Durum Wheat (Triticum durum Desf.) Grain Yield and Protein Estimation by Multispectral UAV Monitoring and Machine Learning Under Mediterranean Conditions
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Site
2.2. Experimental Design and Crop Management
2.3. UAV Surveys and Image Processing
2.4. Statistical and Machine Learning (ML) Approaches
3. Results and Discussion
3.1. Plant Height and Chlorophyll Concentration Index
3.2. Grain Yield and Protein Concentration
3.3. Pearson’s Correlation Analysis
3.4. Machine Learning (ML)
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Blanco, A. Structure and trends of worldwide research on durum wheat by bibliographic mapping. Int. J. Plant Biol. 2024, 15, 132–160. [Google Scholar] [CrossRef]
- Carbone, A.; Henke, R. Recent trends in agri-food Made in Italy exports. Agric. Food Econ. 2023, 11, 32. [Google Scholar]
- Grosse-Heilmann, M.; Cristiano, E.; Deidda, R.; Viola, F. Durum wheat productivity today and tomorrow: A review of influencing factors and climate change effects. Resour. Environ. Sustain. 2024, 17, 100170. [Google Scholar]
- FAO. Land & Water: Wheat. Available online: https://www.fao.org/land-water/databases-and-software/crop-information/wheat/en/ (accessed on 12 January 2024).
- Ciliberti, S.; Stanco, M.; Frascarelli, A.; Marotta, G.; Martino, G.; Nazzaro, C. Sustainability strategies and contractual arrangements in the italian pasta supply chain: An analysis under the neo institutional economics lens. Sustainability 2022, 14, 8542. [Google Scholar] [CrossRef]
- Raun, W.R.; Johnson, G.V. Improving nitrogen use efficiency for cereal production. Agron. J. 1999, 91, 357–363. [Google Scholar]
- Lupini, A.; Preiti, G.; Badagliacca, G.; Abenavoli, M.R.; Sunseri, F.; Monti, M.; Bacchi, M. Nitrogen use efficiency in durum wheat under different nitrogen and water regimes in the mediterranean basin. Front. Plant Sci. 2021, 11, 607226. [Google Scholar]
- Kubar, M.S.; Alshallash, K.S.; Asghar, M.A.; Feng, M.; Raza, A.; Wang, C.; Saleem, K.; Ullah, A.; Yang, W.; Kubar, K.A.; et al. Improving winter wheat photosynthesis, nitrogen use efficiency, and yield by optimizing nitrogen fertilization. Life 2022, 12, 1478. [Google Scholar] [CrossRef]
- European Commission. Ensuring Availability and Affordability of Fertilisers. Available online: https://agriculture.ec.europa.eu/common-agricultural-policy/agri-food-supply-chain/ensuring-availability-and-affordability-fertilisers_en#documents (accessed on 21 February 2024).
- Mahmud, K.; Panday, D.; Mergoum, A.; Missaoui, A. Nitrogen losses and potential mitigation strategies for a sustainable agroecosystem. Sustainability 2021, 13, 2400. [Google Scholar] [CrossRef]
- Durán-Sandoval, D.; Uleri, F.; Durán-Romero, G.; López, A.M. Food, climate change, and the challenge of innovation. Encyclopedia 2023, 3, 839–852. [Google Scholar] [CrossRef]
- Toselli, M.; Baldi, E.; Ferro, F.; Rossi, S.; Cillis, D. Smart farming tool for monitoring nutrients in soil and plants for precise fertilization. Horticulturae 2023, 9, 1011. [Google Scholar] [CrossRef]
- Santaga, F.S.; Agnelli, A.; Leccese, A.; Vizzari, M. Using Sentinel-2 for simplifying soil sampling and mapping: Two case studies in Umbria, Italy. Remote Sens. 2021, 13, 3379. [Google Scholar] [CrossRef]
- Raeva, P.L.; Šedina, J.; Dlesk, A. Monitoring of crop fields using multispectral and thermal imagery from UAV. Eur. J. Remote Sens. 2019, 52, 192–201. [Google Scholar]
- Modica, G.; Messina, G.; De Luca, G.; Fiozzo, V.; Praticò, S. Monitoring the vegetation vigor in heterogeneous citrus and olive orchards. A multiscale object-based approach to extract trees’ crowns from UAV multispectral imagery. Comput. Electron. Agric. 2020, 175, 105500. [Google Scholar]
- Messina, G.; Praticò, S.; Badagliacca, G.; Di Fazio, S.; Monti, M.; Modica, G. Monitoring onion crop “Cipolla rossa di Tropea Calabria IGP” growth and yield response to varying nitrogen fertilizer application rates using UAV imagery. Drones 2021, 5, 61. [Google Scholar] [CrossRef]
- Marti, J.; Bort, J.; Slafer, G.A.; Araus, J.L. Can wheat yield be assessed by early measurements of Normalized Difference Vegetation Index? Ann. Appl. Biol. 2007, 150, 253–257. [Google Scholar]
- Sharma, P.; Leigh, L.; Chang, J.; Maimaitijiang, M.; Caffé, M. Above-ground biomass estimation in oats using UAV remote sensing and machine learning. Sensors 2022, 22, 601. [Google Scholar] [CrossRef]
- Tatsumi, K.; Igarashi, N.; Mengxue, X. Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery. Plant Methods 2021, 17, 77. [Google Scholar]
- Messina, G.; Badagliacca, G.; Praticò, S.; Preiti, G.; Monti, M.; Modica, G. Multispectral UAV-based monitoring of behavior of different wheat and barley varieties. In Biosystems Engineering Towards the Green Deal. AIIA 2022. Lecture Notes in Civil Engineering; Ferro, V., Giordano, G., Orlando, S., Vallone, M., Cascone, G., Porto, S.M.C., Eds.; Springer: Amsterdam, The Netherlands, 2023; pp. 1173–1181. [Google Scholar]
- Kyratzis, A.C.; Skarlatos, D.P.; Menexes, G.C.; Vamvakousis, V.F.; Katsiotis, A. Assessment of vegetation indices derived by UAV imagery for durum wheat phenotyping under a water limited and heat stressed Mediterranean environment. Front. Plant Sci. 2017, 8, 1–14. [Google Scholar]
- Fiorentini, M.; Schillaci, C.; Denora, M.; Zenobi, S.; Deligios, P.; Orsini, R.; Santilocchi, R.; Perniola, M.; Montanarella, L.; Ledda, L. A machine learning modeling framework for Triticum turgidum subsp. durum Desf. yield forecasting in Italy. Agron. J. 2022, 116, 1050–1070. [Google Scholar]
- Modica, G.; De Luca, G.; Messina, G.; Praticò, S. Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery: A case study in a citrus orchard and an onion crop. Eur. J. Remote Sens. 2021, 54, 431–460. [Google Scholar]
- Li, J.; He, Z.; Zhou, G.; Yan, S.; Zhang, J. DeepAT: A deep learning wheat phenotype prediction model based on genotype data. Agronomy 2024, 14, 2756. [Google Scholar] [CrossRef]
- Peng, D.; Cheng, E.; Feng, X.; Hu, J.; Lou, Z.; Zhang, H.; Zhao, B.; Lv, Y.; Peng, H.; Zhang, B. A deep–learning network for wheat yield prediction combining weather forecasts and remote sensing data. Remote Sens. 2024, 16, 3613. [Google Scholar] [CrossRef]
- Iqbal, N.; Shahzad, M.U.; Sherif, E.-S.M.; Tariq, M.U.; Rashid, J.; Le, T.-V.; Ghani, A. Analysis of wheat-yield prediction using machine learning models under climate change scenarios. Sustainability 2024, 16, 6976. [Google Scholar] [CrossRef]
- Wang, C.; Song, X.; Pan, W.; Yu, H.; Li, X.; Liu, P. Determination of wheat growth stages using image sequences and deep learning. Agronomy 2024, 15, 13. [Google Scholar] [CrossRef]
- Cheng, E.; Zhang, B.; Peng, D.; Zhong, L.; Yu, L.; Liu, Y.; Xiao, C.; Li, C.; Li, X.; Chen, Y.; et al. Wheat yield estimation using remote sensing data based on machine learning approaches. Front. Plant Sci. 2022, 13, 1090970. [Google Scholar]
- Pantazi, X.E.; Moshou, D.; Alexandridis, T.; Whetton, R.L.; Mouazen, A.M. Wheat yield prediction using machine learning and advanced sensing techniques. Comput. Electron. Agric. 2016, 121, 57–65. [Google Scholar]
- Escalante, H.J.; Rodríguez-Sánchez, S.; Jiménez-Lizárraga, M.; Morales-Reyes, A.; De La Calleja, J.; Vazquez, R. Barley yield and fertilization analysis from UAV imagery: A deep learning approach. Int. J. Remote Sens. 2019, 40, 2493–2516. [Google Scholar]
- Petersen, C.T.; Langgaard, M.K.; Petersen, S.D. Yield prediction in spring barley from spectral reflectance and weather data using machine learning. Soil Use Manag. 2023, 39, 975–987. [Google Scholar]
- Zhang, P.; Lu, B.; Shang, J.; Wang, X.; Hou, Z.; Jin, S.; Yang, Y.; Zang, H.; Ge, J.; Zeng, Z. Ensemble learning for oat yield prediction using multi-growth stage UAV images. Remote Sens. 2024, 16, 4575. [Google Scholar] [CrossRef]
- Ashapure, A.; Oh, S.; Marconi, T.G.; Chang, A.; Jung, J.; Landivar, J.; Enciso, J. Unmanned aerial system based tomato yield estimation using machine learning. In Proceedings of the Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV; Thomasson, J.A., McKee, M., Moorhead, R.J., Eds.; SPIE: Baltimore, MD, USA, 2019; Volume 11008. [Google Scholar]
- El-Kenawy, E.-S.M.; Alhussan, A.A.; Khodadadi, N.; Mirjalili, S.; Eid, M.M. Predicting potato crop yield with machine learning and deep learning for sustainable agriculture. Potato Res. 2024. [Google Scholar] [CrossRef]
- Kuradusenge, M.; Hitimana, E.; Hanyurwimfura, D.; Rukundo, P.; Mtonga, K.; Mukasine, A.; Uwitonze, C.; Ngabonziza, J.; Uwamahoro, A. Crop yield prediction using machine learning models: Case of irish potato and maize. Agriculture 2023, 13, 225. [Google Scholar] [CrossRef]
- Marino, S.; Alvino, A. Agronomic traits analysis of ten winter wheat cultivars clustered by UAV-derived vegetation indices. Remote Sens. 2020, 12, 249. [Google Scholar] [CrossRef]
- Marino, S.; Alvino, A. Detection of homogeneous wheat areas using multi-temporal UAS images and ground truth data analyzed by cluster analysis. Eur. J. Remote Sens. 2018, 51, 266–275. [Google Scholar]
- Badagliacca, G.; Messina, G.; Praticò, S.; Lo Presti, E.; Preiti, G.; Monti, M.; Modica, G. Multispectral vegetation indices and machine learning approaches for durum wheat (Triticum durum Desf.) yield prediction across different varieties. AgriEngineering 2023, 5, 2032–2048. [Google Scholar] [CrossRef]
- Mancini, A.; Solfanelli, F.; Coviello, L.; Martini, F.M.; Mandolesi, S.; Zanoli, R. Time series from Sentinel-2 for organic durum wheat yield prediction using functional data analysis and deep learning. Agronomy 2024, 14, 109. [Google Scholar] [CrossRef]
- Vatter, T.; Gracia-Romero, A.; Kefauver, S.C.; Nieto-Taladriz, M.T.; Aparicio, N.; Araus, J.L. Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging. Plant J. 2022, 109, 1507–1518. [Google Scholar]
- Romano, E.; Bergonzoli, S.; Pecorella, I.; Bisaglia, C.; De Vita, P. Methodology for the definition of durum wheat yield homogeneous zones by using satellite spectral indices. Remote Sens. 2021, 13, 2036. [Google Scholar] [CrossRef]
- Smith, D.W. Soil Survey Staff Keys to soil taxonomy. Soil Conserv. Serv. 2014, 12, 410. [Google Scholar]
- International, A. Official Methods of Analysis, 15th ed.; AOAC International: Arlington, VA, USA, 2016. [Google Scholar]
- Fujihara, S.; Sasaki, H.; Aoyagi, Y.; Sugahara, T. Nitrogen-to-protein conversion factors for some cereal products in Japan. J. Food Sci. 2008, 73, 204–209. [Google Scholar]
- Vincini, M.; Frazzi, E. Comparing narrow and broad-band vegetation indices to estimate leaf chlorophyll content in planophile crop canopies. Precis. Agric. 2011, 12, 334–344. [Google Scholar]
- Cao, X.; Liu, Y.; Yu, R.; Han, D.; Su, B. A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population. Remote Sens. 2021, 13, 5173. [Google Scholar] [CrossRef]
- Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289–298. [Google Scholar] [CrossRef]
- Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004, 90, 337–352. [Google Scholar]
- Barnes, E.M.; Clarke, T.R.; Richards, S.E.; Colaizzi, P.D.; Haberland, J.; Kostrzewski, M.; Waller, P.; Choi, R.E.C.; Thompson, T.; Lascano, R.J.; et al. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, MN, USA, 16 July 2000; CABI: Bloomington, MN, USA, 2000; Volume 1619, pp. 1–15. [Google Scholar]
- Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ 1974, 351, 309. [Google Scholar]
- Yue, J.; Yang, G.; Tian, Q.; Feng, H.; Xu, K.; Zhou, C. Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices. ISPRS J. Photogramm. Remote Sens. 2019, 150, 226–244. [Google Scholar]
- Roujean, J.-L.; Breon, F.-M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 1995, 51, 375–384. [Google Scholar]
- Chen, P.-F.; Nicolas, T.; Wang, J.-H.; Philippe, V.; Huang, W.-J.; Li, B.-G. New index for crop canopy fresh biomass estimation. Spectrosc. Spectr. Anal. 2010, 30, 512–517. [Google Scholar]
- Jordan, C.F. Derivation of Leaf-Area Index from Quality of Light on the Forest Floor. Ecology 1969, 50, 663–666. [Google Scholar] [CrossRef]
- Vogelmann, J.E.; Rock, B.N.; Moss, D.M. Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sens. 1993, 14, 1563–1575. [Google Scholar]
- R CoreTeam. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021. [Google Scholar]
- Correlation Coefficient and Correlation Test in R. Available online: https://statsandr.com/blog/correlation-coefficient-and-correlation-test-in-r/ (accessed on 15 February 2024).
- Kuhn, M.; Wing, J.; Weston, S.; Williams, A. The caret package. J. Stat. Softw. 2008, 28, 1–26. [Google Scholar]
- Araújo, S.O.; Peres, R.S.; Ramalho, J.C.; Lidon, F.; Barata, J. machine learning applications in agriculture: Current trends, challenges, and future perspectives. Agronomy 2023, 13, 2976. [Google Scholar] [CrossRef]
- Meshram, V.; Patil, K.; Meshram, V.; Hanchate, D.; Ramkteke, S.D. Machine learning in agriculture domain: A state-of-art survey. Artif. Intell. Life Sci. 2021, 1, 100010. [Google Scholar] [CrossRef]
- Tao, H.; Feng, H.; Xu, L.; Miao, M.; Yang, G.; Yang, X.; Fan, L. Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images. Sensors 2020, 20, 1231. [Google Scholar] [CrossRef] [PubMed]
- Abenavoli, L.; Milanovic, M.; Procopio, A.C.; Spampinato, G.; Maruca, G.; Perrino, E.V.; Mannino, G.C.; Fagoonee, S.; Luzza, F.; Musarella, C.M. Ancient wheats: Beneficial effects on insulin resistance. Minerva Med. 2021, 112, 641–650. [Google Scholar] [CrossRef]
- Zhang, M.; Wang, H.; Yi, Y.; Ding, J.; Zhu, M.; Li, C.; Guo, W.; Feng, C.; Zhu, X. Effect of nitrogen levels and nitrogen ratios on lodging resistance and yield potential of winter wheat (Triticum aestivum L.). PLoS ONE 2017, 12, e0187543. [Google Scholar] [CrossRef]
- Li, W.; Han, M.; Pang, D.; Chen, J.; Wang, Y.; Dong, H.; Chang, Y.; Jin, M.; Luo, Y.; Li, Y.; et al. Characteristics of lodging resistance of high-yield winter wheat as affected by nitrogen rate and irrigation managements. J. Integr. Agric. 2022, 21, 1290–1309. [Google Scholar] [CrossRef]
- Ravier, C.; Quemada, M.; Jeuffroy, M.-H. Use of a chlorophyll meter to assess nitrogen nutrition index during the growth cycle in winter wheat. Field Crops Res. 2017, 214, 73–82. [Google Scholar] [CrossRef]
- Careddu, M.L.; Giunta, F.; Motzo, R. Lessons from the varietal evolution of durum wheat in italy. Agronomy 2023, 14, 87. [Google Scholar] [CrossRef]
- De Santis, M.A.; Giuliani, M.M.; Giuzio, L.; De Vita, P.; Flagella, Z. Assessment of grain protein composition in old and modern Italian durum wheat genotypes. Ital. J. Agron. 2018, 13, 40–43. [Google Scholar] [CrossRef]
- Giambalvo, D.; Ruisi, P.; Di Miceli, G.; Frenda, A.S.; Amato, G. Nitrogen use efficiency and nitrogen fertilizer recovery of durum wheat genotypes as affected by interspecific competition. Agron. J. 2010, 102, 707. [Google Scholar] [CrossRef]
- Fu, Z.; Zhang, J.; Jiang, J.; Zhang, Z.; Cao, Q.; Tian, Y.; Zhu, Y.; Cao, W.; Liu, X. Using the time series nitrogen diagnosis curve for precise nitrogen management in wheat and rice. Field Crops Res. 2024, 307, 109259. [Google Scholar]
- Ingraffia, R.; Lo Porto, A.; Ruisi, P.; Amato, G.; Giambalvo, D.; Frenda, A.S. Conventional tillage versus no-tillage: Nitrogen use efficiency component analysis of contrasting durum wheat genotypes grown in a Mediterranean environment. Field Crops Res. 2023, 296, 108904. [Google Scholar]
- Hassan, M.A.; Yang, M.; Rasheed, A.; Yang, G.; Reynolds, M.; Xia, X.; Xiao, Y.; He, Z. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Sci. 2019, 282, 95–103. [Google Scholar] [PubMed]
- Duan, T.; Chapman, S.C.; Guo, Y.; Zheng, B. Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle. Field Crops Res. 2017, 210, 71–80. [Google Scholar] [CrossRef]
- Babar, M.A.; Reynolds, M.P.; van Ginkel, M.; Klatt, A.R.; Raun, W.R.; Stone, M.L. Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Sci. 2006, 46, 578–588. [Google Scholar]
- Gutierrez, M.; Reynolds, M.P.; Raun, W.R.; Stone, M.L.; Klatt, A.R. Spectral water indices for assessing yield in elite bread wheat genotypes under well-irrigated, water-stressed, and high-temperature conditions. Crop Sci. 2010, 50, 197–214. [Google Scholar]
- Xue, J.; Su, B. Significant remote sensing vegetation indices: A review of developments and applications. J. Sens. 2017, 2017, 1353691. [Google Scholar]
- Latif, M.A.; Cheema, M.J.; Saleem, M.F.; Maqsood, M. mapping wheat response to variations in N, P, Zn, and irrigation using an unmanned aerial vehicle. Int. J. Remote Sens. 2018, 39, 7172–7188. [Google Scholar]
- Yang, M.; Hassan, M.A.; Xu, K.; Zheng, C.; Rasheed, A.; Zhang, Y.; Jin, X.; Xia, X.; Xiao, Y.; He, Z. Assessment of water and nitrogen use efficiencies through UAV-based multispectral phenotyping in winter wheat. Front. Plant Sci. 2020, 11, 927. [Google Scholar]
- Potgieter, A.B.; George-Jaeggli, B.; Chapman, S.C.; Laws, K.; Suárez Cadavid, L.A.; Wixted, J.; Watson, J.; Eldridge, M.; Jordan, D.R.; Hammer, G.L. Multi-spectral imaging from an unmanned aerial vehicle enables the assessment of seasonal leaf area dynamics of sorghum breeding lines. Front. Plant Sci. 2017, 8, 1532. [Google Scholar]
- Volpato, L.; Pinto, F.; González-Pérez, L.; Thompson, I.G.; Borém, A.; Reynolds, M.; Gérard, B.; Molero, G.; Rodrigues, F.A. High throughput field phenotyping for plant height using UAV-based rgb imagery in wheat breeding lines: Feasibility and validation. Front. Plant Sci. 2021, 12, 591587. [Google Scholar] [CrossRef] [PubMed]
- Hatfield, J.L.; Gitelson, A.A.; Schepers, J.S.; Walthall, C.L. Application of Spectral Remote Sensing for Agronomic Decisions. Agron. J. 2008, 100, 117. [Google Scholar] [CrossRef]
- Li, J.; Shi, Y.; Veeranampalayam-Sivakumar, A.-N.; Schachtman, D.P. Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system. Front. Plant Sci. 2018, 9, 1406. [Google Scholar] [CrossRef]
- Fu, Z.; Jiang, J.; Gao, Y.; Krienke, B.; Wang, M.; Zhong, K.; Cao, Q.; Tian, Y.; Zhu, Y.; Cao, W.; et al. Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sens. 2020, 12, 508. [Google Scholar] [CrossRef]
- Gordillo-Salinas, V.M.; Flores-Magdaleno, H.; Ortiz-Solorio, C.A.; Arteaga-Ramírez, R. Evaluation of nitrogen status in a wheat crop using unmanned aerial vehicle images. Chil. J. Agric. Res. 2021, 81, 408–419. [Google Scholar] [CrossRef]
- Piikki, K.; Söderström, M.; Stadig, H.; Wolters, S. Remote sensing and on-farm experiments for determining in-season nitrogen rates in winter wheat—Options for implementation, model accuracy and remaining challenges. Field Crops Res. 2022, 289, 108742. [Google Scholar] [CrossRef]
- Jiang, J.; Atkinson, P.M.; Zhang, J.; Lu, R.; Zhou, Y.; Cao, Q.; Tian, Y.; Zhu, Y.; Cao, W.; Liu, X. Combining fixed-wing UAV multispectral imagery and machine learning to diagnose winter wheat nitrogen status at the farm scale. Eur. J. Agron. 2022, 138, 126537. [Google Scholar] [CrossRef]
- Chen, Z.; Miao, Y.; Lu, J.; Zhou, L.; Li, Y.; Zhang, H.; Lou, W.; Zhang, Z.; Kusnierek, K.; Liu, C. In-season diagnosis of winter wheat nitrogen status in smallholder farmer fields across a village using unmanned aerial vehicle-based remote sensing. Agronomy 2019, 9, 619. [Google Scholar] [CrossRef]
- Zheng, H.; Li, W.; Jiang, J.; Liu, Y.; Cheng, T.; Tian, Y.; Zhu, Y.; Cao, W.; Zhang, Y.; Yao, X. A comparative assessment of different modeling algorithms for estimating leaf nitrogen content in winter wheat using multispectral images from an unmanned aerial vehicle. Remote Sens. 2018, 10, 2026. [Google Scholar] [CrossRef]
- Argento, F.; Anken, T.; Abt, F.; Vogelsanger, E.; Walter, A.; Liebisch, F. Site-specific nitrogen management in winter wheat supported by low-altitude remote sensing and soil data. Precis. Agric. 2021, 22, 364–386. [Google Scholar] [CrossRef]
- Benincasa, P.; Antognelli, S.; Brunetti, L.; Fabbri, C.A.; Natale, A.; Sartoretti, V.; Modeo, G.; Guiducci, M.; Tei, F.; Vizzari, M. Reliability of ndvi derived by high resolution satellite and uav compared to in-field methods for the evaluation of early crop n status and grain yield in wheat. Exp. Agric. 2018, 54, 604–622. [Google Scholar] [CrossRef]
- Kefauver, S.C.; Vicente, R.; Vergara-Díaz, O.; Fernandez-Gallego, J.A.; Kerfal, S.; Lopez, A.; Melichar, J.P.E.; Serret Molins, M.D.; Araus, J.L. Comparative UAV and field phenotyping to assess yield and nitrogen use efficiency in hybrid and conventional barley. Front. Plant Sci. 2017, 8, 1733. [Google Scholar]
- Zhou, X.; Kono, Y.; Win, A.; Matsui, T.; Tanaka, T.S.T.T. Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches. Plant Prod. Sci. 2021, 24, 137–151. [Google Scholar]
- Yue, J.; Yang, G.; Li, C.; Li, Z.; Wang, Y.; Feng, H.; Xu, B. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sens. 2017, 9, 708. [Google Scholar] [CrossRef]
- Yousfi, S.; Kellas, N.; Saidi, L.; Benlakehal, Z.; Chaou, L.; Siad, D.; Herda, F.; Karrou, M.; Vergara, O.; Gracia, A.; et al. Comparative performance of remote sensing methods in assessing wheat performance under Mediterranean conditions. Agric. Water Manag. 2016, 164, 137–147. [Google Scholar]
- Filippi, P.; Whelan, B.M.; Vervoort, R.W.; Bishop, T.F.A. Mid-season empirical cotton yield forecasts at fine resolutions using large yield mapping datasets and diverse spatial covariates. Agric. Syst. 2020, 184, 102894. [Google Scholar]
- Shendryk, Y.; Davy, R.; Thorburn, P. Integrating satellite imagery and environmental data to predict field-level cane and sugar yields in Australia using machine learning. Field Crops Res. 2021, 260, 107984. [Google Scholar]
- Richetti, J.; Judge, J.; Boote, K.J.; Johann, J.A.; Uribe-Opazo, M.A.; Becker, W.R.; Paludo, A.; Silva, L.C. de A. Using phenology-based enhanced vegetation index and machine learning for soybean yield estimation in Paraná State, Brazil. J. Appl. Remote Sens. 2018, 12, 026029. [Google Scholar] [CrossRef]
- Leo, S.; De Antoni Migliorati, M.; Grace, P.R. Predicting within-field cotton yields using publicly available datasets and machine learning. Agron. J. 2021, 113, 1150–1163. [Google Scholar] [CrossRef]
- Gómez, D.; Salvador, P.; Sanz, J.; Casanova, J.L. Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico. Agric. For. Meteorol. 2021, 300, 108317. [Google Scholar] [CrossRef]
- Wang, L.; Zhou, X.; Zhu, X.; Dong, Z.; Guo, W. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. Crop J. 2016, 4, 212–219. [Google Scholar]
- Yuan, H.; Yang, G.; Li, C.; Wang, Y.; Liu, J.; Yu, H.; Feng, H.; Xu, B.; Zhao, X.; Yang, X. Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models. Remote Sens. 2017, 9, 309. [Google Scholar] [CrossRef]
- Bebie, M.; Cavalaris, C.; Kyparissis, A. Assessing durum wheat yield through Sentinel-2 Imagery: A machine learning approach. Remote Sens. 2022, 14, 3880. [Google Scholar] [CrossRef]
- Yue, J.; Feng, H.; Yang, G.; Li, Z. A Comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sens. 2018, 10, 66. [Google Scholar] [CrossRef]
- Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. Random forests for classification in ecology. Ecology 2007, 88, 2783–2792. [Google Scholar]
- Verrelst, J.; Muñoz, J.; Alonso, L.; Delegido, J.; Rivera, J.P.; Camps-Valls, G.; Moreno, J. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3. Remote Sens. Environ. 2012, 118, 127–139. [Google Scholar]
- Liu, H.; Zhang, S. Noisy data elimination using mutual k-nearest neighbor for classification mining. J. Syst. Softw. 2012, 85, 1067–1074. [Google Scholar]
- Filippi, P.; Jones, E.J.; Wimalathunge, N.S.; Somarathna, P.D.S.N.; Pozza, L.E.; Ugbaje, S.U.; Jephcott, T.G.; Paterson, S.E.; Whelan, B.M.; Bishop, T.F.A. An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precis. Agric. 2019, 20, 1015–1029. [Google Scholar]
- Fu, Z.; Yu, S.; Zhang, J.; Xi, H.; Gao, Y.; Lu, R.; Zheng, H.; Zhu, Y.; Cao, W.; Liu, X. Combining UAV multispectral imagery and ecological factors to estimate leaf nitrogen and grain protein content of wheat. Eur. J. Agron. 2022, 132, 126405. [Google Scholar]
- Ding, F.; Li, C.; Zhai, W.; Fei, S.; Cheng, Q.; Chen, Z. Estimation of nitrogen content in winter wheat based on multi-source data fusion and machine learning. Agriculture 2022, 12, 1752. [Google Scholar] [CrossRef]
- Chen, X.; Li, F.; Shi, B.; Chang, Q. Estimation of winter wheat plant nitrogen concentration from uav hyperspectral remote sensing combined with machine learning methods. Remote Sens. 2023, 15, 2831. [Google Scholar] [CrossRef]
- Li, Z.; Zhou, X.; Cheng, Q.; Fei, S.; Chen, Z. A machine-learning model based on the fusion of spectral and textural features from uav multi-sensors to analyse the total nitrogen content in winter wheat. Remote Sens. 2023, 15, 2152. [Google Scholar] [CrossRef]
- Kanning, M.; Kühling, I.; Trautz, D.; Jarmer, T. High-resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sens. 2018, 10, 2000. [Google Scholar] [CrossRef]
Vegetation Index | Acronym | Formula | Ref. |
---|---|---|---|
Chlorophyll Vegetation Index | CVI | [45] | |
Green Normalized Difference Red Edge | GNDRE | [46] | |
Green Normalized Difference Vegetation | GNDVI | [47] | |
Modified Chlorophyll Absorption Ratio Index | MCARI2 | [48] | |
Modified Triangular Vegetation Index | MTVI | ||
Modified Triangular Vegetation Index 2 | MTVI2 | ||
Normalized Difference Red Edge | NDRE | [49] | |
Normalized Difference Vegetation Index | NDVI | [50] | |
Optimized Soil Adjusted Vegetation Index | OSAVI | [51] | |
Renormalized Difference Vegetation Index | RDVI | [52] | |
Red Edge Triangulated Vegetation Index | RTVI | [53] | |
Simple Ratio | SR | [54] | |
Simple Ratio Red Edge | SRRE | [55] |
Global | Timilia | Ciclope | ||||
---|---|---|---|---|---|---|
VIs | Grain Yield | Protein | Grain Yield | Protein | Grain Yield | Protein |
CVI | 0.60 ** | 0.64 ** | 0.54 * | 0.63 ** | 0.71 *** | 0.65 ** |
GNDRE | 0.54 * | 0.66 ** | 0.45 n.s. | 0.65 ** | 0.70 *** | 0.67 *** |
GNDVI | 0.49 n.s. | 0.63 ** | 0.44 n.s. | 0.62 ** | 0.67 *** | 0.65 ** |
MCARI2 | 0.59 ** | 0.66 ** | 0.48 n.s. | 0.68 ** | 0.66 ** | 0.65 ** |
MTVI2 | 0.61 ** | 0.67 ** | 0.48 n.s. | 0.68 ** | 0.67 *** | 0.66 ** |
MTVI | 0.41 n.s. | 0.57 ** | 0.40 n.s. | 0.66 ** | 0.44 n.s. | 0.48 n.s. |
NDRE | 0.13 n.s. | 0.37 n.s. | 0.29 n.s. | 0.38 n.s. | 0.46 n.s. | 0.45 n.s. |
NDVI | 0.47 n.s. | 0.64 ** | 0.40 n.s. | 0.63 ** | 0.66 ** | 0.65 ** |
OSAVI | 0.46 n.s. | 0.62 ** | 0.43 n.s. | 0.66 ** | 0.55 * | 0.57 * |
RDVI | 0.46 n.s. | 0.62 ** | 0.43 n.s. | 0.65 ** | 0.57 ** | 0.58 ** |
RTVI | 0.24 n.s. | 0.21 n.s. | 0.30 n.s. | 0.39 n.s. | 0.36 n.s. | 0.05 n.s. |
SR | 0.31 n.s. | 0.54 * | 0.28 n.s. | 0.53 * | 0.54 * | 0.56 * |
SRRE | 0.57 ** | 0.67 *** | 0.48 n.s. | 0.66 ** | 0.71 *** | 0.68 *** |
Models | |||||||
---|---|---|---|---|---|---|---|
LM | RF | SVM | k-NN | NN | |||
Global | Grain yield | R2 | 0.59 | 0.59 | 0.56 | 0.53 | 0.51 |
RMSE | 1.19 | 0.54 | 0.46 | 0.46 | 0.55 | ||
MAE | 1.03 | 0.43 | 0.34 | 0.34 | 0.53 | ||
Protein | R2 | 0.46 | 0.42 | 0.48 | 0.45 | 0.44 | |
RMSE | 2.00 | 0.86 | 0.77 | 0.69 | 0.49 | ||
MAE | 1.66 | 0.70 | 0.64 | 0.56 | 0.49 | ||
Timilia | Grain yield | R2 | 0.55 | 0.67 | 0.62 | 0.52 | 0.64 |
RMSE | 0.87 | 0.50 | 0.59 | 0.51 | 0.58 | ||
MAE | 0.77 | 0.42 | 0.39 | 0.41 | 0.48 | ||
Protein | R2 | 0.55 | 0.72 | 0.68 | 0.56 | 0.69 | |
RMSE | 1.42 | 0.53 | 0.65 | 0.51 | 2.48 | ||
MAE | 1.25 | 0.38 | 0.55 | 0.47 | 0.48 | ||
Ciclope | Grain yield | R2 | 0.52 | 0.76 | 0.70 | 0.66 | 0.67 |
RMSE | 0.81 | 0.22 | 0.26 | 0.32 | 0.64 | ||
MAE | 0.66 | 0.20 | 0.25 | 0.27 | 0.54 | ||
Protein | R2 | 0.49 | 0.79 | 0.70 | 0.70 | 0.72 | |
RMSE | 1.80 | 0.56 | 0.72 | 0.72 | 0.57 | ||
MAE | 1.32 | 0.44 | 0.65 | 0.66 | 0.47 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Badagliacca, G.; Messina, G.; Presti, E.L.; Preiti, G.; Di Fazio, S.; Monti, M.; Modica, G.; Praticò, S. Durum Wheat (Triticum durum Desf.) Grain Yield and Protein Estimation by Multispectral UAV Monitoring and Machine Learning Under Mediterranean Conditions. AgriEngineering 2025, 7, 99. https://doi.org/10.3390/agriengineering7040099
Badagliacca G, Messina G, Presti EL, Preiti G, Di Fazio S, Monti M, Modica G, Praticò S. Durum Wheat (Triticum durum Desf.) Grain Yield and Protein Estimation by Multispectral UAV Monitoring and Machine Learning Under Mediterranean Conditions. AgriEngineering. 2025; 7(4):99. https://doi.org/10.3390/agriengineering7040099
Chicago/Turabian StyleBadagliacca, Giuseppe, Gaetano Messina, Emilio Lo Presti, Giovanni Preiti, Salvatore Di Fazio, Michele Monti, Giuseppe Modica, and Salvatore Praticò. 2025. "Durum Wheat (Triticum durum Desf.) Grain Yield and Protein Estimation by Multispectral UAV Monitoring and Machine Learning Under Mediterranean Conditions" AgriEngineering 7, no. 4: 99. https://doi.org/10.3390/agriengineering7040099
APA StyleBadagliacca, G., Messina, G., Presti, E. L., Preiti, G., Di Fazio, S., Monti, M., Modica, G., & Praticò, S. (2025). Durum Wheat (Triticum durum Desf.) Grain Yield and Protein Estimation by Multispectral UAV Monitoring and Machine Learning Under Mediterranean Conditions. AgriEngineering, 7(4), 99. https://doi.org/10.3390/agriengineering7040099