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WO-2026096058-A1 - SYSTEM AND METHOD FOR MONITORING CROPS

WO2026096058A1WO 2026096058 A1WO2026096058 A1WO 2026096058A1WO-2026096058-A1

Abstract

Systems and methods for monitoring crops are disclosed. This disclosure relates to a system for monitoring crops, the system comprising a first support frame, the support frame comprising a first sensor rack coupled to the first support frame, the first sensor rack comprising a first sensor module; a movement module, the movement module comprising a first sensor rack motor, wherein the first sensor rack motor is configured to move the first sensor rack along the first support frame from a first starting location to a first ending location; and a hyperspectral module, wherein the first sensor module is coupled to the hyperspectral module.

Inventors

  • CHANG, CHRISTINE Y.

Assignees

  • THE UNITED STATES OF AMERICA, AS REPRESENTED BY THE SECRETARY OF AGRICULTURE

Dates

Publication Date
20260507
Application Date
20250829
Priority Date
20241030

Claims (20)

  1. 1. A crop monitoring system, the system comprising: a first support frame, the support frame comprising: a first sensor rack coupled to the first support frame, the first sensor rack comprising a first sensor module; a movement module, the movement module comprising a first sensor rack motor, wherein the first sensor rack motor is configured to move the first sensor rack along the first support frame from a first starting location to a first ending location; and a hyperspectral module, wherein the first sensor module is coupled to the hyperspectral module.
  2. 2. The system of claim 1, further comprising: a second sensor rack coupled to a second support frame, the second sensor rack comprising a second sensor module.
  3. 3. The system of claim 2, wherein the movement module further comprises a second sensor rack motor, the second sensor rack motor configured to move the second sensor rack along the second support frame from a second starting location to a second ending location.
  4. 4. The system of claim 3, further comprising: USDA Docket No. 0098.24 Chang a controller in electrical communication with the movement module, the controller configured to: control the movement of the first sensor rack and the second sensor rack.
  5. 5. The system of claim 4, further comprising: an environmental module, the environmental module configured to collect a light intensity; and wherein the controller is in electrical communication with the environmental module to determine a daytime cycle lighting condition or a nighttime cycle lighting condition based on the light intensity from the environmental module.
  6. 6. The system of claim 5, wherein the crop monitoring system is an autonomous crop monitoring system.
  7. 7. The system of claim 1, wherein the first sensor module further comprises a light source, wherein the light source is configured to induce a chlorophyll fluorescence in a subject crop.
  8. 8. The system of claim 3, wherein the second sensor module further comprises an imaging module, wherein the imaging module comprises a camera, wherein the camera is configured to collect above-canopy imagery of a subject crop.
  9. 9. The system of claim 1, wherein the hyperspectral module comprises a spectrometer configured to a spectral range of about 500-900 nm.
  10. 10. The system of claim 6, further comprising: USDA Docket No. 0098.24 Chang a Hall effect sensor, where the Hall effect sensor is coupled to the first support frame.
  11. 11. The system of claim 10, further comprising: a magnet coupled to the first sensor rack, wherein the Hall effect sensor is configured to sense the magnet and send a signal to the controller to stop the movement of the first sensor rack such that the first sensor rack is over a subject crop.
  12. 12. The system of claim 5, wherein the first sensor rack is located below the second sensor rack, such that the first sensor rack is configured to travel parallel to the second sensor rack between a subject crop and the second sensor rack.
  13. 13. The system of claim 9, wherein the first sensor module comprises a fiber optic sensor, and wherein the fiber optic sensor is coupled to the hyperspectral module.
  14. 14. The system of claim 6, further comprising: a first drive shaft, wherein the first drive shaft is operatively connected to the first sensor rack motor; a first drive chain operatively connected to the first drive shaft and a first front top axle; and a first rack chain operatively connected to the first front top axle and a first front bottom axle at a front end of the first support frame, the first rack chain operatively connected to the first sensor rack.
  15. 15. An automated crop monitoring system, the system comprising: USDA Docket No. 0098.24 Chang a control module, configured to direct operations of the system; a first support frame; a first sensor rack coupled to the first support frame, the first sensor rack comprising at least one sensor coupled to the first sensor rack; a movement module, the movement module comprising a first sensor rack motor, wherein the first sensor rack motor is configured to move the first sensor rack along the first support frame from a first starting location to a first ending location; at least one Hall effect sensor coupled to first support frame; and a magnet coupled to the first sensor rack, wherein the at least one Hall effect sensor is configured to sense the magnet and send a signal to the control module to stop the movement of the first sensor rack such that the first sensor rack is over a subject crop.
  16. 16. The system of claim 15 , further comprising: a hyperspectral module, wherein the at least one sensor is coupled to the hyperspectral module.
  17. 17. The system of claim 15, further comprising: a second sensor rack coupled to a second support frame, the second sensor rack comprising a second sensor module.
  18. 18. The system of claim 17, wherein the movement module further comprises a second sensor rack motor, the second sensor rack motor configured to move the second sensor USDA Docket No. 0098.24 Chang rack along the second support frame from a second stalling location to a second ending location.
  19. 19. The system of claim 18, further comprising: an environmental module, the environmental module configured to collect a light intensity; and wherein the control module is in electrical communication with the environmental module to determine a daytime cycle lighting condition or a nighttime cycle lighting condition based on the light intensity from the environmental module.
  20. 20. The system of claim 19, further comprising a light module, wherein the light module comprises a light source, and wherein the light source is configured to induce a chlorophyll fluorescence in a subject crop.

Description

USDA Docket No. 0098.24 Chang SYSTEM AND METHOD FOR MONITORING CROPS FIELD [0001] The disclosed subject matter relates generally to systems and methods for monitoring crops. Specifically, the subject matter described herein relates to systems and methods to automatically track plant stress responses using hyperspectral reflectance, nighttime chlorophyll fluorescence, and imaging. BACKGROUND [0002] Plant phenotyping is considered one of the bottlenecks of current agricultural research (Fiorani and Schurr 2013; Araus and Cairns 2014). Plant phenotyping is a scientific field that measures and analyzes plant traits to improve crop productivity and sustainability. It is the process of measuring a plant's structural and functional properties, such as growth, yield, and stress adaptation. Plant phenotyping refers to a quantitative description of the plant's anatomical, ontogenetical, physiological and biochemical properties. It uses a variety of measurements including destructive sample analyses and non-invasive technologies to track traits over time and screen genotypes. [0003] Crop breeding approaches need to be improved to keep pace with the increasing demand on major grain crops (Tester and Langridge 2010) and other food commodities. The ability to monitor crop health and development efficiently and accurately is paramount for advancing agricultural research and optimizing crop production. Traditional phenotyping methods often involve labor-intensive and time-consuming processes, which limit their application to dynamic environments. Recent technological advancements have paved the way for the development of automated and high-throughput systems that significantly enhance phenotyping capabilities. USDA Docket No. 0098.24 Chang High throughput phenotyping (HTP) has emerged as a critical tool in this regard, providing detailed insights into plant responses to various environmental stresses. Automated approaches for remote sensing -based quantification of several traits to monitor the plant stress response have been established throughout the last decade (Chen et al. 2014; Junker et al. 2015; Jin et al. 2023). Among these traits are plant architecture, height, growth, photosynthesis, biomass, chlorophyll status, senescence and vigor. These traits can be extracted from red, green, and blue (RGB) images, or they can be related to spectral parameters associated with plant traits derived from canopy reflectance, such as the normalized difference vegetation index (ND VI), to chlorophyll fluorescence, or to canopy temperature quantified via thermography (Araus and Cairns 2018). [0004] Several high-throughput phenotyping systems have been developed to address the limitations of traditional methods (Jin et al. 2022). Some systems have introduced automated imaging technologies, including RGB, thermal, and hyperspectral imaging, to monitor various plant traits that allow for an assessment of crop traits at a sufficient throughput (Hairmansis et al. 2014; Asaari et al. 2019; Beauchene et al. 2019). These systems, primarily designed for large- scale facilities, have significantly advanced the field by providing high-resolution data for phenotypic analysis. However, they often require extensive infrastructure and are primarily suitable for field environments, limiting their flexibility and applicability in diverse experimental conditions under controlled environments. Additionally, these systems typically focus on daytime measurements, neglecting the critical physiological processes that occur during the night to detect the stress response. [0005] Chlorophyll fluorescence (ChlF) is an optical signal emitted from illuminated chlorophyll molecules and represents the fraction of absorbed light not used in photochemistry or dissipated by nonphotochemical quenching (Porcar-Castell et al., 2014). Sun-induced chlorophyll USDA Docket No. 0098.24 Chang fluorescence (SIF), measured under natural sunlight, has been identified as a reliable indicator of photosynthesis and gross primary production (GPP) across various environmental conditions (Mohammed et al., 2019; Sun et al., 2017). Understanding the mechanistic aspects of the ChlF signal is crucial to detect plant stress, as it contains inherent information on both dynamic and sustained plant processes. Remote sensing techniques focused on the far-red and red spectral regions have proven effective in retrieving SIF signals from satellites and ground-based hyperspectral sensors (Frankenberg et al., 2011; Guanter et al., 2012). A new approach has recently emerged to measure nighttime ChlF spectra of plant canopies induced by light-emitting diodes (LEDs) (Romero et al., 2018, 2021). Blue LEDs (emitting light around 450-490 nanometers (nm)) arc particularly effective, as ChlF is emitted in the red to far-red spectral range (650-800 nm), thus providing a pure chlorophyll fluorescence signal that is free from contamination from the actinic light source (Atherton et al., 2019). Its key