Steve Mantle

Steve Mantle

Walla Walla, Washington, United States
5K followers 500+ connections

About

As the founder of innov8.ag, I am on a mission to empower growers with data-informed…

Articles by Steve

  • A World of Change on Earth Day

    A World of Change on Earth Day

    As we reflect today on Earth Day 2020, we're looking at the world through a whole new lens - literally & figuratively…

Activity

Experience

Education

Volunteer Experience

  • USDA National Institute of Food and Agriculture Graphic

    Panelist

    USDA National Institute of Food and Agriculture

    - Present 4 years 10 months

    Science and Technology

  • Walla Walla University Graphic

    Guest Lecturer

    Walla Walla University

    Education

    Guest lecture each quarter, typically in a business law class. Topics thus far included Ethical Considerations for Artificial Intelligence (AI), Negotiating for Success.

  • Rotary International Graphic

    Member

    Rotary International

    - 3 years

    Health

  • Entrepreneurial Ecosystem Committee

    Community Council of Walla Walla

    - Present 5 years 5 months

    Economic Empowerment

  • Hopelink Graphic

    Executive Leadership Council

    Hopelink

    - 5 years 10 months

    Poverty Alleviation

    The Hopelink Executive Leadership Council is a volunteer group of talented people with visionary, professional, strategic, social, philanthropic and entrepreneurial skills and expertise that provide high-level advice and support to Hopelink's President and CEO, and the agency's executive staff. Our mission is to build and strengthen Hopelink's mission, campaigns and programs.

  • Big Brothers Big Sisters of Puget Sound Graphic

    Big Brother

    Big Brothers Big Sisters of Puget Sound

    Children

    At Big Brothers Big Sisters, we nurture the academic, cultural, and social lives of children facing adversity to positively change the trajectory of their young lives. Our evidence-based outcomes show that our Littles, mentored by caring adult Bigs, have more success than their non-mentored peers at staying in school, avoiding alcohol and drugs, and staying out of the juvenile justice system.

  • Rogers Adventist School Graphic

    Robotics Coach

    Rogers Adventist School

    Education

Publications

  • Applying Artificial Intelligence (AI) in Agriculture: Evidence from Washington State Orchards

    Agricultural & Applied Economics Association (AAEA) - 2021 Workshop

    The current study models and quantifies the economic impacts of applying artificial intelligence in Chiawana orchards of Washington State—a smart orchard project jointly undertaken by the Washington State’s Tree Fruit Research Commission and innov8.ag.

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  • Geospatial apple canopy transpiration mapping: effect of in-field and open-field weather

    2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 182-186, 2021

    This study aims to assess the impact of localized as well as open-field weather data on geospatial transpiration (T) for a modern apple orchard, mapped using energy balance modelling approach. Open-field weather data was collected from five stations and all-in-one weather sensors within 0–3 km from the orchard-center whereas in-orchard weather data was collected at 0.8 m and 1.8 m above ground level. Pertinent model also uses aerial multispectral and thermal infrared imagery data as standard…

    This study aims to assess the impact of localized as well as open-field weather data on geospatial transpiration (T) for a modern apple orchard, mapped using energy balance modelling approach. Open-field weather data was collected from five stations and all-in-one weather sensors within 0–3 km from the orchard-center whereas in-orchard weather data was collected at 0.8 m and 1.8 m above ground level. Pertinent model also uses aerial multispectral and thermal infrared imagery data as standard inputs which were collected in five campaigns during the growing season. Significant deviations in solar radiation, wind speed, relative humidity, air temperature, and reference evapotranspiration (Coefficient of variation: 3–55 %, Pearson linear correlation [r]: 0.7–1) was observed for open-field weather stations surrounded by different heterogeneous crops. Relatively low deviations were observed for standard open-field weather station (3 km from orchard-center) and the one outside the orchard (100 m from center). Proportional variation in T estimates were also observed with lowest deviation for weather inputs from nearest open-field station and the all-in-one weather sensor (r: 0.85–0.97, Root mean square difference: 3–13 %). Deviations in T estimates were also observed for in-orchard weather data inputs from all-in-one weather sensors installed at different canopy heights (r: 0.6–0.98). The results suggest that crop mapping at high resolution and in-orchard weather data inputs could better estimate crop water use (T).

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  • Spatiotemporal water use mapping of a commercial apple orchard using UAS based spectral imagery

    2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 268-272, 2020

    Crop water use estimation at high geospatial resolution is critical for site-specific irrigation management of perennial specialty crops. This study aims to map actual evapotranspiration (ETa) of a commercial apple orchard using unmanned aerial system (UAS) based thermal and multispectral imagery and a widely adopted METRIC (Mapping Evapotranspiration at High Resolution with Internalized calibration) energy balance model (UASM). Four imaging campaigns were conducted during the 2020 growth…

    Crop water use estimation at high geospatial resolution is critical for site-specific irrigation management of perennial specialty crops. This study aims to map actual evapotranspiration (ETa) of a commercial apple orchard using unmanned aerial system (UAS) based thermal and multispectral imagery and a widely adopted METRIC (Mapping Evapotranspiration at High Resolution with Internalized calibration) energy balance model (UASM). Four imaging campaigns were conducted during the 2020 growth season and weather data for pertinent days was downloaded from the nearest WSU-AgWeatherNet network station. 24-h ET a was also calculated from the soil water balance (SWB) approach that used soil moisture data from sensors installed at three locations and down to depth of 111 cm. A high linear correlation (r) of 0.84 and non-significant difference (p = 0.5) was observed between UASM derived ET a (5.05 ± 0.8 [Mean ± Std. Dev.] mm day -1 ) and SWB calculated ET a (5.44 ± 1.81 mm day -1 ). Notable differences in spatiotemporal water use and crop-coefficients were observed within the orchard. A moderately strong correlation was also observed between the UASM derived crop-coefficients and multispectral imagery derived Normalized Difference Vegetation Index (r = 0.69) that may also be used for estimating actual crop water use. Overall, approach presented in this study may help identify under or over-irrigated areas within the orchard. It may also assist in developing site-specific irrigation prescription maps and schedules.

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