may be less reliable

Furthermore, the disclosed disease analysis is less robust to light conditions when recording plant images of diseased plants, since it does not allow lighting and / or photographic changes to the recorded images and adapt to them

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