無人飛行器（UAV）近年來成為最有利於監測作物健康的技術，其技術與發展依循GPS和大數據分析來獲取更多資訊。在台灣，草坪在運動領域的使用正逐漸成長。草坪品質是評估管理操作中草坪性能的表現。它的組成主要是由顏色，密度，葉片質地，生長習性，平滑度和均勻性等六大項品質表現。
百慕達草在台灣被利用在高爾夫球場及運動場較多，其中雜交種Tifway419為最常利用之品種，而Tifdwarf 或 Tifgreen等可以被割刈至超矮高度的雜交種被利用在高爾夫球場的果嶺為多。在庭院或者是公園等常可以發現普通種或台灣馴化種百慕達具有較長的葉片及較寬的寬度，在經過割刈管理後可以形成品質中等的草坪。
從前台灣草坪發展與品質監測系統缺乏，而如今精準農業為農藝學帶來了更多的好處，在無人飛行器（UAV）的遙感探測中使用多光譜反射訊號可偵測植物之生長品質。植物捕獲太陽輻射中之可見光譜區段，因為它對生存至關重要。因此，我們從植物的光譜反射中捕獲訊息。在植物中吸收的大部分能量仍然是低於380nm波長的紫外線及在380nm和780nm之可見光。多光譜相機或相關儀器應用光譜反應之不同植生指數來建立單一數值並評估植物生長條件，環境逆境和葉面積成長等狀況。
本試驗之目的在利用UAV多光譜之各項植生指標對比草坪不同肥料下之生長與品質以測試UAV應用之可行性。
在第一次和第二次肥料試驗中(分別為沒有割草和割草的操作)。主要目的在測試四種不同肥料的持續時間。在試驗I(沒有割草)，顏色指數顯示在第1天施肥後色澤增加，直到第29天。而在第4週，結果四種不同的處理無顯著差異。其中在前三週中M處理(N:P:K ; 21%: 3%: 21%)相比L處理(2.5%: 0.5%: 0.5%)和O處理(1.8%: 0.5%: 0.5%)相比有顯著性差異，但與F處理(1.25%: 0.5%: 0.5%)無差異。而第4週的比較中均顯示四種不同處理方法無生長差異。
在試驗II(加入割草操作)結果顯示: 在第2週之後在視覺評估的顏色、顏色指數、綠覆蓋百分比、均一性還有割刈鮮重上皆有顯著差異；M處理在第二週後肥料釋出，顯示出與其他處理之間不同的效果，尤其是在第4週，處理之間有顯著差異。所有視覺評估項目在M處理上都顯示出最佳結果，除了葉子寬度。而其結論是肥料可以保留到第4週且有著顯著差異。並將這些結果應用於試驗3和4。
在試驗III進行了田間試驗(沒有割草操作)，結果顯示在第3週，L處理具有顯著最高的NIR和Red反射率（38.6％和9.99％）。在視覺評級中，M處理顯示與L和F處理相比具有顯著差異，其具有最高的顏色值（7），均勻性（71.11％）和密度（31.07/ 25cm2）。UAV之植生指數在第2週和第3週較敏感。結果中僅顯示SR（Simple Ratio），GCI（Green Chlorophyll Index），NDVI（Normalize Difference Vegetation Index）和GNDVI（Green Normalize Difference Vegetation Index）在處理之間存在顯著差異。不同植生指數的相關性檢測結果，SR與均勻度（r = 0.98）、顏色（r = 0.89）、密度（r = 0.71）、鉀（r = 0.91）、葉綠素a（r = 0.90）、葉綠素總量（r = 0.84）、類胡蘿蔔素（r = 0.87）、和花青素（r = -0.9）相關性最高。綠色覆蓋與NDVI的相關性最高（r = 0.88）。
在實驗IV(加入割草操作)，結果顯示NIR反射率在第3週時最顯著，其中且M處理顯示與F處理的差異(37.06 v.s.33.83%)。M處理在紅光上具有顯著最低之反射率(8.67%)。第3週NDVI的差異在於M和L處理（均為0.62）對於O處理（0.58），顏色指數M，L處理（6.26, 6.24）對於O處理（5.98）。視覺評估在第1週和第2週各處理間明顯差異。顏色和綠色覆蓋的顯著差異在於M和L處理，顏色（8.56 v.s. 6.89），綠色覆蓋（78.89 v.s.70％），密度（22.6 v.s.20.13 shoots/ 25cm2），鮮重（116.99g v.s.48.01g）。在第2週各元素含量處理間呈顯著差異: M和L處理差異最大，氮（3.46v.s. 3.12％），磷（0.42 v.s. 0.34％），鉀（2.07 v.s. 1.73％）。色素含量中M和F處理間有顯著差異，葉綠素a （0.11 v.s. 0.10µg/ml），總葉綠素（0.13 v.s. 0.12µg/ml），類胡蘿蔔素（10.85 v.s. 10.38µg/ml）和花青素（0.15 v.s. 0.13µg/ml）。
進一步分析UAV多光譜之植生指數與草坪品質之相關性，結果顯示: NDVI與均勻度（r = 0.85）、氮（r = 0.63）、磷（r = 0.90）和葉綠素a（r = -0.52）的相關性最大。GCI與Green Cover（r = 0.8）和Color（r = 0.92）的相關性最高。Red與雜草百分比最相關（r = -0.81）。GRVI的葉綠素b最多（r = -0.51），花青素（r = -0.55）。MSR對鮮重的影響最大（r = 0.88）。
總結，利用UAV多光譜之各植生指數對比實際草坪生長品質調查，NDVI和SR或MSR對於視覺評估和元素，色素含量具有最大的相關性和敏感性。NDVI具有最大的檢測不同條件的能力。但SR和MSR可以與NDVI共同使用來增加應用上的可靠度。

Unmanned aerial vehicles (UAVs) is the most advantage technology that provides benefits to monitor crops health. The Drone (UAV) development was following by GPS and Big Data analysis. In Taiwan, use of turfgrass is rising in sport filed. Turf quality is an evaluation to determine the turfgrass performance under culture management practice. It is performance of color, density, texture, growth habit, smoothness and uniformity in the turf.
Bermudagrass is commonly used in golf courses and sport fields in Taiwan. The hybrid species Tifway419 is the most commonly used variety, while the Tifdwarf or Tifgreen and ultra-short height hybrids are mostly on the golf green. In the courtyard or in the park, it is common to find common bermudagrass or Taiwanese domesticated species. Common bermudagrass has longer and wider blades, and can form a medium-quality lawn after cutting and management.
Monitoring system on Turfgrass in Taiwan is rarely conducted. Nowadays, precision agriculture brings more benefit to the agronomy studies, especially in the remote sensing UAV. The plant captures solar radiation on visible light region which is essential to plant survival. And, UAV uses the reflectance on plants to capture the information and analyze its conditions. Most light energy absorbed in plant in ultraviolet region that is below 380 nm wavelength, and 380 nm to 780 nm is known as visible light. The application of vegetation indices are through the use of multispectral camera or instrument which creates a single light reflecting value to evaluate plant growth condition, environmental stress, and leaf area. The purpose of this experiment was to test the feasibility of UAV application by using the UAV multi-spectral plant indices to compare the growth and quality of turf under different fertilizers.
In the first and second fertilizer experiment the main purpose was to test fertilization duration while (turf under no mowing and mowing practice) under four different fertilizers. In the first study (no mowing practice), the color index showed increased after Day 1 fertilizer application and last until the Day 29. At week 4, the results didn’t show significant differences among four different treatments. Before week 4, M treatment performed significant difference as compared to L and O treatment, but no difference to F treatment. The comparisons on week 4 all growth characters showed no difference in four different treatments.
In the second study (mowing practice) the result showed significant difference on Week 2 and weeks after. M treatment showed the huge effect compared to other treatments, especially in week 4. All visual rating characters showed the best results on M treatment, except leaf texture, which also concluded the fertilizer can retained to week 4. And, this information was combined and used on experiment III and IV.
In experiment III(no mowing practice) on field test, the results showed significant difference on week 3. L treatment had highest NIR and Red reflectance (38.6% and 9.99%)；In the visual rating, M treatment showed the significant difference compared to L and F treatment, which had the highest value on color (7), uniformity (71.11%), and density (31.07 /25cm2). Vegetation indices produced by UAV was sensitive on week 2 and week 3. The results showed that SR (Simple Ratio), GCI (Green Chlorophyll Index), NDVI (Normalized difference vegetation index), and GNDVI (Green Normalized difference vegetation index) had significant difference among treatments. The correlation for different vegetation indices to grow characters showed SR were highly related to uniformity (r=0.98), color (r=0.89), density (r=0.71), potassium (r=0.91), chlorophyll a (r=0.9), total chlorophyll (r=0.84), carotenoid (r=0.87), ad anthocyanin (r=-0.9). And the green cover showed high correlation on NDVI (r=0.88).
In the experiment IV field test (mowing), NIR reflectance had most significant on week 3, M treatment showed the difference compared to F treatment (37.06 vs. 33.83%). M treatment had significant lowest reflectance on Red (8.67%). At Week3 NDVI showed significant differences between M and L (both 0.62) with O (0.58), and color index M, L (6.26, 6.24) with O treatment (5.98). The visual rating had significant difference among treatments at week 1 and week 2. The significant difference on color and green cover was between M and L treatment, color (8.56 vs. 6.89), green cover percentage (78.89% vs. 70%), density (22.6 vs. 20.13 shoots/25cm2), and mowing fresh weight (116.99g vs. 48.01g). At week 2, element contents showed significant difference among treatments；M and L showed most significant difference on nitrogen (3.46% vs. 3.12%), phosphorus (0.42% vs. 0.34%), and potassium (2.07% vs. 1.73%). On pigment contents, M and F had significant difference on chlorophyll a (0.11 vs. 0.10µg/ml), total chlorophyll (0.13 vs. 0.12µg/ml), carotenoid (10.85 vs. 10.38µg/ml), and anthocyanin (0.15 vs. 0.13µg/ml).
Correlation coefficient on UAV vegetation Index combined turfgrass visual quality rating showed that NDVI had significant correlation to uniformity (r=0.85), nitrogen (r=0.63), phosphors (r=0.90), and chlorophyll a (r=-0.52). GCI showed highest correlation to green cover (r=0.8), and color (r=0.92). Red was most correlated on weed percentage (r=-0.81). GRVI was most correlated to chlorophyll b (r=-0.51), anthocyanin (r=-0.55) ；MSR had highest correlation to fresh weight (r=0.88).
In conclusion, the results on comparison of UAV vegetation indices with visual turfgrass quality evaluation showed that NDVI and SR or MSR had most correlation and sensitivity to the visual quality, element, and pigment contents. The NDVI had the most ability to detect different plant conditions. However, SR and MSR can be used together with NDVI to increase application reliability.

Arai, K., K. Gondoh, O. Shigetomi, and M.Yuko. 2016. Method for NIR Reflectance Estimation with Visible Camera Data Based on Regression for NDVI Estimation and Its Application for Insect Damage Detection of Rice Paddy Fields. International Journal of Advanced Research in Artificial Intelligence 5(11):17–22.
Arnon, D. I. and F. R. Whatley. 1954. Metabolism of Isolated Cellular Particles from Photo- Synthetic Tissues I. Oxygen Uptake and Carbon Dioxide Evolution in the Dark. Physiologia Plantarum 7:602–13.
Baghzouz, M., D. A. Devitt, and R. Morris. 2007. Assessing Canopy Spectral Reflectrance of Hybrid Bermudagrass under Various Combinations of Nitrogen and Water Treatments. Applied Engineering in Agriculture 23:763–74.
Bannari, A., H. Asalhi, and P. Teillet. 2002. Transformed Difference Vegetation Index (TDVI) for Vegetation Cover Mapping. Canada Centre for Remote Sensing 3053–55.
Barati, S., B. Rayegani, M. Saati, A. Sharifi, and M. Nasri. 2011. Comparison the Accuracies of Different Spectral Indices for Estimation of Vegetation Cover Fraction in Sparse Vegetated Areas. Egyptian Journal of Remote Sensing and Space Science 14(1):49–56.
Bárcena-Martín, E., M. Rodríguez-Fernández, and S. Borrego-Domínguez. 2017. Golf, Supply and Demand: The Influence of Economic Factors. Tourism Economics 23(6):1220–34.
Bell, G. E., D. L. Martin, K. Koh, and H. Han. 2009. Comparison of Turfgrass Visual Quality Ratings with Ratings Determined Using a Handheld Optical Sensor. HortTechnology 19(June):309–16.
Bhange, M. and H. A. Hingoliwala. 2015. Smart Farming: Pomegranate Disease Detection Using Image Processing. Procedia Computer Science 58:280–88.
Birth, G. S. and G. R. McVey. 1968. Measuring the Color of Growing Turf with a Reflectance Spectrophotometer. Agronomy Journal 60(6):640.
Bramley, R. and S. Trengove. 2013. Precision Agriculture in Australia: Present Status and Recent Developments. Engenharia Agrícola 33(3):575–88.
Bremer, D. J., H. Lee, K. Su, and S. J. Keeley. 2011a. Relationships between NDVI and Visual Quality in Cool-Season Turfgrass: I. Variation Among Species and Cultivars. Crop Science 51(5):2212–18.
Bremer, D. J., H. Lee, K. Su, and S. J. Keeley. 2011b. Relationships between Normalized Difference Vegetation Index and Visual Quality in Cool-Season Turfgrass : I . Variation among Species and Cultivars. Crop Science 51.
Broge, N. H. and E. Leblanc. 2000. Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density. Remote Sensing of Environment 76(2000):156.
Chapman, D. Homer. and P. F. Parker. 1961. Methods of Analysis for Soils Plants and Waters. University of California, Division of Agricultural Sci；1st Edition: 56-64.
Carlson, T. N. and D. A. Riziley. 1997. On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index. Remote Sensing of Environment 62:241–52.
Carrow, R. N., R. R. Duncan, and D. Wienecke. 2005. BMPs: Critical for the Golf Industry. Science for the Golf Coourse (June):81–84.
Carrow, R. N., J. M. Krum, I. Flitcroft, and V. Cline. 2010. Precision Turfgrass Management: Challenges and Field Applications for Mapping Turfgrass Soil and Stress. Precision Agriculture 11(2):115–34.
Carter, G. A. 1993. Responses of Leaf Spectral Reflectance to Plant Stress. American Journal of Botany 80(3):239.
Carter, G. A. and A. K. Knapp. 2001. Leaf Optical Properties in Higher Plants: Linking Spectral Characteristics to Stress and Chlorophyll Concentration. American Journal of Botany 88(4):677–84.
Casadesús, J., C. Biel, and R. Savé. 2005. Turf Color Measurement with Conventinal Digital Camers. Vila Real (July):804–11.
Caturegli, L., M. Corniglia, M. Gaetani, N. Grossi, S. Magni, M. Migliazzi, L. Angelini, M. Mazzoncini, N. Silvestri, M. Fontanelli, M. Raffaelli, A. Peruzzi, and M. Volterrani. 2016. Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses. PLoS ONE 11(6):1–13.
Chen, J. M. 1995. Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications. Canadian Journal of Remote Sensing (613):1–21.
Chen, T. I., T. K. Hu, C. L. Huang, S. T. Wu, and F. S. Thseng. 2000. Intraspecific Variation of Bermuda Grass (Cynodon Dactylon (L.) Pers. ) in Taiwan. Journal of Agriculture and Forestry 49(4):19–31.
Chen, Y. L. and C. F. Mou. 2015. Applied Sport Tourism Sustainability Index of the Taiwan Golf Course Management. Journal of Aletheia University Sports Knowledge 15:176–85.
Chen, Z. R. 2004. A Study on the Relationship between Consumers’ Defamation and Behavior, Defamation and Motivation in Golf Courses: A Case Study of the Northern Region. Bulletin of Educational Research 94(29):361–74.
Colwell, J. E. 1974. Vegetation Canopy Reflectance. Remote Sensing of Environment 3(3):175–83.
Crippen, R. E. 1990. Calculating the Vegetation Index Faster. Remote Sensing of Environment 34(1):71–73.
Curran, P. J., J. L. Dungan, B. A. Macler, and S. E. Plummer. 1991. The Effect of a Red Leaf Pigment on the Relationship Between Red Edge and Chlorophyll Concentration. Remote Sensing of Environment 35(1):69–76.
Dalal, R. C. and R. J. Henry. 1986. Simultaneous Determination of Moisture, Organic Carbon, and Total Nitrogen by Near. Soil Science Society of America Journal • (January).
Downs, R. J., H. W. Siegelman, W. L. Butler, and S. B. Hendricks. 1965. Photoreceptive Pigments for Anthocyanin Synthesis in Apple Skin. Nature 206:757–61.
Desonie, D. 2012. Solar Energy on Earth. Aug. 07, 2016, from CK-12 Web: https://www.ck12.org/earth-science/Solar-Energy-on-Earth/lesson/Solar-Energy-on-Earth-HS-ES/.
Filella, I., L. Serrano, J. Serra, and J. Penuelas. 1995. Evaluating Wheat Nitrogen Satus with Canopy Reflectance Indices and Discriminant Analysis. Crop Science 35(5):1400–1405.
Florkowski, W. j and G. Landry. 2002. An Economic Profile of Golf Courses in Georgia: Course and Landscape Maintenance. The University of Georgia Research Report 681:3–15.
Giles, D. K. and R. Billing. 2015. Deployment and Performance of A UAV for Crop Spraying. Chemical Engineering Transactions 44:307–12.
Gitelson, A. A., Y. Gritz, and M. N. Merzlyak. 2003. Relationships between Leaf Chlorophyll Content and Spectral Reflectance and Algorithms for Non-Destructive Chlorophyll Assessment in Higher Plant Leaves. Journal of Plant Physiology 160(3):271–82.
Gitelson, A. A. and M. N. Merzylak. 1998. Remote Estimation of Chlorophyll Content in Higher Plant Leaves, International Journal of Remote Sensing. Advances in Space Research 22:37–41.
Goel, N. S. and W. Qin. 1994. Influences of Canopy Architecture on Relationships between Various Vegetation Indices and LAI and FPAR: A Computer Simulation. Remote Sensing Reviews 10(4):309–47.
Haboudane, D., J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan. 2004. Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture. Remote Sensing of Environment 90(3):337–52.
Hsieh, C. S. and D. Lin. 2018. The Study of Using Multi-Rotor UAV To Produce Large-Scale Topographic Map. Journal of Photogrammetry and Remote Sensing 23(4):285–93.
Huang, M. Y. 2018. Cost-Benefit Analysis of Smart Farming Technology for Banana Growing: The Case of Drones Application. Thesis
Huang, W. D., H. M. Chen, and Y. Wang. 2010. Turf Management.
Huete, A. 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment 25:295–309.
James, B. Beard. 1973. Turfgrass: Science and Culture. Prentice Hall, Inc, Englewood Cliffs, N.J. 1-3 : 181-208.
Jiang, Y. and R. N. Carrow. 2005. Assessment of Narrow-Band Canopy Spectral Reflectance and Turfgrass Performance under Drought Stress. HortScience 40(1):242–45.
Jiang, Y., R. N. Carrow, and R. R. Duncan. 2003. Correlation Analysis Procedures for Canopy Spectral Reflectance Data of Seashore Paspalum under Traffic Stress. HortScience 128(3):343–48.
Jones, S. 2014. The Economic Impact of Golf on The Economy of Europe. Sports Marketing Surveys Inc. 3–38.
Keskin, M., Y. J. Han, R. B. Dodd, and A. Khalilian. 2008. Reflectance-Based Sensor to Predict Visual Quality Ratings of Turfgrass Plots. American Society of Agricultural and Biological Engineers 24(6):855–60.
Krans, J. V and K. Morris. 2008. Determining a Profile of Protocols and Standards Used in the Visual Field Assessment of Turfgrasses: A Survey of National Turfgrass Evaluation Program-Sponsored University Scientists. Applied Turfgrass Science 4.
Larson, L. 2002. Employee Turnover : Are They Really a Dime a Dozen USGA.
Lee, H. j, D. J. Bremer, K. Su, and S. J. Keeley. 2011. Relationships between Normalized Difference Vegetation Index and Visual Quality in Turfgrasses : Eff Ects of Mowing Height. Crop Science 51:323–32.
Leinauer, B., M. Schiavon, E. Sevostianova, and M. Serena. 2011. Warm-Season Turfgrass Quality, Spring Green- up, and Fall Color Retention under Drip Irrigation. Applied Turfgrass Science 22.
Leinauer, B., D. M. Vanleeuwen, M. Serena, M. Schiavon, and E. Sevostianova. 2014. Digital Image Analysis and Spectral Reflectance to Determine Turfgrass Quality. Agronomy Journal 106(5):1787–94.
Li, F., Y. Miao, S. D. Hennig, M. L. Gnyp, X. Chen, L. Jia, and G. Bareth. 2010. Evaluating Hyperspectral Vegetation Indices for Estimating Nitrogen Concentration of Winter Wheat at Different Growth Stages. Precision Agriculture 11(4):335–57.
Lin, S. W. and C. C. Yih. 1993. Study on the Turf Grasses and Management for Golf Courses in Taiwan. Weed Science Bulletin 14(2):103–24.
Lottes, P., R. Khanna, J. Pfeifer, R. Siegwart, and C. Stachniss. 2017. UAV-Based Crop and Weed Classification for Smart Farming. Proceedings-IEEE International Conference on Robotics and Automation 3024–31.
Lu, F. M. 2000. Trend of Development in Agricultural Machinery for Precision Agriculture. Studies on Rice Precision Farming Yystem 105–10.
Madeira, A. C., T. J. Gillespie, and C. L. Duke. 2001. Effect of Wetness on Turfgrass Canopy Reflectance. Agricultural and Forest Meteorology 107(2):117–30.
Mahajan, U. and B. R. Bundel. 2016. Drones for Normalized Difference Vegetation Index ( NDVI ), to Estimate Crop Health for Precision Agriculture: A Cheaper Alternative for Spatial Satellite Sensors. International Conference on Innovative Research in Agriculture, Food Science, Forestry, Horticulture, Aquaculture, Animal Sciences, Biodiversity, Ecological Sciences and Climate Change (AFHABEC-2016) (October 2016):38–41.
Mogili, U. R. and B. B. V. L. Deepak. 2018. Review on Application of Drone Systems in Precision Agriculture. Procedia Computer Science 133:502–9.
Moss, R. A. and W. E. Loomis. 1951. Absorption Spectra of Leaves. I. The Visible Spectrum. Plant Physiology (2):370–91.
Motohka, T., K. N. Nasahara, H. Oguma, and S. Tsuchida. 2010. Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology. Remote Sensing 2(10):2369–87.
Mulla, D. J. 2013. Twenty Five Years of Remote Sensing in Precision Agriculture: Key Advances and Remaining Knowledge Gaps. Biosystems Engineering 114(4):358–71.
Mulla, D. and R. Khosla. 2015. Historical Evolution and Recent Advances in Precision Farming. Soil Specific Farming 1–36.
Murugan, D., A. Garg, and D. Singh. 2017. Development of an Adaptive Approach for Precision Agriculture Monitoring with Drone and Satellite Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(12):5322–28.
Myneni, R. B., F. G. Hall, P. J. Sellers, and A. L. Marshak. 1995. The Interpretation of Spectral Vegetation Indexes. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 33(2):481–86.
National Aeronautics and Space Administration. 2010. Tour of the Electromagnetic Spectrum. Aug. 10, 2016, from the NASA Web: https://science.nasa.gov/ems/09_visiblelight#top
Oatis, D. and R. Vavrek. 2017. The Economics of Golf Course Maintenance. USGA 55(17):1–7.
Perry, C. R. and L. F. Lautenschlager. 1984. Functional Equivalence of Spectral Vegetation Indices. Remote Sensing of Environment 182:169–82.
Petkovics, I., J. Simon, A. Petkovics, and Z. Covic. 2017. Selection of Unmanned Aerial Vehicle for Precision Agriculture with Multi-Criteria Decision Making Algorithm. International Symposium on Intelligent Systems and Informatics, Proceedings 151–55.
Pobkrut, T., T. Eamsa-Ard, and T. Kerdcharoen. 2016. Sensor Drone for Aerial Odor Mapping for Agriculture and Security Services. International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2016 (June).
Puri, V., A. Nayyar, and L. Raja. 2017. Agriculture Drones: A Modern Breakthrough in Precision Agriculture. Journal of Statistics and Management Systems 20(4):507–18.
Qi, J., A. Chehbouni, A. R. Huete., Y. H. Kerr, and S. Sorooshian. 1994. A Modified Soil Adjusted Vegetation Index. Remote Sensing and Environment. 48(2):119–26.
Rad, C. R., O. Hancu, I. A. Takacs, and G. Olteanu. 2015. Smart Monitoring of Potato Crop: A Cyber-Physical System Architecture Model in the Field of Precision Agriculture. Agriculture and Agricultural Science Procedia 6:73–79.
Reinecke, M. and T. Prinsloo. 2017. The Influence of Drone Monitoring on Crop Health and Harvest Size. International Conference on Next Generation Computing Applications, NextComp 2017 5–10.
Richardson, M. D. and L. C. Purcell. 2001. Quantifying Turfgrass Cover Using Digital Image Analysis. Crop Science 1884–88.
Rodriguez, E. F. and C. Y. Choi. 2002. Monitoring Turfgrass Quality Using Multispectral Radiometry. American Society of Agricultural Engineers 45(3):865–71.
Rokhmana, C. A. 2015. The Potential of UAV-Based Remote Sensing for Supporting Precision Agriculture in Indonesia. Procedia Environmental Sciences 24:245–53.
Rondeaux, G., M. Steven, and F. Baret. 1996. Optimization of Soil-Adjusted Vegetation Indices. Remote Sensing of Environment 55(2):95–107.
Rösch, C., M. Dusseldorp, and R. M. Summary. 2005. Precision Agriculture.
Roujean, J. l and F. M. Breon. 1995. Estimating PAR Absorbed by Vegetation from Bidirectional Reflectance Measurements. Remote Sensing and Environment. 384:375–84.
Rupanagudi, S. R., B. S. Ranjani, P. Nagaraj, V. G. Bhat, and G. Thippeswamy. 2015. A Novel Cloud Computing Based Smart Farming System for Early Detection of Borer Insects in Tomatoes. International Conference on Communication, Information and Computing Technology 16–17.
Ryu, M. W., J. S. Yun, T. Miao, I. Y. Ahn, S. C. Choi, and J. H. Kim. 2015. Design and Implementation of A Connected Farm for Smart Farming System. 2015 IEEE SENSORS - Proceedings (2018).
Saari, H., A. Akujärvi, C. Holmlund, H. Ojanen, J. Kaivosoja, A. Nissinen, and O. Niemeläinen. 2017. Visible, Very Near IR and Short Wave IR Hyperspectral Drone Imaging System For Agriculture and Natural Water Application. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42(3W3):165–70.
Schiavon, M., B. Leinauer, M. Serena, B. Maier, and R. Sallenave. 2014. Plant Growth Regulator and Soil Surfactants’ Effects on Saline and Deficit Irrigated Warm-Season Grasses : I. Turf Quality and Soil Moisture. Crop Science 54:2815–26.
Sims, D. A. and J. A. Gamon. 2002. Relationships between Leaf Pigment Content and Spectral Reflectance Across A Wide Range of Species, Leaf Structures and Developmental Stages. Remote Sensing of Environment 81:337–54.
Sönmez, N. K., Y. Emekli, M. Sari, and R. Baştuğ. 2008. Relationship Between Spectral Reflectance and Water Stress Conditions of Bermuda Grass (Cynodon Dactylon L.). New Zealand Journal of Agricultural Research 51:223–33.
Sripada, R. P. 2005. Determining In-Season Nitrogen Requirements for Corn Using Aerial Color-Infrared Photography. North Carolina State University.
Sripada, R. P., J. P. Schmidt, A. E. Dellinger, and D. B. Beegle. 2008. Evaluating Multiple Indices from a Canopy Reflectance Sensor to Estimate Corn N Requirements. Agronomy Journal 100(6):1553–61.
Stiegler, J. C., G. E. Bell, N. O. Maness, and M. W. Smith. 2005. Spectral Detection of Pigment Concentrations in Creeping Bentgrass Golf Greens. International Turfgrass Society 10:818–24.
Sullivan, D., J. Zhang, A. R. Kowalewski, J. B. Peake, W. F. Anderson, F. C. Waltz, and B. M. Schwartz. 2017. Evaluating Hybrid Bermudagrass Using Spectral Reflectance under Different Mowing Heights and Trinexapac-Ethyl Applications. HortTechnology 27(1):45–53.
Turgeon, A. 1991. Turfgrass management. Prentice Hall, Englewood Cliff s, N.J: 1-15.
Tang, L. and G. Shao. 2015. Drone Remote Sensing for Forestry Research and Practices. Journal of Forestry Research 26(4):791–97.
Taylor, J. and B. Whelan. 1997. A General Introduction to Precision Agriculture Precision.
Taylor, P., N. Viovy, O. Arino, and A. S. Belward. 1992. The Best Index Slope Extraction ( BISE ): A Method for Reducing Noise in NDVI Time-Series. International Journal of Remote Sensing 13(May 2013):37–41.
The 2011 Golf Economy report. 2012. The 2011 Golf Economy Report.
Trenholm, L. E., M. J. Schlossberg, G. Lee, W. Parks, and S. A. Geer. 2000. An Evaluation of Multi-Spectral Responses on Selected Turfgrass Species. International Journal of Remote Sensing 21(4):709–21.
Tucker, C. J. 1979. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment 8(2):127–50.
Veroustraete, F. 2015. The Rise of the Drones in Agriculture. EC Agriculture 2(2):325–27.
Volterrani, M., N. Grossi, L. Foschi, and S. Miele. 2005. Effects of Nitrogen Nutrition on Bermudagrass Spectral Reflectance. International Turfgrass Society Research Journal 10(January 2014):1005–14.
Volterrani, M., A. Minelli, M. Gaetani, N. Grossi, S. Magni, and L. Caturegli. 2017. Reflectance, Absorbance and Transmittance Spectra of Bermudagrass and Manilagrass Turfgrass Canopies. PLoS ONE 12(11):1–13.
Wang, Y. W. 2000. Introduction of the Classification, Distribution and Utilization of Turfgrasses. Journal of Weed Science Society of Republic of China 21–1:1–16.
Wherley, B. G. and T. R. Sinclair. 2009. Differential Sensitivity of C3 and C4 Turfgrass Species to Increasing Atmospheric Vapor Pressure Deficit. Environmental and Experimental Botany 67(2):372–76.
Wu, Weicheng. 2014. The Generalized Difference Vegetation Index (GDVI) for Dryland Characterization. Remote Sensing 6(2):1211–33.
Xiong, X. 2001. The Relationship among NDVI, Nitrogen and Irrigation on Bermudagrass (Cynodon Dactylon [L.] Pers.). Thesis
Xu, L. M. and M. Y. Jiang. 2000. Turfgrass Species, Characteristics and Weed Management. 95–98.
Xue, J. and B. F. Su. 2017. Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors 2017:1–17.
Yang, R. 2008. Research on the Management Strategy of Taiwan Golf Course.
Zhou, Q. Y., S. Y. Wu, Q. L. Chen, and H. Y. Guo. 2018. UAV Assists in Agricultural Damage Surveys. Agricultural Meteorological Disaster Technology Special Issue 26–32.