# US 2018/0320967 A1 · Methods for post-harvest crop quality management

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- Number: US 2018/0320967 A1
- Document ID: US20180320967A1
- Status: Published application
- Kind: A1
- Application number: 15/968,516
- Filed: 2018-05-01
- Published: 2018-11-08
- Earliest claimed: 2017-05-02
- Assignees: Centaur Analytics, Inc.
- Applicants: Centaur Analytics, Inc.
- Inventors: Efsthathios Kaloudis; Sotirios Bantas; Vasileios Sotiroudas
- Web page: https://patentgrep.com/patent/US20180320967A1

## CPC

- G06Q 10/06395: {Quality analysis or management}
- A01D 91/00: Methods for harvesting agricultural products
- A01F 25/22: Ventilating arrangements
- A01M 1/026: {combined with devices for monitoring insect presence, e.g. termites (bait stations A01M1/2005; detecting other animals in a given area A01M31/002)}
- F26B 9/063: {for drying granular material in bulk, e.g. grain bins or silos with false floor (shallow layer rotary sweep drying bins F26B9/10; perforated wall silos with drying air channels in the stack F26B9/103)}
- F26B 21/35: Temperature; Pressure
- G01N 27/4162: {investigating the composition of gases, by the influence exerted on ionic conductivity in a liquid (conductometry in general G01N27/06; amperometric gas sensors G01N27/404)}
- G01N 33/004: {CO or CO2}
- G01N 33/0075: {for multiple spatially distributed sensors, e.g. for environmental monitoring}
- G01N 33/025: {Fruits or vegetables}
- G01N 33/10: Starch-containing substances, e.g. dough
- G06F 17/13: Differential equations (using digital differential analysers G06F7/64)
- G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30)
- G06Q 10/06315: {Needs-based resource requirements planning or analysis}
- G06Q 10/087: Inventory or stock management, e.g. order filling, procurement or balancing against orders
- G06Q 50/02: Agriculture; Fishing; Forestry; Mining
- G08C 17/02: using a radio link
- A01M 13/00: Fumigators; Apparatus for distributing gases {(poisoning or narcotising insects by vaporising an insecticide A01M1/2022)}
- G01N 33/02: Food
- G06F 2111/10: Numerical modelling
- H04W 84/18: Self-organising networks, e.g. ad-hoc networks or sensor networks
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- A01D: HARVESTING; MOWING
- A01F: PROCESSING OF HARVESTED PRODUCE; HAY OR STRAW PRESSES; DEVICES FOR STORING AGRICULTURAL OR HORTICULTURAL PRODUCE (devices for topping or skinning onions or flower bulbs A23N15/08)
- A01M: CATCHING, TRAPPING OR SCARING OF ANIMALS (appliances for catching swarms or drone-catching A01K57/00; fishing A01K69/00 - {A01K99/00}; biocides, pest repellants or attractants A01N); APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- F26B: DRYING SOLID MATERIALS OR OBJECTS BY REMOVING LIQUID THEREFROM (drying devices for combines A01D41/133; racks for drying fruit or vegetables A01F25/12; drying foodstuffs A23; drying hair A45D20/00; body-drying implements A47K10/00; drying household articles A47L; drying gases or vapours B01D; chemical or physical processes for dewatering or like separating liquids from solids B01D43/00; centrifugal apparatus B04; drying ceramics C04B33/30; drying yarns or fabrics in association with some other form of treatment D06C; drying frames for laundry without heating or positive air circulation, domestic laundry-or spin-driers, wringing or hot pressing laundry D06F; furnaces, kilns, ovens F27)
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G08C: TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS (fluid pressure transmitting systems F15B; mechanical means for transferring the output of a sensing member into a different variable G01D5/00; mechanical control systems G05G)
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

Embodiments of systems and approaches for managing post-harvest crop quality and pests are described. Such a system may include a plurality of edge devices each comprising sensor components and collectively forming a mesh network, for measuring the local physical environment within stored crops and, for example, transmitting the measurements to a service from within the crop storage area. In certain embodiments, such a system may be used to manage post-harvest crops and storage areas—for example, approaches are described for determining fumigation treatment duration, determining phosphine dosage, determining heat treatment duration, and determining safe storage time for crops.

## Claims

### Claim 1

A method for managing a current post-harvest crop heat treatment in a current storage area, comprising:
at a server, preparing a training set of heat treatment time series comprising temperature measurements generated by a first plurality of edge devices placed inside a variety of training storage areas, wherein the temperature measurements comprise measurements of a respective local temperature for a respective edge device at a respective location within the respective training storage area across a respective range of time points;
clustering each time series of the training set of heat treatment time series using a k-shape-based distance method to generate a plurality of time series clusters, each time series cluster having a time axis;
extracting a respective plurality of cluster centroids for a range of points along the time axis of the respective cluster from the plurality of time series clusters;
at the server, receiving current temperature measurement data generated by a current plurality of edge devices placed inside the current storage area, wherein the current temperature measurement data comprises one or more measurements of a respective local temperature for a respective edge device at a respective location within the current storage area;
arranging the temperature measurements into a respective current time series for each respective edge device in the current storage area;
calculating the sum of squared distances between each current time series and the centroids associated with each time series cluster;
assigning each current time series to the respective time series cluster associated with the smallest sum of squared distances;
based on the centroids for each assigned cluster, identifying the expected temperatures at the respective locations within the current storage area for a range of future time points;
based on the current time series, expected temperatures at the respective locations, and a protocol for pest heat tolerance, providing an estimate of the duration for the current post-harvest crop heat treatment.

### Claim 2 (depends on 1)

The method of claim 1, wherein preparing the training set comprises interpolating missing data and processing the training set with a low-pass filter.

### Claim 3 (depends on 1)

The method of claim 1, wherein the centroid is a time series.

### Claim 4

A method for managing post-harvest stored crop quality and marketability, comprising:
at a server, receiving measurement data generated by a plurality of edge devices placed inside a storage area, wherein the measurement data concerns measurements of a respective local physical environment for a respective edge device at a respective location within the storage area, and wherein the measurements are either or both temperature and relative humidity;
determining a portion safe storage time for each portion of a plurality of portions of a stored crop, the respective portions co-localized with respective edge devices of the plurality of edge devices based on the measurement data and a linear model or exponential model for dry matter loss, mold appearance, or germination capacity;
determining a total safe storage time for the stored crop based on the respective portion safe storage times for the plurality of portions of the stored crop; and
providing the total safe storage time.

### Claim 5 (depends on 4)

The method of claim 4, wherein determining the portion safe storage time for each portion of the plurality of portions is further based on a three-dimensional mathematical model and the measurement data, wherein the measurement data includes both temperature and relative humidity.

### Claim 6 (depends on 4)

The method of claim 4, wherein determining the portion safe storage time is additionally based on external weather conditions at a geographic site for the storage area as coupled to the temperature, moisture, oxygen, and carbon dioxide concentrations inside the storage area.

### Claim 7 (depends on 4)

The method of claim 4, wherein determining the portion safe storage time is additionally based on an insect population reported by a plurality of edge devices.

### Claim 8 (depends on 4)

The method of claim 4, wherein the total safe storage time is determined as the earliest portion safe storage time or a weighted average of the portion safe storage times.