About
As the founder of innov8.ag, I am on a mission to empower growers with data-informed…
Articles by Steve
Activity
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Skagit Farmers Supply & Country Store welcomed members to the Icicle Village Resort for our annual Grower Education Conference. A stacked agenda…
Skagit Farmers Supply & Country Store welcomed members to the Icicle Village Resort for our annual Grower Education Conference. A stacked agenda…
Liked by Steve Mantle
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We are looking forward hosting the postharvest community in our neck of the woods. Lots of fun and educational activities planned.
We are looking forward hosting the postharvest community in our neck of the woods. Lots of fun and educational activities planned.
Liked by Steve Mantle
Experience
Education
Volunteer Experience
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Panelist
USDA National Institute of Food and Agriculture
- Present 4 years 10 months
Science and Technology
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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.
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Entrepreneurial Ecosystem Committee
Community Council of Walla Walla
- Present 5 years 5 months
Economic Empowerment
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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.
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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.
Publications
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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.
Other authorsSee publication -
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).
Other authorsSee publication -
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.
Other authorsSee publication
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