Computer-implemented method for providing training data for a machine learning algorithm for classifying plants infested with a pathogen
Computer-implemented method for providing training data for a machine learning algorithm for image classifying a pathogen infestation of a plant, comprising the steps: providing image data of a plant or a plant part infested with a pathogen; providing genetic result data of the plant or the plant part to which the image data referred comprising at least information about the type of pathogen; labeling the image data with the genetic result data.
1 . A computer-implemented method for generating training data for a machine learning model for image-based detection of a pathogen infestation of a plant, the method comprising:
providing image data of a plant or a plant part infested with a pathogen;
performing DNA and/or RNA analysis to obtain genetic result data;
providing the genetic result data of the plant or the plant part, the genetic result data comprising information about a type of pathogen, the type including at least one of species, subspecies, variety, biotype, or mutant; and
labeling the image data with the genetic result data, wherein the labeling includes assigning, to the image data, a label representing the type of pathogen based on the genetic result data.
2 . The method according to claim 1 , wherein the genetic result data further comprises information on a quantity of the pathogen.
3 . The method according to claim 1 , wherein the image data is based on at least one RGB-image of the plant or the plant part, wherein the at least one RGB-image is taken against a white background.
4 . The method according to claim 1 , wherein the image data is based on:
at least one multispectral image, in a radiation range between 390 nm and 850 nm, in a near infrared range with 800 nm and in a far infrared range (FIR) with 820 nm, wherein the at least one multispectral image is taken against a white background; and/or
at least one hyperspectral image in a radiation range between 390 nm and 1650 nm.
5 . The method according to claim 1 , wherein the method further comprises the step of filtering the images, trimming and/or marking specific areas of the images before providing the image data.
6 . The method according to claim 1 , wherein the genetic result data is based on a quantitative DNA-analysis tool.
7 . The method according to claim 1 , wherein the pathogen is a fungus.
8 . The method according to claim 1 , wherein the plant is a soybean plant or a part of a soybean plant and the pathogen is a Phakopsora pachyrhizi fungus.
9 . A method comprising providing image data of a plant or a plant part infested with a pathogen for use in the computer-implemented method according to claim 1 .
10 . A method comprising providing genetic result data of a plant or a plant part obtained via DNA and/or RNA analysis, the genetic result data comprising information about a type of pathogen, for use in the computer-implemented method according to claim 1 .
11 . A method comprising training a machine learning model using training data generated according to the method according to claim 1 , wherein the model is configured for image-based detection of pathogen infestation of a plant.
12 . A non-transitory computer-readable medium storing a neural network trained with training data generated according to claim 1 , wherein training features for the training of the neural network comprise a Normalized Differences Vegetation Index (NDVI) and a Greeness Index (G).
13 . A classification system for image classifying a pathogen infestation of a plant, the system comprising:
processing circuitry configured to:
provide image data of a plant or plant part;
feed forward a machine learning algorithm with the image data of a plant or plant part, wherein the machine learning algorithm is trained on the basis of training data provided according to claim 1 ; and
output a classification result of a pathogen infestation of the plant.
14 . A computer implemented method for image classifying a pathogen infestation of a plant, comprising:
providing image data of a plant or plant part;
feeding the machine learning model with the image data of a plant or plant part, wherein the machine learning model is trained on the basis of training data provided according to claim 1 ; and
outputting a classification result of a pathogen infestation of the plant.
15 . The method according to claim 14 , further comprising providing recommendation data for treating the infested plants with an active ingredient and/or a crop protection product suitable for the classified pathogen infestation of the plant.
16 . A non-transitory computer-readable medium having instructions encoded thereon that, when executed by a processor, cause the processor to carry out the method of claim 14 .
17 . The method according to claim 1 , wherein the performing DNA and/or RNA analysis to obtain the genetic result data is carried out in-situ.