Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops
Pengcheng Hu
A , Scott C. Chapman A B and Bangyou Zheng A C
+ Author Affiliations
- Author Affiliations
A CSIRO Agriculture and Food, Queensland Biosciences Precinct 306 Carmody Road, St Lucia 4067, Qld, Australia.
B School of Food and Agricultural Sciences, The University of Queensland, via Warrego Highway, Gatton 4343, Qld, Australia.
C Corresponding author. Email: bangyou.zheng@csiro.au
Functional Plant Biology 48(8) 766-779 https://doi.org/10.1071/FP20309
Submitted: 2 October 2020 Accepted: 14 February 2021 Published: 5 March 2021
Journal Compilation © CSIRO 2021 Open Access CC BY
Abstract
Ground coverage (GC) allows monitoring of crop growth and development and is normally estimated as the ratio of vegetation to the total pixels from nadir images captured by visible-spectrum (RGB) cameras. The accuracy of estimated GC can be significantly impacted by the effect of ‘mixed pixels’, which is related to the spatial resolution of the imagery as determined by flight altitude, camera resolution and crop characteristics (fine vs coarse textures). In this study, a two-step machine learning method was developed to improve the accuracy of GC of wheat (Triticum aestivum L.) estimated from coarse-resolution RGB images captured by an unmanned aerial vehicle (UAV) at higher altitudes. The classification tree-based per-pixel segmentation (PPS) method was first used to segment fine-resolution reference images into vegetation and background pixels. The reference and their segmented images were degraded to the target coarse spatial resolution. These degraded images were then used to generate a training dataset for a regression tree-based model to establish the sub-pixel classification (SPC) method. The newly proposed method (i.e. PPS-SPC) was evaluated with six synthetic and four real UAV image sets (SISs and RISs, respectively) with different spatial resolutions. Overall, the results demonstrated that the PPS-SPC method obtained higher accuracy of GC in both SISs and RISs comparing to PPS method, with root mean squared errors (RMSE) of less than 6% and relative RMSE (RRMSE) of less than 11% for SISs, and RMSE of less than 5% and RRMSE of less than 35% for RISs. The proposed PPS-SPC method can be potentially applied in plant breeding and precision agriculture to balance accuracy requirement and UAV flight height in the limited battery life and operation time.
Keywords: ground coverage, UAV, remote sensing, high-throughput phenotyping, mixed pixels, plant breeding.
References
Adams JE, Arkin GF (1977) A Light Interception Method for Measuring Row Crop Ground Cover. Soil Science Society of America Journal 41, 789–792.| A Light Interception Method for Measuring Row Crop Ground Cover.Crossref | GoogleScholarGoogle Scholar |
Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science 19, 52–61.
| Field high-throughput phenotyping: the new crop breeding frontier.Crossref | GoogleScholarGoogle Scholar | 24139902PubMed |
Ashapure A, Jung J, Yeom J, Chang A, Maeda M, Maeda A, Landivar J (2019) A novel framework to detect conventional tillage and no-tillage cropping system effect on cotton growth and development using multi-temporal UAS data. ISPRS Journal of Photogrammetry and Remote Sensing 152, 49–64.
| A novel framework to detect conventional tillage and no-tillage cropping system effect on cotton growth and development using multi-temporal UAS data.Crossref | GoogleScholarGoogle Scholar |
Ayhan B, Kwan C, Budavari B, Kwan L, Lu Y, Perez D, Li J, Skarlatos D, Vlachos M (2020) Vegetation Detection Using Deep Learning and Conventional Methods. Remote Sensing 12, 2502
| Vegetation Detection Using Deep Learning and Conventional Methods.Crossref | GoogleScholarGoogle Scholar |
Baccini A, Laporte N, Goetz SJ, Sun M, Dong H (2008) A first map of tropical Africa’s above-ground biomass derived from satellite imagery. Environmental Research Letters 3, 045011
| A first map of tropical Africa’s above-ground biomass derived from satellite imagery.Crossref | GoogleScholarGoogle Scholar |
Barthelme S (2019) ‘Imager: image processing library based on “Cimg”.’ https://CRAN.R-project.org/package=imager.
Bhatnagar S, Gill L, Ghosh B (2020) Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities. Remote Sensing 12, 2602
| Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities.Crossref | GoogleScholarGoogle Scholar |
Bhatnagar S, Gill L, Regan S, Naughton O, Johnston P, Waldren S, Ghosh B (2020) Mapping vegetation communities inside wetlands using Sentinel-2 imagery in ireland. International Journal of Applied Earth Observation and Geoinformation 88, 102083
| Mapping vegetation communities inside wetlands using Sentinel-2 imagery in ireland.Crossref | GoogleScholarGoogle Scholar |
Blaschke T (2010) Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65, 2–16.
| Object based image analysis for remote sensing.Crossref | GoogleScholarGoogle Scholar |
Bojacá CR, García SJ, Schrevens E (2011) Analysis of Potato Canopy Coverage as Assessed Through Digital Imagery by Nonlinear Mixed Effects Models. Potato Research 54, 237–252.
| Analysis of Potato Canopy Coverage as Assessed Through Digital Imagery by Nonlinear Mixed Effects Models.Crossref | GoogleScholarGoogle Scholar |
Breiman L, Friedman J, Stone CJ, Olshen RA (1984) ‘Classification and Regression Trees.’ (Wadsworth International Group)
Campilho A, Garcia B, Toorn HVD, Wijk HV, Campilho A, Scheres B (2006) Time-lapse analysis of stem-cell divisions in the Arabidopsis thaliana root meristem. The Plant Journal 48, 619–627.
| Time-lapse analysis of stem-cell divisions in the Arabidopsis thaliana root meristem.Crossref | GoogleScholarGoogle Scholar | 17087761PubMed |
Campillo C, Prieto MH, Daza C, Moñino MJ, García MI (2008) Using Digital Images to Characterize Canopy Coverage and Light Interception in a Processing Tomato Crop. HortScience 43, 1780–1786.
| Using Digital Images to Characterize Canopy Coverage and Light Interception in a Processing Tomato Crop.Crossref | GoogleScholarGoogle Scholar |
Chapman SC, Merz T, Chan A, Jackway P, Hrabar S, Dreccer MF, Holland E, Zheng B, Ling TJ, Jimenez-Berni J (2014) Pheno-Copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy (Basel) 4, 279–301.
| Pheno-Copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping.Crossref | GoogleScholarGoogle Scholar |
Daryaei A, Sohrabi H, Atzberger C, Immitzer M (2020) Fine-scale detection of vegetation in semi-arid mountainous areas with focus on riparian landscapes using Sentinel-2 and UAV data. Computers and Electronics in Agriculture 177, 105686
| Fine-scale detection of vegetation in semi-arid mountainous areas with focus on riparian landscapes using Sentinel-2 and UAV data.Crossref | GoogleScholarGoogle Scholar |
Deery DM, Rebetzke GJ, Jimenez-Berni JA, James RA, Condon AG, Bovill WD, Hutchinson P, Scarrow J, Davy R, Furbank RT (2016) Methodology for high-throughput field phenotyping of canopy temperature using airborne thermography. Frontiers in Plant Science 7, 1808
| Methodology for high-throughput field phenotyping of canopy temperature using airborne thermography.Crossref | GoogleScholarGoogle Scholar | 27999580PubMed |
Drzewiecki W (2016) Comparison of selected machine learning algorithms for sub-pixel imperviousness change assessment. In ‘2016 Baltic Geodetic Congress (BGC Geomatics)’, Gdansk, Poland. pp. 67–72. (IEEE: Gdansk, Poland)
Duan T, Zheng B, Guo W, Ninomiya S, Guo Y, Chapman SC (2017) Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV. Functional Plant Biology 44, 169–183.
| Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV.Crossref | GoogleScholarGoogle Scholar |
Gago J, Douthe C, Coopman RE, Gallego PP, Ribas-Carbo M, Flexas J, Escalona J, Medrano H (2015) UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management 153, 9–19.
| UAVs challenge to assess water stress for sustainable agriculture.Crossref | GoogleScholarGoogle Scholar |
Gebhardt S, Kühbauch W (2007) A new algorithm for automatic Rumex obtusifolius detection in digital images using colour and texture features and the influence of image resolution. Precision Agriculture 8, 1–13.
| A new algorithm for automatic Rumex obtusifolius detection in digital images using colour and texture features and the influence of image resolution.Crossref | GoogleScholarGoogle Scholar |
Gonias ED, Oosterhuis DM, Bibi AC, Purcell LC (2012) Estimating light interception by cotton using a digital imaging technique. American Journal of Experimental Agriculture 2, 1–8.
| Estimating light interception by cotton using a digital imaging technique.Crossref | GoogleScholarGoogle Scholar |
Großkinsky DK, Svensgaard J, Christensen S, Roitsch T (2015) Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. Journal of Experimental Botany 66, 5429–5440.
| Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap.Crossref | GoogleScholarGoogle Scholar | 26163702PubMed |
Gu D, Zhen F, Hannaway DB, Zhu Y, Liu L, Cao W, Tang L (2017) Quantitative classification of rice (Oryza sativa l.) root length and diameter using image analysis. PLoS One 12, e0169968
| Quantitative classification of rice (Oryza sativa l.) root length and diameter using image analysis.Crossref | GoogleScholarGoogle Scholar | 28961259PubMed |
Guo W, Rage UK, Ninomiya S (2013) Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model. Computers and Electronics in Agriculture 96, 58–66.
| Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model.Crossref | GoogleScholarGoogle Scholar |
Guo W, Zheng B, Duan T, Fukatsu T, Chapman S, Ninomiya S (2017) EasyPCC: benchmark datasets and tools for high-throughput measurement of the plant canopy coverage ratio under field conditions. Sensors 17, 798
| EasyPCC: benchmark datasets and tools for high-throughput measurement of the plant canopy coverage ratio under field conditions.Crossref | GoogleScholarGoogle Scholar |
Haghighattalab A, González Pérez L, Mondal S, Singh D, Schinstock D, Rutkoski J, Ortiz-Monasterio I, Singh RP, Goodin D, Poland J (2016) Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods 12, 35
| Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries.Crossref | GoogleScholarGoogle Scholar | 27347001PubMed |
Hansen MC, DeFries RS, Townshend JRG, Sohlberg R, Dimiceli C, Carroll M (2002) Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sensing of Environment 83, 303–319.
| Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data.Crossref | GoogleScholarGoogle Scholar |
Hengl T (2006) Finding the right pixel size. Computers & Geosciences 32, 1283–1298.
| Finding the right pixel size.Crossref | GoogleScholarGoogle Scholar |
Hsieh P-F, Lee LC, Chen N-Y (2001) Effect of spatial resolution on classification errors of pure and mixed pixels in remote sensing. IEEE Transactions on Geoscience and Remote Sensing 39, 2657–2663.
| Effect of spatial resolution on classification errors of pure and mixed pixels in remote sensing.Crossref | GoogleScholarGoogle Scholar |
Hu P, Chapman SC, Wang X, Potgieter A, Duan T, Jordan D, Guo Y, Zheng B (2018) Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: example for sorghum breeding. European Journal of Agronomy 95, 24–32.
| Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: example for sorghum breeding.Crossref | GoogleScholarGoogle Scholar |
Hu P, Guo W, Chapman SC, Guo Y, Zheng B (2019) Pixel size of aerial imagery constrains the applications of unmanned aerial vehicle in crop breeding. ISPRS Journal of Photogrammetry and Remote Sensing 154, 1–9.
| Pixel size of aerial imagery constrains the applications of unmanned aerial vehicle in crop breeding.Crossref | GoogleScholarGoogle Scholar |
Hunt ER, Horneck DA, Spinelli CB, Turner RW, Bruce AE, Gadler DJ, Brungardt JJ, Hamm PB (2018) Monitoring nitrogen status of potatoes using small unmanned aerial vehicles. Precision Agriculture 19, 314–333.
| Monitoring nitrogen status of potatoes using small unmanned aerial vehicles.Crossref | GoogleScholarGoogle Scholar |
Jamieson PD, Porter JR, Wilson DR (1991) A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research 27, 337–350.
| A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand.Crossref | GoogleScholarGoogle Scholar |
Jay S, Baret F, Dutartre D, Malatesta G, Héno S, Comar A, Weiss M, Maupas F (2019) Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops. Remote Sensing of Environment 231, 110898
| Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops.Crossref | GoogleScholarGoogle Scholar |
Jin X, Liu S, Baret F, Hemerlé M, Comar A (2017) Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment 198, 105–114.
| Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery.Crossref | GoogleScholarGoogle Scholar |
Jones HG, Sirault XRR (2014) Scaling of thermal images at different spatial resolution: the mixed pixel problem. Agronomy (Basel) 4, 380–396.
| Scaling of thermal images at different spatial resolution: the mixed pixel problem.Crossref | GoogleScholarGoogle Scholar |
Keshava N, Mustard JF (2002) Spectral unmixing. IEEE Signal Processing Magazine 19, 44–57.
| Spectral unmixing.Crossref | GoogleScholarGoogle Scholar |
Kipp S, Mistele B, Baresel P, Schmidhalter U (2014) High-throughput phenotyping early plant vigour of winter wheat. European Journal of Agronomy 52, 271–278.
| High-throughput phenotyping early plant vigour of winter wheat.Crossref | GoogleScholarGoogle Scholar |
Laliberte AS, Rango A, Herrick JE, Fredrickson EL, Burkett L (2007) An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. Journal of Arid Environments 69, 1–14.
| An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography.Crossref | GoogleScholarGoogle Scholar |
Lati RN, Filin S, Eizenberg H (2011) Robust Methods for Measurement of Leaf-Cover Area and Biomass from Image Data. Weed Science 59, 276–284.
| Robust Methods for Measurement of Leaf-Cover Area and Biomass from Image Data.Crossref | GoogleScholarGoogle Scholar |
Lee K-J, Lee B-W (2013) Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. European Journal of Agronomy 48, 57–65.
| Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis.Crossref | GoogleScholarGoogle Scholar |
Li Y, Chen D, Walker CN, Angus JF (2010) Estimating the nitrogen status of crops using a digital camera. Field Crops Research 118, 221–227.
| Estimating the nitrogen status of crops using a digital camera.Crossref | GoogleScholarGoogle Scholar |
Liebisch F, Kirchgessner N, Schneider D, Walter A, Hund A (2015) Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 11, 9
| Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach.Crossref | GoogleScholarGoogle Scholar | 25793008PubMed |
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28, 823–870.
| A survey of image classification methods and techniques for improving classification performance.Crossref | GoogleScholarGoogle Scholar |
Lu N, Zhou J, Han Z, Li D, Cao Q, Yao X, Tian Y, Zhu Y, Cao W, Cheng T (2019) Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system. Plant Methods 15, 17
| Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system.Crossref | GoogleScholarGoogle Scholar | 30828356PubMed |
Mahlein A-K (2016) Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease 100, 241–251.
| Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping.Crossref | GoogleScholarGoogle Scholar | 30694129PubMed |
Martínez J, Egea G, Agüera J, Pérez-Ruiz M (2017) A cost-effective canopy temperature measurement system for precision agriculture: a case study on sugar beet. Precision Agriculture 18, 95–110.
| A cost-effective canopy temperature measurement system for precision agriculture: a case study on sugar beet.Crossref | GoogleScholarGoogle Scholar |
Mullan DJ, Reynolds MP (2010) Quantifying genetic effects of ground cover on soil water evaporation using digital imaging. Functional Plant Biology 37, 703–712.
| Quantifying genetic effects of ground cover on soil water evaporation using digital imaging.Crossref | GoogleScholarGoogle Scholar |
Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q (2011) Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment 115, 1145–1161.
| Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery.Crossref | GoogleScholarGoogle Scholar |
Nielsen DC, Miceli-Garcia JJ, Lyon DJ (2012) Canopy Cover and Leaf Area Index Relationships for Wheat, Triticale, and Corn. Agronomy Journal 104, 1569–1573.
| Canopy Cover and Leaf Area Index Relationships for Wheat, Triticale, and Corn.Crossref | GoogleScholarGoogle Scholar |
Pan G, Li F, Sun G (2007) Digital camera based measurement of crop cover for wheat yield prediction. In ‘2007 IEEE International Geoscience and Remote Sensing Symposium’, Barcelona, Spain. pp. 797–800. (IEEE: Barcelona, Spain)
Pauli D, Chapman SC, Bart R, Topp CN, Lawrence-Dill CJ, Poland J, Gore MA (2016) The Quest for Understanding Phenotypic Variation via Integrated Approaches in the Field Environment. Plant Physiology 172, 622–634.
| The Quest for Understanding Phenotypic Variation via Integrated Approaches in the Field Environment.Crossref | GoogleScholarGoogle Scholar | 27482076PubMed |
Peña JM, Torres-Sánchez J, de Castro AI, Kelly M, López-Granados F (2013) Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images. PLoS One 8, e77151
| Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images.Crossref | GoogleScholarGoogle Scholar | 24146963PubMed |
Peña JM, Torres-Sánchez J, Serrano-Pérez A, de Castro AI, López-Granados F (2015) Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors 15, 5609–5626.
| Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution.Crossref | GoogleScholarGoogle Scholar | 25756867PubMed |
Prieto I, Stokes A, Roumet C (2016) Root functional parameters predict fine root decomposability at the community level. Journal of Ecology 104, 725–733.
| Root functional parameters predict fine root decomposability at the community level.Crossref | GoogleScholarGoogle Scholar |
Purcell LC (2000) Soybean canopy coverage and light interception measurements using digital imagery. Crop Science 40, 834–837.
| Soybean canopy coverage and light interception measurements using digital imagery.Crossref | GoogleScholarGoogle Scholar |
R Core Team (2019) ‘R: A language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna, Austria) https://www.R-project.org/.
Ranđelović P, Đorđević V, Milić S, Balešević-Tubić S, Petrović K, Miladinović J, Đukić V (2020) Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV. Agronomy (Basel) 10, 1108
| Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV.Crossref | GoogleScholarGoogle Scholar |
Sankaran S, Khot LR, Espinoza CZ, Jarolmasjed S, Sathuvalli VR, Vandemark GJ, Miklas PN, Carter AH, Pumphrey MO, Knowles NR, Pavek MJ (2015) Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy 70, 112–123.
| Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review.Crossref | GoogleScholarGoogle Scholar |
Sharma B, Ritchie GL (2015) High-throughput phenotyping of cotton in multiple irrigation environments. Crop Science 55, 958–969.
| High-throughput phenotyping of cotton in multiple irrigation environments.Crossref | GoogleScholarGoogle Scholar |
Shi Y, Thomasson JA, Murray SC, Pugh NA, Rooney WL, Shafian S, Rajan N, Rouze G, Morgan CLS, Neely HL, Rana A, Bagavathiannan MV, Henrickson J, Bowden E, Valasek J, Olsenholler J, Bishop MP, Sheridan R, Putman EB, Popescu S, Burks T, Cope D, Ibrahim A, McCutchen BF, Baltensperger DD, Jr RVA, Vidrine M, Yang C (2016) Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research. PLoS One 11, e0159781
| Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research.Crossref | GoogleScholarGoogle Scholar | 28033334PubMed |
Su J, Yi D, Su B, Mi Z, Liu C, Hu X, Xu X, Guo L, Chen W-H (2021) Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring. IEEE Transactions on Industrial Informatics 17, 2242–2249.
| Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring.Crossref | GoogleScholarGoogle Scholar |
Suzuki T, Ohta T, Izumi Y, Kanyomeka L, Mwandemele O, Sakagami J-I, Yamane K, Iijima M (2013) Role of Canopy Coverage in Water Use Efficiency of Lowland Rice in Early Growth Period in Semi-Arid Region. Plant Production Science 16, 12–23.
| Role of Canopy Coverage in Water Use Efficiency of Lowland Rice in Early Growth Period in Semi-Arid Region.Crossref | GoogleScholarGoogle Scholar |
Torres-Sánchez J, Peña JM, de Castro AI, López-Granados F (2014) Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture 103, 104–113.
| Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV.Crossref | GoogleScholarGoogle Scholar |
Torres-Sánchez J, López-Granados F, Peña JM (2015) An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture 114, 43–52.
| An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops.Crossref | GoogleScholarGoogle Scholar |
Tsutsumida N, Comber A, Barrett K, Saizen I, Rustiadi E (2016) Sub-pixel classification of MODIS EVI for annual mappings of impervious surface areas. Remote Sensing 8, 143
| Sub-pixel classification of MODIS EVI for annual mappings of impervious surface areas.Crossref | GoogleScholarGoogle Scholar |
van Evert FK, Booij R, Jukema JN, ten Berge HFM, Uenk D, Meurs EJJ, van Geel WCA, Wijnholds KH, Slabbekoorn JJ (2012) Using crop reflectance to determine sidedress N rate in potato saves N and maintains yield. European Journal of Agronomy 43, 58–67.
| Using crop reflectance to determine sidedress N rate in potato saves N and maintains yield.Crossref | GoogleScholarGoogle Scholar |
Waldner F, Defourny P (2017) Where can pixel counting area estimates meet user-defined accuracy requirements? International Journal of Applied Earth Observation and Geoinformation 60, 1–10.
| Where can pixel counting area estimates meet user-defined accuracy requirements?Crossref | GoogleScholarGoogle Scholar |
Walter A, Liebisch F, Hund A (2015) Plant phenotyping: from bean weighing to image analysis. Plant Methods 11, 14
| Plant phenotyping: from bean weighing to image analysis.Crossref | GoogleScholarGoogle Scholar | 25767559PubMed |
Xie Y, Sha Z, Yu M (2008) Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology 1, 9–23.
| Remote sensing imagery in vegetation mapping: a review.Crossref | GoogleScholarGoogle Scholar |
Xu M, Watanachaturaporn P, Varshney PK, Arora MK (2005) Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment 97, 322–336.
| Decision tree regression for soft classification of remote sensing data.Crossref | GoogleScholarGoogle Scholar |
Yan G, Li L, Coy A, Mu X, Chen S, Xie D, Zhang W, Shen Q, Zhou H (2019) Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing. ISPRS Journal of Photogrammetry and Remote Sensing 158, 23–34.
| Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing.Crossref | GoogleScholarGoogle Scholar |
Yang G, Liu J, Zhao C, Li Z, Huang Y, Yu H, Xu B, Yang X, Zhu D, Zhang X, Zhang R, Feng H, Zhao X, Li Z, Li H, Yang H (2017) Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives. Frontiers in Plant Science 8, 1111
| Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives.Crossref | GoogleScholarGoogle Scholar | 28713402PubMed |
Yang M-D, Tseng H-H, Hsu Y-C, Tsai HP (2020) Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images. Remote Sensing 12, 633
| Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images.Crossref | GoogleScholarGoogle Scholar |
Yu Q, Gong P, Clinton N, Biging G, Kelly M, Schirokauer D (2006) Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing 72, 799–811.
| Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery.Crossref | GoogleScholarGoogle Scholar |
Zhang T, Su J, Liu C, Chen W-H (2019) Bayesian calibration of AquaCrop model for winter wheat by assimilating UAV multi-spectral images. Computers and Electronics in Agriculture 167, 105052
| Bayesian calibration of AquaCrop model for winter wheat by assimilating UAV multi-spectral images.Crossref | GoogleScholarGoogle Scholar |
