| - Grain yield – Productivity - Phenotyping |
27.8% |
Shafiee et al. (2021)SHAFIEE, S. et al. Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery. Computers and Electronics in Agriculture, v. 183, abr. 2021., Volpato et al. (2021)VOLPATO, L. et al. High throughput field phenotyping for plant height using UAV-based RGB imagery in wheat breeding lines: feasibility and validation. Frontiers in Plant Science, v. 12, fev. 2021., Zhou et al. (2021)ZHOU, X. et al. Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches. Plant Production Science, v. 24, n. 2, p. 137-151, abr. 2021., Fernandez-Gallego et al. (2020)FERNANDEZ-GALLEGO, J. A. et al. Automatic wheat ear counting using machine learning based on RGB UAV imagery. Plant Journal, v. 103, n. 4, p. 1603-1613, ago. 2020., Moghimi, Yang and Anderson (2020)MOGHIMI, A.; YANG, C.; ANDERSON, J. A. Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat. Computers and Electronics in Agriculture, v. 172, maio 2020., Bukowiecki et al. (2020)BUKOWIECKI, J. et al. High-throughput prediction of whole season green area index in winter wheat with an airborne multispectral sensor. Frontiers in Plant Science, v. 10, fev. 2020., Z. Fu et al. (), J. Jiang et al. (2019a)JIANG, J. et al. Analysis and evaluation of the image preprocessing process of a six-band multispectral camera mounted on an unmanned aerial vehicle for winter wheat monitoring. Sensors, v. 19, n. 3, fev. 2019a., Ma et al. (2020)MA, J. et al. Segmenting ears of winter wheat at flowering stage using digital images and deep learning. Computers and Electronics in Agriculture, v. 168, jan. 2020., Li et al. (2019)LI, J. et al. Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery. Plant Methods, v. 15, n. 1, nov. 2019., Sadeghi-Tehran et al. (2019)SADEGHI-TEHRAN, P. et al. Deepcount: in-field automatic quantification of wheat spikes using simple linear iterative clustering and deep convolutional neural networks. Frontiers in Plant Science, v. 10, set. 2019., Ostos-Garrido et al. (2019)OSTOS-GARRIDO, F. J. et al. High-throughput phenotyping of bioethanol potential in cereals using UAV-based multi-spectral i m a g e r y. Frontiers in Plant Science, v. 10, jul. 2019., Holman et al. (2019)HOLMAN, F. H. et al. Radiometric calibration of ``commercial off the shelf’ cameras for UAV based highresolution temporal crop phenotyping of reflectance and NDVI. Remote Sensing, v. 11, n. 14, jul. 2019., Hassan et al. (2019)HASSAN, M. A. et al. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, v. 282, p. 95-103, maio 2019., Madec et al. (2019)MADEC, S. et al. Ear density estimation from high resolution RGB imagery using deep learning technique. Agricultural and Forest Meteorology, v. 264, p. 225-234, jan. 2019., Guan et al. (2019)GUAN, S. et al. Assessing correlation of high-resolution ndvi with fertilizer application level and yield of rice and wheat crops using small UAVs. Remote Sensing, v. 11, n. 2, jan. 2019., Kanning et al. (2018)KANNING, M. et al. High-resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sensing, v. 10, n. 12, dez. 2018., Z. Fu et al. (2021), J. J. Jiang et al. (2020)JIANG, J. et al. Use of an active canopy sensor mounted on an unmanned aerial vehicle to monitor the growth and nitrogen status of winter wheat. Remote Sensing, v. 12, n. 22, nov. 2020.; Haghighattalab et al. (2016)HAGHIGHATTALAB, A. et al. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods, v. 12, jun. 2016., Sankaran, Khot and Carter (2015)SANKARAN, S.; KHOT, L. R.; CARTER, A. H. Field-based crop phenotyping: multispectral aerial imaging for evaluation of winter wheat emergence and spring stand. Computers and Electronics in Agriculture, v. 118, p. 372-379, out. 2015. and Overgaard et al. (2010)OVERGAARD, S. I. et al. Comparisons of two hand-held, multispectral field radiometers and a hyperspectral airborne imager in terms of predicting spring wheat grain yield and quality by means of powered partial least squares regression. Journal of Near Infrared Spectroscopy, v. 18, n. 4, p. 247-261, 2010.. |
| - Nutritional status - Nitrogen fertilization |
19.4% |
Fiorentini, Zenobi and Orsini (2021)FIORENTINI, M.; ZENOBI, S.; ORSINI, R. Remote and proximal sensing applications for durum wheat nutritional status detection in mediterranean area. Agriculture-Basel, v. 11, n. 1, jan. 2021., Jie Jiang et al. (2020)JIANG, J. et al. Use of an active canopy sensor mounted on an unmanned aerial vehicle to monitor the growth and nitrogen status of winter wheat. Remote Sensing, v. 12, n. 22, nov. 2020., Y. Fu (2021)FU, Y. et al. Improved estimation of winter wheat aboveground biomass using multiscale textures extracted from UAV-based digital images and hyperspectral feature analysis. Remote Sensing, v. 13, n. 4, fev. 2021., G. Yang et al. ( ), H. Liu et al. (2020)LIU, H. et al. Quantitative analysis and hyperspectral remote sensing of the nitrogen nutrition index in winter wheat. International Journal of Remote Sensing, v. 41, n. 3, p. 858-881, fev. 2020., Lu (2019a)LU, N. et al. 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, v. 15, fev. 2019a., Wang et al. (2019)WANG, L. et al. Bibliometric analysis of remote sensing research trend in crop growth monitoring: a case study in china. Remote Sens, v. 11, n. 7, 809, 2019., Jiale Jiang (2021), Cai et al. ( ), Dong et al. (2019)DONG, T. et al. Assessment of red-edge vegetation indices for crop leaf area index estimation. Remote Sensing of Environment, v. 222, p. 133-143, 2019., Chen et al. (2019)CHEN, Z. et al. In-season diagnosis of winter wheat nitrogen status in smallholder farmer fields across a village using unmanned aerial vehicle-based remote sensing. Agronomy-Basel, v. 9, n. 10, out. 2019., H. Zhao et al. (2019)ZHAO, H. et al. Monitoring of nitrogen and grain protein content in winter wheat based on Sentinel-2A data. Remote Sensing, v. 11, n. 14, jul. 2019., L. Yao et al. (2019)YAO, L. et al. UAV-borne dual-band sensor method for monitoring physiological crop status. Sensors, v. 19, n. 4, fev. 2019., H. Zheng et al. (2018)ZHENG, H. et al. A comparative assessment of different modeling algorithms for estimating leaf nitrogen content in winter wheat using multispectral images from an unmanned aerial vehicle. Remote Sensing, v. 10, n. 12, dez. 2018., Zhu et al. (2018)ZHU, H. et al. UAV-based hyperspectral analysis and spectral indices constructing for quantitatively monitoring leaf nitrogen content of winter wheat. Applied Optics, v. 57, n. 27, p. 7722-7732, set. 2018., Latif et al. (2018)LATIF, M. A. et al. Mapping wheat response to variations in N, P, Zn, and irrigation using an unmanned aerial vehicle. International Journal of Remote Sensing, v. 39, n. 21, p. 7172 7188, 2018.. |
| - Monitoring - Spectro-temporal |
19.4% |
T. Zhang et al. (2021)ZHANG, T. et al. State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter. Computers and Electronics in Agriculture, v. 180, jan. 2021., Z. Fu et al. ( ), J. Jiang et al. (2019b)JIANG, J. et al. Using digital cameras on an unmanned aerial vehicle to derive optimum color vegetation indices for leaf nitrogen concentration monitoring in winter wheat. Remote Sensing, v. 11, n. 22, nov. 2019b.; Revill et al. ( ), S. Zhang et al. (2019)ZHANG, S. et al. Integrated satellite, unmanned aerial vehicle (UAV) and ground inversion of the SPAD of winter wheat in the reviving stage. Sensors, v. 19, n. 7, abr. 2019., Jiale Jiang (2021), Zheng et al. ( ), J. Zhao et al. (2018)ZHAO, J. et al. Fusion of unmanned aerial vehicle panchromatic and hyperspectral images combining joint skewness-kurtosis figures and a non-subsampled contourlet transform. Sensors, v. 18, n. 10, out. 2018., Honkavaara and Khoramshahi (2018)HONKAVAARA, E.; KHORAMSHAHI, E. Radiometric correction of close-range spectral image blocks captured using an unmanned aerial vehicle with a radiometric block adjustment. Remote Sensing, v. 10, n. 2, fev. 2018., X. Yao et al. (2017)YAO, X. et al. Estimation of wheat LAI at middle to high levels using unmanned aerial vehicle narrowband multispectral imagery. Remote Sensing, v. 9, n. 12, 2017., Mengmeng et al. (2017)MENGMENG, D. et al. Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle. International Journal of Agricultural and Biological Engineering, v. 10, n. 5, p. 1-13, set. 2017., Roosjen et al. (2016)ROOSJEN, P. P. J. et al. Hyperspectral reflectance anisotropy measurements using a pushbroom spectrometer on an unmanned aerial vehicle-results for barley, winter wheat, and potato. Remote Sensing, v. 8, n. 11, nov. 2016., Schirrmann et al. (2016)SCHIRRMANN, M. et al. Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery. Remote Sensing, v. 8, n. 9, set. 2016., Burkart et al. (2015)BURKART, A. et al. Angular dependency of hyperspectral measurements over wheat characterized by a novel UAV based goniometer. Remote Sensing, v. 7, n. 1, p. 725-746, jan. 2015., Honkavaara et al. (2013)HONKAVAARA, E. et al. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sensing, v. 5, n. 10, p. 5006-5039, out. 2013., Lelong et al. (2008)LELONG, C. C. D. et al. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, v. 8, n. 5, p. 3557-3585, maio 2008.. |
| - Biomass - Leaf area index - Chlorophyll |
16.7% |
Khadka et al. (2021)KHADKA, K. et al. Does leaf waxiness confound the use of NDVI in the assessment of chlorophyll when evaluating genetic diversity panels of wheat? Agronomy-Basel, v. 11, n. 3, mar. 2021., Y. Fu (2020)FU, Y. et al. Winter wheat nitrogen status estimation using UAV-based RGB imagery and Gaussian processes regression. Remote Sensing, v. 12, n. 22, nov. 2020., G. Yang et al. ( ), Banerjee, Spangenberg and Kant (2020)BANERJEE, B. P. ; SPANGENBERG, G.; KANT, S. Fusion of spectral and structural information from aerial images for improved biomass estimation. Remote Sensing, v. 12, n. 19, out. 2020., Revill ( ), Tao et al. (2020)TAO, H. et al. Estimation of crop growth parameters using UAV-based hyperspectral remote sensing data. Sensors, v. 20, n. 5, mar. 2020., Hasan et al. (2019)HASAN, U.; SAWUT, M.; CHEN, S. Estimating the leaf area index of winter wheat based on unmanned aerial vehicle RGB-image parameters. Sustainability, v. 11, n. 23, dez. 2019., Yue et al. (2019)YUE, J. et al. Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing, v. 150, p. 226-244, abr. 2019., Lu ( ), Zhou et al. (2021)ZHOU, X. et al. Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches. Plant Production Science, v. 24, n. 2, p. 137-151, abr. 2021., Kanning et al. (2018)KANNING, M. et al. High-resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sensing, v. 10, n. 12, dez. 2018., Mozgeris et al. (2018)MOZGERIS, G. et al. Imaging from manned ultra-light and unmanned aerial vehicles for estimating properties of spring wheat. Precision Agriculture, v. 19, n. 5, p. 876-894, out. 2018., X. Yao et al. (2017)YAO, X. et al. Estimation of wheat LAI at middle to high levels using unmanned aerial vehicle narrowband multispectral imagery. Remote Sensing, v. 9, n. 12, 2017.. |
| - Diseases - Pests |
15.4% |
Su (2021)SU, J. et al. Aerial visual perception in smart farming: field study of wheat yellow rust monitoring. IEEE Transactions on Industrial Informatics, v. 17, n. 3, p. 2242-2249, mar. 2021., Yi et al. ( ), Guo et al. (2021)GUO, A. et al. Wheat yellow rust detection using UAV-based hyperspectral technology. Remote Sensing, v. 13, n. 1, jan. 2021., Heidarian Dehkordi et al. (2020)HEIDARIAN DEHKORDI, R. et al. Monitoring wheat leaf rust and stripe rust in winter wheat using high-resolution UAV-based red-green-blue imagery. Remote Sensing, v. 12, n. 22, nov. 2020., L. Liu et al. (2020)LIU, L. et al. Monitoring wheat fusarium head blight using unmanned aerial vehicle hyperspectral imagery. Remote Sensing, v. 12, n. 22, nov. 2020., Q. Zheng et al. (2020)ZHENG, Q. et al. Using continous wavelet analysis for monitoring wheat yellow rust in different infestation stages based on unmanned aerial vehicle hyperspectral images. Applied Optics, v. 59, n. 26, p. 8003-8013, set. 2020., Bhandari et al. (2020)BHANDARI, M. et al. Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV). Computers and Electronics in Agriculture, v. 176, set. 2020., Su (2019)SU, J. et al. Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery. Computers and Electronics in Agriculture, v. 167, dez. 2019., Liu et al. ( ), Bohnenkamp, Behmann and Mahlein (2019)BOHNENKAMP, D.; BEHMANN, J.; MAHLEIN, A.-K. In-field detection of yellow rust in wheat on the ground canopy and UAV scale. Remote Sensing, v. 11, n. 21, nov. 2019., Rasmussen et al. (2019)RASMUSSEN, J. et al. Pre-harvest weed mapping of cirsium arvense in wheat and barley with off-the-shelf UAVs. Precision Agriculture, v. 20, n. 5, p. 983-999, out. 2019., Mateen and Zhu (2019)MATEEN, A. ; ZHU, Q. Weed detection in wheat crop using UAV for precision agriculture. Pakistan Journal of Agricultural Sciences, v. 56, n. 3, p. 809-817, set. 2019., X. Zhang et al. (2019)ZHANG, X. et al. A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sensing, v. 11, n. 13, jul. 2019., Torres-Sanchez, Lopez-Granados and Pena (2015)TORRES-SANCHEZ, J.; LOPEZ-GRANADOS, F.; PENA, J. M. An automatic object-based method for optimal thresholding in UAV images: application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture, v. 114, p. 43-52, jun. 2015.. |
| - Population - Uniformity |
6.9% |
T. Liu et al. (2017)LIU, T. et al. Evaluation of seed emergence uniformity of mechanically sown wheat with UAV RGB imagery. Remote Sensing, v. 9, n. 12, dez. 2017., Hu, Chapman and Zheng (2021)HU, P.; CHAPMAN, S. C.; ZHENG, B. Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops. Functional Plant Biology, v. 48, n. 8, p. 766-779, 2021., Jin et al. (2017)JIN, X. et al. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, v. 198, p. 105-114, set. 2017., Schirrmann et al. (2016)SCHIRRMANN, M. et al. Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery. Remote Sensing, v. 8, n. 9, set. 2016. and Sankaran, Khot and Carter (2015)SANKARAN, S.; KHOT, L. R.; CARTER, A. H. Field-based crop phenotyping: multispectral aerial imaging for evaluation of winter wheat emergence and spring stand. Computers and Electronics in Agriculture, v. 118, p. 372-379, out. 2015.. |
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