[1]

Cabrera-Bosquet L, Crossa J, von Zitzewitz J, Serret MD, Araus JL. 2012. High-throughput phenotyping and genomic selection: The frontiers of crop breeding converge. Journal of Integrative Plant Biology 54:312−20

doi: 10.1111/j.1744-7909.2012.01116.x
[2]

Montes JM, Melchinger AE, Reif JC. 2007. Novel throughput phenotyping platforms in plant genetic studies. Trends in Plant Science 12:433−36

doi: 10.1016/j.tplants.2007.08.006
[3]

Araus JL, Cairns JE. 2014. Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science 19:52−61

doi: 10.1016/j.tplants.2013.09.008
[4]

Li D, Li C, Yao Y, Li M, Liu L. 2020. Modern imaging techniques in plant nutrition analysis: A review. Computers and Electronics in Agriculture 174:14

doi: 10.1016/j.compag.2020.105459
[5]

Song P, Wang J, Guo X, Yang W, Zhao C. 2021. High-throughput phenotyping: Breaking through the bottleneck in future crop breeding. The Crop Journal 9:633−45

doi: 10.1016/j.cj.2021.03.015
[6]

Lee KJ, Lee BW. 2013. Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. European Journal of Agronomy 48:57−65

doi: 10.1016/j.eja.2013.02.011
[7]

Tomita H, Fukuoka M, Takemori T, Sakai N. 2019. Development of the visualization and quantification method of the rice soaking process by using the digital microscope. Journal of Food Engineering 243:33−38

doi: 10.1016/j.jfoodeng.2018.08.034
[8]

Wang Y, Wang D, Zhang G, Wang J. 2013. Estimating nitrogen status of rice using the image segmentation of G-R thresholding method. Field Crops Research 149:33−39

doi: 10.1016/j.fcr.2013.04.007
[9]

Scharf PC, Lory JA. 2002. Calibrating corn color from aerial photographs to predict sidedress nitrogen need. Agronomy Journal 94:397−404

doi: 10.2134/agronj2002.0397
[10]

Li Q, Li H, Gao Q. 2015. The influence of different sugars on corn starch gelatinization process with digital image analysis method. Food Hydrocolloids 43:803−11

doi: 10.1016/j.foodhyd.2014.08.012
[11]

Li Y, Chen D, Walker CN, Angus JF. 2010. Estimating the nitrogen status of crops using a digital camera. Field Crops Research 118:221−27

doi: 10.1016/j.fcr.2010.05.011
[12]

Lu N, Zhou J, Han Z, Li D, Cao Q, et al. 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

doi: 10.1186/s13007-019-0402-3
[13]

El-Hendawy SE, Al-Suhaibani NA, Hassan WM, Dewir YH, Elsayed S, et al. 2019. Evaluation of wavelengths and spectral reflectance indices for high-throughput assessment of growth, water relations and ion contents of wheat irrigated with saline water. Agricultural Water Management 212:358−77

doi: 10.1016/j.agwat.2018.09.009
[14]

Gonçalves MIS, Vilar WTS, Medeiros EP, Pontes MJC. 2016. An analytical method for determination of quality parameters in cotton plumes by digital image and chemometrics. Computers and Electronics in Agriculture 123:89−94

doi: 10.1016/j.compag.2016.02.007
[15]

Alganci U, Ozdogan M, Sertel E, Ormeci C. 2014. Estimating maize and cotton yield in southeastern Turkey with integrated use of satellite images, meteorological data and digital photographs. Field Crops Research 157:8−19

doi: 10.1016/j.fcr.2013.12.006
[16]

Jiang Y, Li C, Paterson AH. 2016. High throughput phenotyping of cotton plant height using depth images under field conditions. Computers and Electronics in Agriculture 130:57−68

doi: 10.1016/j.compag.2016.09.017
[17]

Bendig J, Bolten A, Bennertz S, Broscheit J, Eichfuss S, et al. 2014. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing 6:10395−412

doi: 10.3390/rs61110395
[18]

Fischer RA. 1993. Irrigated spring wheat and timing and amount of nitrogen fertilizer. II. Physiology of grain yield response. Field Crops Research 33:57−80

doi: 10.1016/0378-4290(93)90094-4
[19]

Hussain S, Ali H, Gardezi STR. 2021. Soil applied potassium improves productivity and fiber quality of cotton cultivars grown on potassium deficient soils. PLoS One 16:e0250713

doi: 10.1371/journal.pone.0250713
[20]

Yao H, Zhang Y, Yi X, Hu Y, Luo H, et al. 2015. Plant density alters nitrogen partitioning among photosynthetic components, leaf photosynthetic capacity and photosynthetic nitrogen use efficiency in field-grown cotton. Field Crops Research 184:39−49

doi: 10.1016/j.fcr.2015.09.005
[21]

Li B, Xu X, Zhang L, Han J, Bian C, et al. 2020. Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS - Journal of Photogrammetry and Remote Sensing 162:161−72

doi: 10.1016/j.isprsjprs.2020.02.013
[22]

Cruz de Carvalho MH. 2008. Drought stress and reactive oxygen species: Production, scavenging and signaling. Plant Signaling and Behavior 3:156−65

doi: 10.4161/psb.3.3.5536
[23]

Lafitte HR, Guan YS, Yan S, Li ZK. 2007. Whole plant responses, key processes, and adaptation to drought stress: the case of rice. Journal of Experimental Botany 58:169−75

doi: 10.1093/jxb/erl101
[24]

Tebaldi C, Lobell DB. 2008. Towards probabilistic projections of climate change impacts on global crop yields. Geophysical Research Letters 35:L08705

doi: 10.1029/2008gl033423
[25]

Asaari MSM, Mertens S, Dhondt S, Inzé D, Wuyts N, Scheunders P. 2019. Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform. Computers and Electronics in Agriculture 162:749−58

doi: 10.1016/j.compag.2019.05.018
[26]

Fisher M. 2015. Canopeo: a new tool to measure green canopy cover. CSA News 60:14−15

doi: 10.2134/csa2015-60-11-3
[27]

Govindasamy P, Mahawer SK, Sarangi D, Halli HM, Das TK, et al. 2022. The Comparison of Canopeo and Samplepoint for Measurement of Green Canopy Cover for Forage Crops in India. MethodsX 9:101916

doi: 10.1016/j.mex.2022.101916
[28]

Chung YS, Choi SC, Silva RR, Kang JW, Eom JH, et al. 2017. Case study: Estimation of sorghum biomass using digital image analysis with Canopeo. Biomass and Bioenergy 105:207−10

doi: 10.1016/j.biombioe.2017.06.027
[29]

Ma Y, Zhang Q, Yi X, Ma L, Zhang L, et al. 2022. Estimation of cotton leaf area index (LAI) based on spectral transformation and vegetation index. Remote Sensing 14:136

doi: 10.3390/rs14010136
[30]

Sun B, Wang C, Yang C, Xu B, Zhou G, et al. 2021. Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter. International Journal of Applied Earth Observation and Geoinformation 102:102373

doi: 10.1016/j.jag.2021.102373
[31]

Guo X, Wang M, Jia M, Wang W. 2021. Estimating mangrove leaf area index based on red-edge vegetation indices: A comparison among UAV, WorldView-2 and Sentinel-2 imagery. International Journal of Applied Earth Observation and Geoinformation 103:102493

doi: 10.1016/j.jag.2021.102493
[32]

Zhang S, Han S, Jin H. 2006. Application of digital image processing in detection of foreign fibers in cotton. Optical Technique 32:584−86

[33]

Li H, Wang G, Dong Z, Wei X, Wu M, et al. 2021. Identifying cotton fields from remote sensing images using multiple deep learning networks. Agronomy 11:174

doi: 10.3390/agronomy11010174
[34]

Olson ML, Khanna R, Neal L, Li F, Wong WK. 2021. Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295:103455

doi: 10.1016/j.artint.2021.103455
[35]

Maimaitijiang M, Sagan V, Sidike P, Hartling S, Esposito F, et al. 2020. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment 237:111599

doi: 10.1016/j.rse.2019.111599
[36]

Liu J, Zhang W. 2018. Study on the application of a rapid diagnosis method of wheat population on mobile phone in the turning green period. Agriculture and Technology 38:1−4

[37]

Ma J, Liu H, Zheng F, Du K, Zhang L, et al. 2019. Estimation of winter wheat seedling growth parameters based on visible light image and convolutional neural network. Journal of Agricultural Engineering 35:183−89

[38]

Zhang L, Chen Y, Li Y, Ma J, Du K, et al. 2019. Estimation of aboveground biomass of Winter Wheat Seedlings by visible light spectroscopy. Spectroscopy and Spectral Analysis 39:2501−06

[39]

Soja MJ, Quegan S, d'Alessandro MM, Banda F, Scipal K, et al. 2021. Mapping above-ground biomass in tropical forests with ground-cancelled P-band SAR and limited reference data. Remote Sensing of Environment 253:112153

doi: 10.1016/j.rse.2020.112153
[40]

Schell JA, Rouse JW, Haas RH, Deering DW, Harlan JC. 1974. Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. NASA/GSFC, Greenbelt, Maryland, USA. pp. 11−371

[41]

Behrens T, Diepenbrock W. 2006. Using digital image analysis to describe canopies of winter oilseed rape (Brassica napus L.) during vegetative developmental stages. Journal of Agronomy and Crop Science 192:295−302

doi: 10.1111/j.1439-037x.2006.00211.x
[42]

Husson E, Lindgren F, Ecke F. 2014. Assessing biomass and metal contents in riparian vegetation along a pollution gradient using an unmanned aircraft system. Water, Air, & Soli Pollution 225:1957

doi: 10.1007/s11270-014-1957-2
[43]

Adar S, Sternberg M, Paz-Kagan T, Henkin Z, Dovrat G, et al. 2022. Estimation of aboveground biomass production using an unmanned aerial vehicle (UAV) and VENμS satellite imagery in Mediterranean and semiarid rangelands. Remote Sensing Applications: Society and Environment 26:100753

doi: 10.1016/j.rsase.2022.100753
[44]

Tian Y, Huang H, Zhou G, Zhang Q, Tao J, et al. 2021. Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing. Science of the Total Environment 781:146816

doi: 10.1016/j.scitotenv.2021.146816
[45]

Lu J, Wang H, Qin S, Cao L, Pu R, et al. 2020. Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds. International Journal of Applied Earth Observation and Geoinformation 86:102014

doi: 10.1016/j.jag.2019.102014
[46]

Zhang Y, Xia C, Zhang X, Cheng X, Feng G, et al. 2021. Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images. Ecological Indicators 129:107985

doi: 10.1016/j.ecolind.2021.107985
[47]

Ma J, Du K, Zhang L, Zheng F, Chu J, et al. 2017. A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing. Computers and Electronics in Agriculture 142:110−17

doi: 10.1016/j.compag.2017.08.023
[48]

Prabhakara K, Hively WD, McCarty GW. 2015. Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States. International Journal of Applied Earth Observation and Geoinformation 39:88−102

doi: 10.1016/j.jag.2015.03.002
[49]

Patrignani A, Ochsner TE. 2015. Canopeo: A powerful new tool for measuring fractional green canopy cover. Agronomy Journal 107:2312−20

doi: 10.2134/agronj15.0150
[50]

González-Esquiva JM, Oates MJ, García-Mateos G, Moros-Valle B, Molina-Martínez JM, et al. 2017. Development of a visual monitoring system for water balance estimation of horticultural crops using low cost cameras. Computers and Electronics in Agriculture 141:15−26

doi: 10.1016/j.compag.2017.07.001
[51]

Yellareddygari SKR, Gudmestad NC. 2017. Bland-Altman comparison of two methods for assessing severity of Verticillium wilt of potato. Crop Protection 101:68−75

doi: 10.1016/j.cropro.2017.07.019