Golf course putting greens require a high level of inputs predicated on timely, well informed decisions. Putting green quality is ultimately defined by performance of the turfgrass, and this performance encompasses both (i) the health and vitality of the turfgrass plants, and (ii) the ability of the turfgrass to exist as a playing surface, as it interacts with the golf ball. For golf course superintendents, accurately and efficiently assessing moisture levels and nutrient status are critical for guiding maintenance practices. This research sought to examine new ways for measuring each of these parameters, and compared them to ground-truth data and/or industry standard methodology/devices. Putting green moisture levels are typically measured as volumetric water content (θ) using portable time domain reflectometry (TDR) meters; in this research a common TDR meter (FieldScout TDR300, Spectrum Technologies Inc.) was fit with spacer-blocks, so that new measurement depths closer to the putting surface (1.2 and 2.5 cm depth) could be obtained. For nutrient status (as well as overall turf health), green color is often used as a general (and subjective) assessment; in this research, a new smartphone app (GreenIndex+ Turf, Spectrum Technologies) utilizing the dark green color index (DGCI) was compared to (i) visual color ratings, as well as (ii) standard research methodology using digital image analysis (DIA). The modified TDR 300 produced significant (P < 0.0001) linear prediction equations for 1.2 and 2.5 cm depths. The DGCI from the GreenIndex+ Turf app lacked the consistency of standardized DIA. Based on these results, simple modifications to an existing TDR300 meter can provide additional moisture information very close to the putting surface. For DIA, further research and refinements are necessary to improve ambient light adjustments, in order to produce reliable data across a range of different lighting conditions.
","abstract_html":"<p>Golf course putting greens require a high level of inputs predicated on timely, well informed decisions. Putting green quality is ultimately defined by performance of the turfgrass, and this performance encompasses both (i) the health and vitality of the turfgrass plants, and (ii) the ability of the turfgrass to exist as a playing surface, as it interacts with the golf ball. For golf course superintendents, accurately and efficiently assessing moisture levels and nutrient status are critical for guiding maintenance practices. This research sought to examine new ways for measuring each of these parameters, and compared them to ground-truth data and/or industry standard methodology/devices. Putting green moisture levels are typically measured as volumetric water content (θ) using portable time domain reflectometry (TDR) meters; in this research a common TDR meter (FieldScout TDR300, Spectrum Technologies Inc.) was fit with spacer-blocks, so that new measurement depths closer to the putting surface (1.2 and 2.5 cm depth) could be obtained. For nutrient status (as well as overall turf health), green color is often used as a general (and subjective) assessment; in this research, a new smartphone app (GreenIndex+ Turf, Spectrum Technologies) utilizing the dark green color index (DGCI) was compared to (i) visual color ratings, as well as (ii) standard research methodology using digital image analysis (DIA). The modified TDR 300 produced significant (P < 0.0001) linear prediction equations for 1.2 and 2.5 cm depths. The DGCI from the GreenIndex+ Turf app lacked the consistency of standardized DIA. Based on these results, simple modifications to an existing TDR300 meter can provide additional moisture information very close to the putting surface. For DIA, further research and refinements are necessary to improve ambient light adjustments, in order to produce reliable data across a range of different lighting conditions. </p>","abstract_has_math":false,"creators":["O'Brien, Daniel Patrick"],"institution":null,"degree_name":"Master of Science in Horticulture (MS)","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Brye, Kristofor R.","Purcell, Larry C."],"advisors":["Karcher, Douglas E."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-12-01T08:00:00Z","date_published":"2017-12-01T08:00:00Z","updated_at":"2026-07-24T01:00:09Z","subjects":["Digital Image Analysis","Golf","Putting Green","Technology","Time Domain Reflectometry","Turfgrass","Agricultural Science","Agronomy and Crop Sciences","Horticulture"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/2614","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Brye, Kristofor R.","Purcell, Larry C."]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Karcher, Douglas E."]},{"key":"dc:creator","label":"Author","values":["O'Brien, Daniel Patrick"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-02-06T08:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Horticulture (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Digital Image Analysis","Golf","Putting Green","Technology","Time Domain Reflectometry","Turfgrass","Agricultural Science","Agronomy and Crop Sciences","Horticulture"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uark.edu/etd/2614"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Golf course putting greens require a high level of inputs predicated on timely, well informed decisions. Putting green quality is ultimately defined by performance of the turfgrass, and this performance encompasses both (i) the health and vitality of the turfgrass plants, and (ii) the ability of the turfgrass to exist as a playing surface, as it interacts with the golf ball. For golf course superintendents, accurately and efficiently assessing moisture levels and nutrient status are critical for guiding maintenance practices. This research sought to examine new ways for measuring each of these parameters, and compared them to ground-truth data and/or industry standard methodology/devices. Putting green moisture levels are typically measured as volumetric water content (θ) using portable time domain reflectometry (TDR) meters; in this research a common TDR meter (FieldScout TDR300, Spectrum Technologies Inc.) was fit with spacer-blocks, so that new measurement depths closer to the putting surface (1.2 and 2.5 cm depth) could be obtained. For nutrient status (as well as overall turf health), green color is often used as a general (and subjective) assessment; in this research, a new smartphone app (GreenIndex+ Turf, Spectrum Technologies) utilizing the dark green color index (DGCI) was compared to (i) visual color ratings, as well as (ii) standard research methodology using digital image analysis (DIA). The modified TDR 300 produced significant (P < 0.0001) linear prediction equations for 1.2 and 2.5 cm depths. The DGCI from the GreenIndex+ Turf app lacked the consistency of standardized DIA. Based on these results, simple modifications to an existing TDR300 meter can provide additional moisture information very close to the putting surface. For DIA, further research and refinements are necessary to improve ambient light adjustments, in order to produce reliable data across a range of different lighting conditions.
"]},{"key":"dc:title","label":"Title","values":["New Technologies for Evaluating Putting Green Surface Characteristics"]}]}],"canonical_facts":{"dc:contributor":["Brye, Kristofor R.","Purcell, Larry C."],"dc:contributor.advisor":["Karcher, Douglas E."],"dc:creator":["O'Brien, Daniel Patrick"],"dc:date":["2017"],"dc:date.available":["2024-02-06T08:00:00Z"],"dc:description.abstract":["Golf course putting greens require a high level of inputs predicated on timely, well informed decisions. Putting green quality is ultimately defined by performance of the turfgrass, and this performance encompasses both (i) the health and vitality of the turfgrass plants, and (ii) the ability of the turfgrass to exist as a playing surface, as it interacts with the golf ball. For golf course superintendents, accurately and efficiently assessing moisture levels and nutrient status are critical for guiding maintenance practices. This research sought to examine new ways for measuring each of these parameters, and compared them to ground-truth data and/or industry standard methodology/devices. Putting green moisture levels are typically measured as volumetric water content (θ) using portable time domain reflectometry (TDR) meters; in this research a common TDR meter (FieldScout TDR300, Spectrum Technologies Inc.) was fit with spacer-blocks, so that new measurement depths closer to the putting surface (1.2 and 2.5 cm depth) could be obtained. For nutrient status (as well as overall turf health), green color is often used as a general (and subjective) assessment; in this research, a new smartphone app (GreenIndex+ Turf, Spectrum Technologies) utilizing the dark green color index (DGCI) was compared to (i) visual color ratings, as well as (ii) standard research methodology using digital image analysis (DIA). The modified TDR 300 produced significant (P < 0.0001) linear prediction equations for 1.2 and 2.5 cm depths. The DGCI from the GreenIndex+ Turf app lacked the consistency of standardized DIA. Based on these results, simple modifications to an existing TDR300 meter can provide additional moisture information very close to the putting surface. For DIA, further research and refinements are necessary to improve ambient light adjustments, in order to produce reliable data across a range of different lighting conditions.
"],"dc:identifier":["https://scholarworks.uark.edu/etd/2614"],"dc:subject":["Digital Image Analysis","Golf","Putting Green","Technology","Time Domain Reflectometry","Turfgrass","Agricultural Science","Agronomy and Crop Sciences","Horticulture"],"dc:title":["New Technologies for Evaluating Putting Green Surface Characteristics"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Horticulture (MS)"]},"updated_at":"2026-07-24T01:00:09Z"}