Advanced LEAFSENSE System Leverages Optical Zoom To Map Leaf Health
Outcome/Accomplishment
Drought is a leading cause of crop stress and the resulting economic injury to farmers. LEAFSENSE is a sophisticated sensor technology that researchers are developing to help farmers actively monitor leaf moisture and temperature levels, allowing them to be proactive and make informed decisions in crop cultivation before yields are permanently affected. The researchers developing LEAFSENSE have now developed, optimized, and scaled up fabrication of a biodegradable colorimetric sensor design-fabrication-deployment-detection technology that can be readily extended to desired targets for high figure of merit (FOM) sensing while ensuring high-throughput, low-cost manufacturing. More than 50 sensors were deployed at the Merced, California testbed field in collaboration with 17 Orchard. And autonomous sensor detection, navigation, and interrogation was demonstrated using ground robots in collaboration with Optical Zoom technology. This important work is supported by the U.S. National Science Foundation (NSF)-funded Internet of Things for Precision Agriculture (NSF IoT4Ag), an Engineering Research Center (ERC) headquartered at the University of Pennsylvania (Penn) with partner institutions at the University of California at Merced (UC-Merced) and the University of Florida (UF).
Impact/Benefits
The Internet of Things (IoT) encompasses physical objects with sensors, processing ability, software, and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks. NSF IoT4Ag is working to enable IoT technologies for precision agriculture that will address the challenges of assuring food, energy, and water security for decades to come. LEAFSENSE combines artificial intelligence and smart sensors to enable precision agriculture, optimizing productivity and reducing costs while minimizing the use of energy and water resources and the impact of agricultural practices on the environment. The sensors are cost-effective compared to current moisture sensors, do not require power, and are biodegradable and biocompatible, so they do not need to be retrieved from the field. Colorimetric sensors can be autonomously deployed to spatially and temporally map local leaf health levels at low cost and without a power source. This sensor platform can be readily extended to measure multiple desired environmental parameters and to create “optical noses” for leaf health sensing. Researchers are finding that the sensors can be extended to other applications, including manufacturing.
Explanation/Background
The LEAFSENSE research team—including Drs. Cherie Kagan and Kevin Turner at Penn, Reza Ehsani at UC-Merced, and Ian Small at UF—first developed the biodegradable, adhesive, colorimetric sensors using moisture-sensitive Chitosan polymer, as well as a single-step, low-cost, scalable, and sustainable fabrication process using a custom-built nanoimprint tool and solution-processable nanocrystal inks. Field tests were then successfully conducted on pistachio, almond, and citrus crop leaves in testbed orchards, enabling the team to characterize their adhesive properties and color shifts.
Additional leaf level tests have been performed in lab, greenhouse, and orchard in collaboration with Optical Zoom technology to determine minimum sensor FOM and resonant spectra requirements for outdoor detection of leaf wetness. Optical Zoom uses physical glass lenses inside a camera to change focal length; the light is bent to magnify a subject directly onto a sensor. The team co-optimized titanium dioxide/nitrogen-doped carbon metasurface design and imprinting on leaf-compatible substrates and incorporated adaptive polymers for increased sensitivity. Multi-stress and differential sensing methods were also developed using multi-band and polarization detection.
Four publications resulted from the work, as well as three provisional patent applications and two invention disclosures.
Location
Philadelphia, Pennsylvaniawebsite
Start Year
Microelectronics and IT
Quantum, Microelectronics, Sensing, and IT
Lead Institution
Core Partners
Fact Sheet
Outcome/Accomplishment
Drought is a leading cause of crop stress and the resulting economic injury to farmers. LEAFSENSE is a sophisticated sensor technology that researchers are developing to help farmers actively monitor leaf moisture and temperature levels, allowing them to be proactive and make informed decisions in crop cultivation before yields are permanently affected. The researchers developing LEAFSENSE have now developed, optimized, and scaled up fabrication of a biodegradable colorimetric sensor design-fabrication-deployment-detection technology that can be readily extended to desired targets for high figure of merit (FOM) sensing while ensuring high-throughput, low-cost manufacturing. More than 50 sensors were deployed at the Merced, California testbed field in collaboration with 17 Orchard. And autonomous sensor detection, navigation, and interrogation was demonstrated using ground robots in collaboration with Optical Zoom technology. This important work is supported by the U.S. National Science Foundation (NSF)-funded Internet of Things for Precision Agriculture (NSF IoT4Ag), an Engineering Research Center (ERC) headquartered at the University of Pennsylvania (Penn) with partner institutions at the University of California at Merced (UC-Merced) and the University of Florida (UF).
Location
Philadelphia, Pennsylvaniawebsite
Start Year
Microelectronics and IT
Quantum, Microelectronics, Sensing, and IT
Lead Institution
Core Partners
Fact Sheet
Impact/benefits
The Internet of Things (IoT) encompasses physical objects with sensors, processing ability, software, and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks. NSF IoT4Ag is working to enable IoT technologies for precision agriculture that will address the challenges of assuring food, energy, and water security for decades to come. LEAFSENSE combines artificial intelligence and smart sensors to enable precision agriculture, optimizing productivity and reducing costs while minimizing the use of energy and water resources and the impact of agricultural practices on the environment. The sensors are cost-effective compared to current moisture sensors, do not require power, and are biodegradable and biocompatible, so they do not need to be retrieved from the field. Colorimetric sensors can be autonomously deployed to spatially and temporally map local leaf health levels at low cost and without a power source. This sensor platform can be readily extended to measure multiple desired environmental parameters and to create “optical noses” for leaf health sensing. Researchers are finding that the sensors can be extended to other applications, including manufacturing.
Explanation/Background
The LEAFSENSE research team—including Drs. Cherie Kagan and Kevin Turner at Penn, Reza Ehsani at UC-Merced, and Ian Small at UF—first developed the biodegradable, adhesive, colorimetric sensors using moisture-sensitive Chitosan polymer, as well as a single-step, low-cost, scalable, and sustainable fabrication process using a custom-built nanoimprint tool and solution-processable nanocrystal inks. Field tests were then successfully conducted on pistachio, almond, and citrus crop leaves in testbed orchards, enabling the team to characterize their adhesive properties and color shifts.
Additional leaf level tests have been performed in lab, greenhouse, and orchard in collaboration with Optical Zoom technology to determine minimum sensor FOM and resonant spectra requirements for outdoor detection of leaf wetness. Optical Zoom uses physical glass lenses inside a camera to change focal length; the light is bent to magnify a subject directly onto a sensor. The team co-optimized titanium dioxide/nitrogen-doped carbon metasurface design and imprinting on leaf-compatible substrates and incorporated adaptive polymers for increased sensitivity. Multi-stress and differential sensing methods were also developed using multi-band and polarization detection.
Four publications resulted from the work, as well as three provisional patent applications and two invention disclosures.