US 12,332,167 B2
Method for assessing nitrogen nutritional status in plants by visible-to-shortwave infrared reflectance spectroscopy of carbohydrates
Tarin Paz-Kagan, Meitar (IL); Or Sperling, Beit Kama (IL); Ze'ev Schmilovitch, Yehud (IL); Uri Yermiyahu, Yavne (IL); and Tal Rapaport, Lehavim (IL)
Assigned to THE STATE OF ISRAEL, MINISTRY OF AGRICULTURE & RURAL DEVELOPMENT, AGRICULTURAL RESEARCH ORGANIZATION (ARO) (VOLCANI CENTER), Rishon Lezion (IL)
Appl. No. 17/801,868
Filed by THE STATE OF ISRAEL, MINISTRY OF AGRICULTURE & RURAL DEVELOPMENT, AGRICULTURAL RESEARCH ORGANIZATION (ARO) (VOLCANI CENTER), Rishon Lezion (IL)
PCT Filed Mar. 1, 2021, PCT No. PCT/IL2021/050236
§ 371(c)(1), (2) Date Aug. 24, 2022,
PCT Pub. No. WO2021/176452, PCT Pub. Date Sep. 10, 2021.
Claims priority of provisional application 62/983,677, filed on Mar. 1, 2020.
Prior Publication US 2023/0078617 A1, Mar. 16, 2023
Int. Cl. G01N 21/35 (2014.01); G01N 1/28 (2006.01); G01N 1/40 (2006.01); G01N 21/31 (2006.01); G01N 21/84 (2006.01)
CPC G01N 21/35 (2013.01) [G01N 1/4044 (2013.01); G01N 2001/2866 (2013.01); G01N 2021/3155 (2013.01); G01N 2021/8466 (2013.01); G01N 2201/129 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A method for assessing nitrogen status in a plant comprising steps of:
a. obtaining a sample from different plant parts or tissues;
b. drying, digesting, and grinding said plant parts or tissues to a powder;
c. measuring concentrations of predetermined materials of said powder; said predetermined materials selected from the group consisting of non-structural carbohydrates (NSC), soluble carbohydrates (SC), starch (ST), nitrogen (N), potassium (K), phosphorus (P), and any combination thereof;
d. obtaining spectral data of said powder;
e. correlating said obtained spectral data to concentrations and concentration ratios of predetermined materials; said concentration ratios selected from the group consisting of N, K, P and the ratio between N/SC, N/ST, and any combination thereof;
f. conducting pre-processing transformations (PPTs) on said obtained spectral data; and
g. calibrating said obtained spectral data against said concentrations and concentration ratios of predetermined materials by multivariate machine learning statistical models;
wherein said multivariate machine learning statistical models are used to assess and predict crop nitrogen nutritional status and carbohydrate concentrations; said prediction is utilized to determine under fertilization and overfertilization based on redaction in starch levels decrease and yield of said crops.