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1. WO2020112013 - PROCÉDÉS, MODÈLES ET SYSTÈMES DE PRÉDICTION DE LA ROUILLE JAUNE DANS DES CULTURES DE BLÉ

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CLAIMS

1. A method of constructing a model, preferably a Random Forest model, for predicting the presence of Yellow Rust in wheat crops, comprising the steps of:

i. obtaining, for a plurality of sample wheat plots, a

plurality of values for first, second and third vegetation indices VI, V2 , V3, wherein

- the first vegetation index VI is defined by Vl= ( R53i±ionm

- R570+10nm ) / (R531+10nm + R570±10nm) ,

- the second vegetation index V2 is defined by V2=V1* (- 1 ) / (V4*R7oo+ionm /R67o+ionm) , wherein V4 is defined by V4= ( RsotVlOnm - R670+10nm) / ( RsOO+lOnm + R670±10nm) and

- the third vegetation index V3 is defined by V3= ( R734±ionm

- R747+10nm) / ( R715±10nm + R726±10nm)

wherein Rx is the wheat canopy reflectance at the wavelength X nm,

ii. obtaining, from the plurality of sample wheat plots, a

plurality of Yellow Rust scores, the score for each sample wheat plot specifying the presence of Yellow Rust in that sample wheat plot, and

iii. constructing or calculating a model associating the

plurality of values for the vegetation indices VI, V2 and V3 with the plurality of Yellow Rust scores.

2. The method according to claim 1, wherein the first vegetation index VI is PRI, Photochemical Reflectance Index, the second vegetation index V2 is PRI norm, Renormalized Difference

Vegetation Index, and the third vegetation index V3 is

Vogelmann2, Vogelmann indices 2.

3. The method according to any of claims 1 or 2, wherein no other vegetation indices than the vegetation indices VI, V2 and V3 are used in step iii for constructing or calculating the model, and wherein preferably no other vegetation indices than the vegetation indices VI, V2 and V3 are obtained in step i.

4. The method according to any of the claims 1-3, further comprising the step of normalizing the plurality of values for the first, second and third vegetation indices VI, V2 , and V3 prior to constructing the model in step iii.

5. The method according to any of the preceding claims, wherein the wheat is bread wheat ( Triticum aestivum L.) .

6. A method of predicting the presence of Yellow Rust in a wheat plant or wheat plot, comprising the steps of:

i. Obtaining, for the wheat plant or the wheat plot values for first, second and third vegetation indices VI, V2, V3, wherein

a. the first vegetation index VI is defined by

Vl= ( R531+10nm - R570±10nm) / ( R531±10nm + R570±10nm) , b. the second vegetation index V2 is defined by V2=V1* (- 1 ) / (V4*R7oo+ionm /R67o+ionm), wherein V4 is defined by

V4= ( RsotVlOnm - R670±10nm) / ( RsOO+lOnm + R670±10nm) and

c. the third vegetation index V3 is defined by

V3= ( R734+10nm - R747±10nm) / ( R715±10nm + R726±10nm)

wherein Rx is the wheat canopy reflectance at the wavelength X nm, and

ii. predicting the presence of Yellow Rust in the wheat plant or wheat plot by subjecting the values for the first, second and third vegetation indices VI, V2 , V3 to the model obtained by the method according to any of the claims 1-5.

7. The method according to claim 6, wherein the method further comprises performing the method according to any of the claims 1-5.

8. The method according to any of claims 6-7, wherein the first, second and third vegetation indices VI, V2 , V3 are obtained using at least one sensor, the at least one sensor being capable of detecting light in the wavelength range of 520-810 nm, the at least one sensor preferably only being capable of detecting light in the wavelength ranges needed to determine the first, second and third vegetation indices VI, V2 and V3.

9. A method of breeding wheat, comprising the steps of

i. predicting the presence of Yellow Rust in a plurality of wheat plants or wheat plots using the method according to any of the claims 6-8,

ii. selecting at least one wheat plant or wheat plot predicted to be free of Yellow Rust, and

iii. further breeding the at least one wheat plant or wheat

plot .

10. A system (10) for predicting the presence of Yellow Rust in wheat crops, the system comprising:

- at least one sensor module (12) capable of measuring reflected light from a wheat canopy in the wavelength range of 520-810 nm, - a computation module (16) configured to provide first, second and third vegetation indices VI, V2, V3, wherein

the first vegetation index VI is defined by Vl= ( R53i±ionm

- R570+10nm ) / (R531+10nm + R570±10nm) ,

the second vegetation index V2 is defined by V2=V1* (- 1 ) / (V4*R7oo+ionm /R67o+ionm), wherein V4 is defined by V4= ( RsotVlOnm - R670±10nm) / ( RsOO+lOnm + R670±10nm), and the third vegetation index V3 is defined by V3= ( R734±ionm

- R747+10nm) / ( R715±10nm + R726±10nm) ,

wherein Rx is the wheat canopy reflectance at the wavelength X nm,

based on the reflected light measured by the at least one sensor module ,

- a model module (20) comprising a model, preferably a Random Forest model, associating a plurality of values for the

vegetation indices VI, V2 and V3 for a plurality of sample wheat plots with a plurality of Yellow Rust scores for the plurality of sample wheat plots,

wherein the model module and/or the computation module is further configured to subject the first, second and third vegetation indices VI, V2, V3 to the model to obtain a

prediction of the presence of Yellow Rust.

11. A computer program product for being used in the system according to claim 10, wherein the computer program product comprises program code instructions configured to, when executed by the computation module and/or model module of the system, cause the computation module and/or model module to perform the method according to any of the claims 6 to 8.