A system for plant disease detection, comprising: an interface module configured to receive an image of a plant, the image including a visual representation of at least one plant element; a color normalization module configured to apply a color constancy method to the received image to generate a color-normalized image; an extractor module configured to extract one or more image portions from the color-normalized image wherein the extracted image portions relate to the at least one plant element; a filtering module configured: to identify one or more clusters (C1 to Cn) by one or more visual features in a plurality of color channels within the extracted image portions wherein each cluster is associated with a plant element portion showing characteristics of a plant disease and different diseases are associated with different color channels; 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 naive Bayes classifier being trained by using multi-color channels as features wherein the naive Bayes classifier models visual feature statistics which are always present on a diseased plant image indicating a particular disease, and, in case no candidate region is determined, to iteratively repeat the filtering with at least a further predefined threshold, the further predefined threshold being less restrictive than the predefined threshold, and, in case still no candidate region is determined when using the predefined thresholds, to determine the cluster with the highest probability as candidate region; and a plant disease diagnosis module configured: to extract, by using a statistical inference, 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 particular disease; and to compute a confidence score for the particular disease by evaluating all the determined probabilities of the candidate regions (C4, C5, C6, Cn) to make a classification decision for one or more diseases. an interface module configured to receive an image of a plant, the image including a visual representation of at least one plant element; a color normalization module configured to apply a color constancy method to the received image to generate a color-normalized image; an extractor module configured to extract one or more image portions from the color-normalized image wherein the extracted image portions relate to the at least one plant element; a filtering module configured: to identify one or more clusters (C1 to Cn) by one or more visual features in a plurality of color channels within the extracted image portions wherein each cluster is associated with a plant element portion showing characteristics of a plant disease and different diseases are associated with different color channels; 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 naive Bayes classifier being trained by using multi-color channels as features wherein the naive Bayes classifier models visual feature statistics which are always present on a diseased plant image indicating a particular disease, and, in case no candidate region is determined, to iteratively repeat the filtering with at least a further predefined threshold, the further predefined threshold being less restrictive than the predefined threshold, and, in case still no candidate region is determined when using the predefined thresholds, to determine the cluster with the highest probability as candidate region; and to identify one or more clusters (C1 to Cn) by one or more visual features in a plurality of color channels within the extracted image portions wherein each cluster is associated with a plant element portion showing characteristics of a plant disease and different diseases are associated with different color channels; 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 naive Bayes classifier being trained by using multi-color channels as features wherein the naive Bayes classifier models visual feature statistics which are always present on a diseased plant image indicating a particular disease, and, in case no candidate region is determined, to iteratively repeat the filtering with at least a further predefined threshold, the further predefined threshold being less restrictive than the predefined threshold, and, in case still no candidate region is determined when using the predefined thresholds, to determine the cluster with the highest probability as candidate region; and a plant disease diagnosis module configured: to extract, by using a statistical inference, 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 particular disease; and to compute a confidence score for the particular disease by evaluating all the determined probabilities of the candidate regions (C4, C5, C6, Cn) to make a classification decision for one or more diseases. to extract, by using a statistical inference, 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 particular disease; and to compute a confidence score for the particular disease by evaluating all the determined probabilities of the candidate regions (C4, C5, C6, Cn) to make a classification decision for one or more diseases.