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Marino, S., & Alvino, A. (2021) · Remote Sensing · doi:10.3390/rs13040541 · Cited by 41 · see citing works

Evaluation of UAV multispectral cameras for yield and biomass prediction in wheat under different sun elevation angles and phenological stages

Shafiee, S., Mróz, T. L., Burud, I., & Lillemo, M. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107874 · Cited by 40 · see citing works

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

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Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

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Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

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Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing

Murphy, M. E., Boruff, B. J., Callow, N., & Flower, K. C. (2020) · Remote Sensing · doi:10.3390/rs12030477 · Cited by 34 · see citing works

Identification of High Nitrogen Use Efficiency Phenotype in Rice (Oryza sativa L.) Through Entire Growth Duration by Unmanned Aerial Vehicle Multispectral Imagery

Liang, T. B., Duan, B., Luo, X., Ma, Y., Yuan, Z., Zhu, R., Peng, Y., Gong, Y., Fang, S., & Wu, X. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.740414 · Cited by 34 · see citing works

The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

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Smart Breeding for Climate Resilient Agriculture

Bakala, H. S., Singh, G., & Srivastava, P. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.94847 · Cited by 34 · see citing works

Multispectral Vegetation Indices and Machine Learning Approaches for Durum Wheat (Triticum durum Desf.) Yield Prediction across Different Varieties

Badagliacca, G., Messina, G., Praticò, S., Presti, E. L., Preiti, G., Monti, M., & Modica, G. (2023) · AgriEngineering · doi:10.3390/agriengineering5040125 · Cited by 30 · see citing works

Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes

Yousfi, S., Marin, J. F., Parra, L., Lloret, J., & Mauri, P. V. (2022) · Agricultural Water Management · doi:10.1016/j.agwat.2022.107581 · Cited by 28 · see citing works

Field identification of drought tolerant wheat genotypes using canopy vegetation indices instead of plant physiological and biochemical traits

Wen, P., Meng, Y., Gao, C., Guan, X., Wang, T., & Feng, W. (2023) · Ecological Indicators · doi:10.1016/j.ecolind.2023.110781 · Cited by 27 · see citing works

Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models

Fu, H., Lu, J., Li, J., Zou, W., Tang, X., Ning, X., & Sun, Y. (2025) · Agronomy · doi:10.3390/agronomy15010205 · Cited by 27 · see citing works

Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high‐throughput phenotyping

Wang, J., Li, X., Guo, T., Dzievit, M. J., Yu, X., Liu, P., Price, K. P., & Yu, J. (2021) · The Plant Genome · doi:10.1002/tpg2.20155 · Cited by 27 · see citing works

Enhancing phenotyping efficiency in faba bean breeding: integrating UAV imaging and machine learning

Mohammadi, S., Uhlen, A. K., Lillemo, M., Ergon, Å., & Shafiee, S. (2024) · Precision Agriculture · doi:10.1007/s11119-024-10121-4 · Cited by 26 · see citing works

Genome Wide Association Study Uncovers the QTLome for Osmotic Adjustment and Related Drought Adaptive Traits in Durum Wheat

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Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics

Marzougui, A., McGee, R. J., Vleet, S. M. V., & Sankaran, S. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1111575 · Cited by 23 · see citing works

Forecasting Yield and Lignocellulosic Composition of Energy Cane Using Unmanned Aerial Systems

Cholula, U., da Silva, J. A., Marconi, T. G., Thomasson, J. A., Diaz, J. S., & Enciso, J. (2020) · Agronomy · doi:10.3390/agronomy10050718 · Cited by 23 · see citing works

The feasibility of using a low-cost near-infrared, sensitive, consumer-grade digital camera mounted on a commercial UAV to assess Bambara groundnut yield

Jewan, S. Y. Y., Pagay, V., Billa, L., Tyerman, S. D., Gautam, D., Sparkes, D. L., Chai, H. H., & Singh, A. K. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1974116 · Cited by 22 · see citing works

Application and Potential of Drone Technology in Oil Palm Plantation: Potential and Limitations

Khuzaimah, Z., Nawi, N. M., Adam, S. N., Kalantar, B., Emeka, O. J., & Ueda, N. (2022) · Journal of Sensors · doi:10.1155/2022/5385505 · Cited by 22 · see citing works

Early Detection of Drought Stress in Durum Wheat Using Hyperspectral Imaging and Photosystem Sensing

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Use of consumer-grade cameras to assess wheat N status and grain yield

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Comparison of Machine Learning Algorithms for Wildland-Urban Interface Fuelbreak Planning Integrating ALS and UAV-Borne LiDAR Data and Multispectral Images

Rodríguez, F., Ponce, R. A., Pérez-Rodríguez, F., Águeda, B. F., Martín-García, S., Martínez-Rodrigo, R., & Lizarralde, I. (2020) · Drones · doi:10.3390/drones4020021 · Cited by 22 · see citing works

High-resolution satellite image to predict peanut maturity variability in commercial fields

dos Santos, A. F., Corrêa, L. N., Lacerda, L. N., Tedesco, D., Pilon, C., Vellidis, G., & da Silva, R. P. (2021) · Precision Agriculture · doi:10.1007/s11119-021-09791-1 · Cited by 21 · see citing works

Evaluation of drought tolerance of wheat genotypes in rain-fed sodic soil environments using high-resolution UAV remote sensing techniques

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Hyperspectral Reflectance as a Basis to Discriminate Olive Varieties—A Tool for Sustainable Crop Management

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Image phenotyping of lettuce germplasm with genetically diverse carotenoid levels

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The Uncertainty Assessment by the Monte Carlo Analysis of NDVI Measurements Based on Multispectral UAV Imagery

Khalesi, F., Ahmed, I., Daponte, P., Picariello, F., Vito, L. D., & Tudosa, I. (2024) · Sensors · doi:10.3390/s24092696 · Cited by 19 · see citing works

Unmanned aerial vehicle (UAV) remote sensing estimation of wheat chlorophyll in subsidence area of coal mine with high phreatic level

Hu, X. F., Niu, B., Li, X., & Min, X. (2021) · Earth Science Informatics · doi:10.1007/s12145-021-00676-5 · Cited by 19 · see citing works

Validating UAS-Based Photogrammetry with Traditional Topographic Methods for Surveying Dune Ecosystems in the Spanish Mediterranean Coast

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Utilizing UAV-based high-throughput phenotyping and machine learning to evaluate drought resistance in wheat germplasm

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Towards improving the precision agriculture management of the wheat crop using remote sensing: A case study in Central Non-Black Earth region of Russia

Rebouh, N. Y., Mohamed, E. S., Политыко, П. М., Dokukin, P. A., Kucher, D. E., Latati, M., Okeke, S. E., & Ali, M. A. (2023) · The Egyptian Journal of Remote Sensing and Space Science · doi:10.1016/j.ejrs.2023.06.007 · Cited by 18 · see citing works

Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data

Morota, G., Jarquín, D., Campbell, M. T., & Iwata, H. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2537-8_21 · Cited by 18 · see citing works

Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field

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Identification of Quantitative Trait Loci Hotspots Affecting Agronomic Traits and High-Throughput Vegetation Indices in Rainfed Wheat

Rufo, R., López, A., Lopes, M. S., Bellvert, J., & Soriano, J. M. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.735192 · Cited by 16 · see citing works

Spectral Reflectance Indices as a High Throughput Selection Tool in a Sesame Breeding Scheme

Petsoulas, C., Evangelou, E., Tsitouras, A., Aschonitis, V. G., Kargiotidou, A., KHAH, E. M., Pavli, O. I., & Vlachostergios, D. Ν. (2022) · Remote Sensing · doi:10.3390/rs14112629 · Cited by 14 · see citing works

Using Unmanned Aerial Vehicle and Ground-Based RGB Indices to Assess Agronomic Performance of Wheat Landraces and Cultivars in a Mediterranean-Type Environment

Rufo, R., Soriano, J. M., Villegas, D., Royo, C., & Bellvert, J. (2021) · Remote Sensing · doi:10.3390/rs13061187 · Cited by 14 · see citing works

Assessment of the suitability of Triticum turgidum accessions for incorporation into a durum wheat breeding program

Mohammadi, R., & Amri, A. (2022) · Euphytica · doi:10.1007/s10681-022-03024-w · Cited by 14 · see citing works

Inversion of Crop Water Content Using Multispectral Data and Machine Learning Algorithms in the North China Plain

Zhang, Z., Dou, G., Zhao, X., Gao, Y., Liu, S., & Qin, A. (2024) · Agronomy · doi:10.3390/agronomy14102361 · Cited by 13 · see citing works

Early Yield Prediction of Oilseed Rape Using UAV-Based Hyperspectral Imaging Combined with Machine Learning Algorithms

Zhu, H., Lin, C., Dong, Z., Xu, J., & He, Y. (2025) · Agriculture · doi:10.3390/agriculture15101100 · Cited by 12 · see citing works

The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle

Júnior, M. R. B., Tedesco, D., Carreira, V. D. S., Pinto, A. A., Moreira, B. R. D. A., Shiratsuchi, L. S., Zerbato, C., & da Silva, R. P. (2022) · Drones · doi:10.3390/drones6050112 · Cited by 12 · see citing works

Can We Select Sugarbeet Harvesting Dates Using Drone‐based Vegetation Indices?

Olson, D., Chatterjee, A., & Franzen, D. W. (2019) · Agronomy Journal · doi:10.2134/agronj2019.03.0219 · Cited by 12 · see citing works

Comparisons of sensors to predict spring wheat grain yield and protein content

Veverka, D., Chatterjee, A., & Carlson, M. (2021) · Agronomy Journal · doi:10.1002/agj2.20621 · Cited by 12 · see citing works

Sensor-Based Yield Prediction in Durum Wheat Under Semi-Arid Conditions Using Machine Learning Across Zadoks Growth Stages

Rufaioğlu, S. B., Bilgili, A. V., Savaşlı, E., Özberk, İ., Aydemir, S., Ismael, A. M., Kaya, Y., & Matos-Carvalho, J. P. (2025) · Remote Sensing · doi:10.3390/rs17142416 · Cited by 11 · see citing works

Remote Sensing Monitoring of Durum Wheat under No Tillage Practices by Means of Spectral Indices Interpretation: A Preliminary Study

Calcagno, F., Romano, E., Furnitto, N., Jamali, A., & Failla, S. I. G. (2022) · Sustainability · doi:10.3390/su142215012 · Cited by 11 · see citing works

Insights on multi-spectral vegetation indices derived from UAV-based high-throughput phenotyping for indirect selection in tropical wheat breeding

Silva, C. M. E., Mezzomo, H. C., Ribeiro, J. P. O., Signorini, V. S., Lima, G. W., Vieira, E. F. T., Portes, M. F., Morota, G., Corrêdo, L. D. P., & Nardino, M. (2024) · Euphytica · doi:10.1007/s10681-024-03299-1 · Cited by 10 · see citing works

Phenotyping for Effects of Drought Levels in Quinoa Using Remote Sensing Tools

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Unraveling the Secrets of Early-Maturity and Short-Duration Bread Wheat in Unpredictable Environments

Singh, C., Yadav, S., Khare, V., Gupta, V., Kamble, U., Gupta, O. P., Kumar, R., Saini, P. K., Bairwa, R. K., Khobra, R., et al. (2024) · Plants · doi:10.3390/plants13202855 · Cited by 10 · see citing works

Unmanned Aerial Vehicle (UAV) Imagery for Plant Communities: Optimizing Visible Light Vegetation Index to Extract Multi-Species Coverage

Wang, M., Zhang, Z., Gao, R., Zhang, J., & Feng, W. (2025) · Plants · doi:10.3390/plants14111677 · Cited by 10 · see citing works

Durum Wheat (Triticum durum Desf.) Grain Yield and Protein Estimation by Multispectral UAV Monitoring and Machine Learning Under Mediterranean Conditions

Badagliacca, G., Messina, G., Presti, E. L., Preiti, G., Fazio, S. D., Monti, M., Modica, G., & Praticò, S. (2025) · AgriEngineering · doi:10.3390/agriengineering7040099 · Cited by 9 · see citing works

Towards an Effective Risk Management in Durum Wheat Production: A Systematic Review and Bibliometric Analysis of Factors Influencing Quality and Yield

Chab, L., Biagini, L., & Severini, S. (2024) · Agriculture · doi:10.3390/agriculture14122266 · Cited by 8 · see citing works

Preharvest Durum Wheat Yield, Protein Content, and Protein Yield Estimation Using Unmanned Aerial Vehicle Imagery and Pléiades Satellite Data in Field Breeding Experiments

Ganeva, D., Roumenina, E., Dimitrov, P., Gikov, A., Bozhanova, V., Dragov, R., Jelev, G., & Taneva, K. (2024) · Remote Sensing · doi:10.3390/rs16030559 · Cited by 8 · see citing works

Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV

Chávez-Martínez, O., Monjardín-Armenta, S. A., Rangel‐Peraza, J. G., Sanhouse-García, A. J., Mora-Félix, Z. D., & Plata-Rocha, W. (2024) · Open Agriculture · doi:10.1515/opag-2022-0357 · Cited by 8 · see citing works

Optimal scale extraction of farmland in coal mining areas with high groundwater levels based on visible light images from an unmanned aerial vehicle (UAV)

Hu, X. F., Li, X., Min, X., & Niu, B. (2020) · Earth Science Informatics · doi:10.1007/s12145-020-00493-2 · Cited by 8 · see citing works

Does Leaf Waxiness Confound the Use of NDVI in the Assessment of Chlorophyll When Evaluating Genetic Diversity Panels of Wheat?

Khadka, K., Burt, A. J., Earl, H. J., Raizada, M. N., & Navabi, A. (2021) · Agronomy · doi:10.3390/agronomy11030486 · Cited by 7 · see citing works

Identifying genomic regions determining shoot and root traits related to nitrogen uptake efficiency in a multiparent advanced generation intercross (MAGIC) winter wheat population in a high-throughput phenotyping facility

Schmidt, L., Jacobs, J. J. M. R., Schmutzer, T., Alqudah, A. M., Sannemann, W., Pillen, K., & Maurer, A. (2023) · Plant Science · doi:10.1016/j.plantsci.2023.111656 · Cited by 7 · see citing works

Development of machine learning models for estimating wheat biophysical variables using satellite-based vegetation indices

Jamali, M., Bakhshandeh, E., Yeganeh, B., & Mutlu Ozdogan, A. (2023) · Advances in Space Research · doi:10.1016/j.asr.2023.10.004 · Cited by 7 · see citing works

High-Throughput Phenotyping for Agronomic Traits in Cassava Using Aerial Imaging

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A Survey on Unauthorized UAV Threats to Smart Farming

Chen, P., Yan, S., Janicke, H., Mahboubi, A., Bui, H. T., Aboutorab, H., Bewong, M., & Islam, R. (2025) · Drones · doi:10.3390/drones9040251 · Cited by 6 · see citing works

UAS‐derived vegetation indices detect wheat leaf rust infection and its influence on grain yield and canopy temperature

Raman, R., Neely, H. L., Rajan, N., Bhandari, M., Siegfried, J., Ibrahim, A. M. H., Adams, C. B., & Hardin, R. G. (2025) · Crop Science · doi:10.1002/csc2.70062 · Cited by 6 · see citing works

Characterization of vegetative vigor of two doubled-haploid wheat populations

Khumalo, T. P., Barnard, A., Dube, E., & Tsilo, T. J. (2021) · Journal of Crop Improvement · doi:10.1080/15427528.2021.1970675 · Cited by 6 · see citing works

Assessing the Impact of Soil Contamination on Maize Plant Development using UAV-Based Multispectral Indices

Gargiulo, M., Savarese, C., Tufano, F., Parrilli, S., Verrillo, M., Cozzolino, V., Piccolo, A., & Mizio, M. D. (2023) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas57906.2023.10155981 · Cited by 5 · see citing works

AERONAVE REMOTAMENTE PILOTADA DE BAIXO CUSTO NO ESTUDO DE PLANTAS INVASORAS EM ÁREAS DE CERRADO

Pessi, D. D., José, J. V., Mioto, C. L., & da Silva, N. M. (2020) · Nativa · doi:10.31413/nativa.v8i1.8433 · Cited by 5 · see citing works

Individualized Indicators and Estimation Methods for Tiger Nut (Cyperus esculentus L.) Tubers Yield Using Light Multispectral UAV and Lightweight CNN Structure

Li, D., & Wu, X. (2023) · Drones · doi:10.3390/drones7070432 · Cited by 5 · see citing works

Two-year remote sensing and ground verification: Estimating chlorophyll content in winter wheat using UAV multi-spectral imagery

Ai, W., Yang, G., Li, Z., Du, J., Ye, L., Feng, X., Jin, X., & He, Y. (2025) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2025.10.017 · Cited by 4 · see citing works

Comparative assessment of einkorn and emmer wheat phenomes: III. Phenology

Jaradat, A. A. (2019) · Genetic Resources and Crop Evolution · doi:10.1007/s10722-019-00816-3 · Cited by 4 · see citing works

Abiotic Stresses

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Establishing a knowledge structure for yield prediction in cereal crops using unmanned aerial vehicles

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Dynamics of Land and Water Resources and Utilization of Cultivated Land in the Yellow River Beach Area of China

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The Usefulness of Drone Imagery and Remote Sensing Methods for Monitoring Turfgrass Irrigation

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A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform

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Predicting grain yield in wheat using UAV multispectral and ground based vegetation indices

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Investigating Correlation among NDVI Index Derived by Unmanned Aerial Vehicle Photography and Grain Yield under Late Drought Stress Conditions

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UAV ‐Based High‐Throughput Phenotyping for Early Canopy Vigor Quantification in Wheat

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