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Zheng C, Abd-Elrahman A, Whitaker V, Dalid C. Prediction of Strawberry Dry Biomass from UAV Multispectral Imagery Using Multiple Machine Learning Methods. REMOTE SENSING 2022; 14:4511. [DOI: 10.3390/rs14184511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Biomass is a key biophysical parameter for precision agriculture and plant breeding. Fast, accurate and non-destructive monitoring of biomass enables various applications related to crop growth. In this paper, strawberry dry biomass weight was modeled using 4 canopy geometric parameters (area, average height, volume, standard deviation of height) and 25 spectral variables (5 band original reflectance values and 20 vegetation indices (VIs)) extracted from the Unmanned Aerial Vehicle (UAV) multispectral imagery. Six regression techniques—multiple linear regression (MLR), random forest (RF), support vector machine (SVM), multivariate adaptive regression splines (MARS), eXtreme Gradient Boosting (XGBoost) and artificial neural network (ANN)—were employed and evaluated for biomass prediction. The ANN had the highest accuracy in a five-fold cross-validation, with R2 of 0.89~0.93, RMSE of 7.16~8.98 g and MAE of 5.06~6.29 g. As for the other five models, the addition of VIs increased the R2 from 0.77~0.80 to 0.83~0.86, and reduced the RMSE from 8.89~9.58 to 7.35~8.09 g and the MAE from 6.30~6.70 to 5.25~5.47 g, respectively. Red-edge-related VIs, including the normalized difference red-edge index (NDRE), simple ratio vegetation index red-edge (SRRedEdge), modified simple ratio red-edge (MSRRedEdge) and chlorophyll index red and red-edge (CIred&RE), were the most influential VIs for biomass modeling. In conclusion, the combination of canopy geometric parameters and VIs obtained from the UAV imagery was effective for strawberry dry biomass estimation using machine learning models.
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Senger E, Osorio S, Olbricht K, Shaw P, Denoyes B, Davik J, Predieri S, Karhu S, Raubach S, Lippi N, Höfer M, Cockerton H, Pradal C, Kafkas E, Litthauer S, Amaya I, Usadel B, Mezzetti B. Towards smart and sustainable development of modern berry cultivars in Europe. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 111:1238-1251. [PMID: 35751152 DOI: 10.1111/tpj.15876] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/15/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Fresh berries are a popular and important component of the human diet. The demand for high-quality berries and sustainable production methods is increasing globally, challenging breeders to develop modern berry cultivars that fulfill all desired characteristics. Since 1994, research projects have characterized genetic resources, developed modern tools for high-throughput screening, and published data in publicly available repositories. However, the key findings of different disciplines are rarely linked together, and only a limited range of traits and genotypes has been investigated. The Horizon2020 project BreedingValue will address these challenges by studying a broader panel of strawberry, raspberry and blueberry genotypes in detail, in order to recover the lost genetic diversity that has limited the aroma and flavor intensity of recent cultivars. We will combine metabolic analysis with sensory panel tests and surveys to identify the key components of taste, flavor and aroma in berries across Europe, leading to a high-resolution map of quality requirements for future berry cultivars. Traits linked to berry yields and the effect of environmental stress will be investigated using modern image analysis methods and modeling. We will also use genetic analysis to determine the genetic basis of complex traits for the development and optimization of modern breeding technologies, such as molecular marker arrays, genomic selection and genome-wide association studies. Finally, the results, raw data and metadata will be made publicly available on the open platform Germinate in order to meet FAIR data principles and provide the basis for sustainable research in the future.
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Affiliation(s)
- Elisa Senger
- Institute of Bio- and Geosciences, IBG-4 Bioinformatics, BioSC, CEPLAS, Forschungszentrum Jülich, Jülich, Germany
| | - Sonia Osorio
- Departamento de Biología Molecular y Bioquímica, Instituto de Hortofruticultura Subtropical y Mediterránea 'La Mayora', Universidad de Málaga-Consejo Superior de Investigaciones Científicas, Campus de Teatinos, Málaga, Spain
| | | | - Paul Shaw
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Scotland, UK
| | - Béatrice Denoyes
- Université de Bordeaux, UMR BFP, INRAE, Villenave d'Ornon, France
| | - Jahn Davik
- Department of Molecular Plant Biology, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway
| | - Stefano Predieri
- Bio-Agrofood Department, Institute for Bioeconomy, IBE-CNR, Italian National Research Council, Bologna, Italy
| | - Saila Karhu
- Natural Resources Institute Finland (Luke), Turku, Finland
| | - Sebastian Raubach
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Scotland, UK
| | - Nico Lippi
- Bio-Agrofood Department, Institute for Bioeconomy, IBE-CNR, Italian National Research Council, Bologna, Italy
| | - Monika Höfer
- Institute of Breeding Research on Fruit Crops, Federal Research Centre for Cultivated Plants (JKI), Dresden, Germany
| | - Helen Cockerton
- Genetics, Genomics and Breeding Department, NIAB, East Malling, UK
| | - Christophe Pradal
- CIRAD and UMR AGAP Institute, Montpellier, France
- INRIA and LIRMM, University Montpellier, CNRS, Montpellier, France
| | - Ebru Kafkas
- Department of Horticulture, Faculty of Agriculture, Çukurova University, Balcalı, Adana, Turkey
| | | | - Iraida Amaya
- Unidad Asociada deI + D + i IFAPA-CSIC Biotecnología y Mejora en Fresa, Málaga, Spain
- Laboratorio de Genómica y Biotecnología, Centro IFAPA de Málaga, Instituto Andaluz de Investigación y Formación Agraria y Pesquera, Málaga, Spain
| | - Björn Usadel
- Institute of Bio- and Geosciences, IBG-4 Bioinformatics, BioSC, CEPLAS, Forschungszentrum Jülich, Jülich, Germany
- Institute for Biological Data Science, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Bruno Mezzetti
- Department of Agricultural, Food and Environmental Sciences, Università Politecnica delle Marche, Ancona, Italy
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