# US 12,590,939 B2 · Systems, methods, and devices for detecting harmful algal blooms > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,590,939 B2 - Document ID: US12590939B2 - Status: Granted - Kind: B2 - Application number: 17/845,281 - Filed: 2022-06-21 - Published: 2022-12-22 - Granted: 2026-03-31 - Earliest claimed: 2021-06-18 - Assignees: Colorado State University Research Foundation - Applicants: Colorado State University Research Foundation - Inventors: Bradley Reisfeld; Steven J Simske; Edward Hall - Web page: https://patentgrep.com/patent/US12590939B2 ## CPC - G01N 33/1826: {Organic contamination in water} - B63B 35/00: Vessels or similar floating structures specially adapted for specific purposes and not otherwise provided for - B63B 35/32: for collecting pollution from open water - B63B 2035/007: {autonomously operating} - B63H 2021/171: {making use of photovoltaic energy conversion, e.g. using solar panels} - B64U 2101/30: for imaging, photography or videography - B64U 2101/31: for surveillance - 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) - B63B: SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING (arrangements of vessel ventilation, heating, cooling, or air-conditioning B63J2/00; floating substructures as supports of dredgers or soil-shifting machines E02F9/06) - B63H: MARINE PROPULSION OR STEERING (propulsion of air-cushion vehicles B60V1/14; specially adapted for submarines, other than nuclear propulsion, B63G; specially adapted for torpedoes F42B19/00) - B64U: UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR ## Abstract Described herein are systems, methods, and devices for detecting harmful algae blooms. An example system includes autonomous watercraft; and a computing device operably connected to the autonomous watercraft over a network, the computing device including a processor and a memory having computer-executable instructions stored thereon that cause the processor to: surveil a body of water for an algae growth; receive a local condition at the body of water; predict a spread of the algae growth in the body of water based on the local condition; determine a deployment strategy for the autonomous watercraft based on the spread of the algae growth; and transmit one or more control signals to the plurality of autonomous watercraft based on the deployment strategy, where the autonomous watercraft are configured to collect and analyze a plurality of water samples to determine whether the algae growth is a harmful algae bloom. ## Claims ### Claim 1 An automated system for detecting harmful algae blooms, the system comprising: a plurality of autonomous watercraft; and a computing device operably connected to the plurality of autonomous watercraft over a network, the computing device comprising a processor and a memory, the memory having computer-executable instructions stored thereon that, when executed by the processor, cause the processor to: receive remote imaging data of a body of water; input the remote imaging data to a trained machine learning model configured to generate a geotagged harmful algal bloom (HAB) probability map; determine a deployment strategy for the plurality of autonomous watercraft based on the geotagged HAB probability map; and transmit one or more control signals to the plurality of autonomous watercraft based on the deployment strategy, wherein the plurality of autonomous watercraft are configured to collect and analyze a plurality of water samples to obtain a plurality of temporally and spatially resolved water sample measurements; and output for display a map overlaying the temporally and spatially resolved water sample measurements and the geotagged HAB probability map. ### Claim 2 (depends on 1) The system of claim 1, wherein the one or more control signals are configured to deploy the plurality of autonomous watercraft to a high probability region of the geotagged HAB probability map. ### Claim 3 (depends on 1) The system of claim 1, wherein the memory has further computer-executable instructions stored thereon that, when executed by the processor, cause the processor to receive, from the plurality of autonomous watercraft, temporally- and spatially-resolved water sample data. ### Claim 4 (depends on 3) The system of claim 3, wherein the memory has further computer-executable instructions stored thereon that, when executed by the processor, cause the processor to overlay the temporally- and spatially-resolved water sample data on a map of the body of water. ### Claim 5 (depends on 1) The system of claim 1, wherein one or more of the plurality of autonomous watercraft comprise a sensor configured to detect a harmful algae bloom indicator. ### Claim 6 (depends on 5) The system of claim 5, wherein the sensor comprises one or more of a fluorescence-based sensor, a phosphorus detection sensor, a nitrogen detection sensor, a temperature sensor, a salinity sensor, a pH sensor, a dissolved oxygen sensor, an ultrasound sensor, a light detection and ranging (LIDAR) sensor, an imaging sensor, or a photoelectric sensor. ### Claim 7 (depends on 1) The system of claim 1, wherein one or more of the plurality of autonomous watercraft comprises a sensor system configured to detect a harmful algae bloom indicator. ### Claim 8 (depends on 7) The system of claim 7, wherein the sensor system comprises one or more of a liquid chromatography-mass spectrometry (LC-MS) system or an assay system. ### Claim 9 (depends on 1) The system of claim 1, wherein the remote imaging data comprises imaging data from one or more of a satellite, an aircraft, or a drone. ### Claim 10 (depends on 1) The system of claim 1, wherein the remote imaging data comprises imaging data captured by one or more of a Sea-viewing Wide Field-of-view Sensor (SeaWiFS), a moderate resolution imaging spectroradiometer (MODIS), an advanced very-high-resolution radiometer (AVHRR), or an airborne visible/infrared spectrometer (AVIRIS). ### Claim 11 (depends on 1) The system of claim 1, wherein the step of receive remote imaging data at the body of water comprises receiving weather or water data, the weather or water data comprising one or more of water temperature, water salinity, wind speed and/or direction, or water current speed and/or direction. ### Claim 12 (depends on 1) The system of claim 1, wherein the map overlaying the temporally and spatially resolved water sample measurements comprises an ensemble model. ### Claim 13 (depends on 1) The system of claim 1, wherein the step of determining the deployment strategy for the plurality of autonomous watercraft comprises using a resource mapping model. ### Claim 14 (depends on 13) The system of claim 13, wherein the resource mapping model is a Markov chain model, a Monte Carlo simulation model, a random forest model, a deep learning model, an agent-based model, or an evolutionary model. ### Claim 15 (depends on 1) The system of claim 1, further comprising one or more autonomous aerial vehicles (UAVs) operably coupled to the computing device over the network, wherein the one or more UAVs are configured to collect remote imaging data of algae growth and/or collect and analyze the water samples. ### Claim 16 A method for detecting harmful algae blooms, the method comprising: providing a plurality of autonomous watercraft; receiving remote imaging data of a body of water; inputting the remote imaging data to a trained machine learning model configured to generate a geotagged harmful algal bloom (HAB) probability map; deploying the plurality of autonomous watercraft based on a deployment strategy, wherein the deployment strategy is based on the HAB probability map; collecting, using the plurality of autonomous watercraft, a plurality of water samples to obtain a plurality of temporally and spatially resolved water sample measurements; and outputting for display a map overlaying the temporally and spatially resolved water sample measurements and HAB probability map. ### Claim 17 (depends on 16) The method of claim 16, further comprising receiving, from the plurality of autonomous watercraft, temporally- and spatially-resolved water sample data. ### Claim 18 (depends on 17) The method of claim 17, further comprising overlaying the temporally- and spatially-resolved water sample data on a map of the body of water. ### Claim 19 (depends on 16) The method of claim 16, wherein the remote imaging data comprises imaging data from one or more of a satellite, an aircraft, or a drone. ### Claim 20 (depends on 16) The method of claim 16, wherein the remote imaging data comprises imaging data captured by one or more of a Sea-viewing Wide Field-of-view Sensor (SeaWiFS), a moderate resolution imaging spectroradiometer (MODIS), an advanced very-high-resolution radiometer (AVHRR), or an airborne visible/infrared spectrometer (AVIRIS). ### Claim 21 (depends on 16) The method of claim 16, further comprising receiving weather or water data, the weather or water data comprising one or more of water temperature, water salinity, wind speed and/or direction, or water current speed and/or direction and inputting the weather or water data into the trained machine learning model. ### Claim 22 (depends on 16) The method of claim 16, wherein the map overlaying the temporally and spatially resolved water sample measurements comprises an ensemble model. ### Claim 23 (depends on 16) The method of claim 16, wherein the step of determining the deployment strategy for the plurality of autonomous watercraft comprises using a resource mapping model. ### Claim 24 (depends on 23) The method of claim 23, wherein the resource mapping model is a Markov chain model, a Monte Carlo simulation model, a random forest model, a deep learning model, an agent-based model, or an evolutionary model. ### Claim 25 A computing system for detecting harmful algae blooms, the system comprising: a processor; and a memory operably coupled to the processor, the memory having computer-executable instructions stored thereon that, when executed by the processor, cause the processor to: receive remote imaging data of a body of water; input the remote imaging data to a trained machine learning model configured to generate a geotagged harmful algal bloom (HAB) probability map; determine a deployment strategy for a plurality of autonomous watercraft based on the geotagged harmful algal bloom (HAB) probability map; and transmit one or more control signals to the plurality of autonomous watercraft based on the deployment strategy, wherein the plurality of autonomous watercraft are configured to collect and analyze a plurality of water samples to obtain a plurality of temporally and spatially resolved water sample measurements; and output for display a map overlaying the temporally and spatially resolved water sample measurements and HAB probability map.