Almond cultivar identification using machine learning classifiers applied to UAV-based multispectral data

References

• Ahmed, O. S., A. Shemrock, D. Chabot, C. Dillon, G. Williams, R. Wasson, and S. E. Franklin. 2017. “Hierarchical Land Cover and Vegetation Classification Using Multispectral Data Acquired from an Unmanned Aerial Vehicle.” International Journal of Remote Sensing 38 (8–10): 8–10–20522037–2052. Taylor & Francis. doi:10.1080/01431161.2017.1294781.
   Web of Science ®Google Scholar
• Antje, K., and C. S. Signorino. 2018. “Using Support Vector Machines for Survey Research.” Survey Practice 11 (1): 1–14. doi:10.29115/SP-2018-0001.
   Google Scholar
• Avola, G., S. Filippo Di Gennaro, C. Cantini, E. Riggi, F. Muratore, C. Tornambè, and A. Matese. 2019. “Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars.” Remote Sensing 11 (10): 10–1242. Multidisciplinary Digital Publishing Institute. doi:10.3390/rs11101242.
   Web of Science ®Google Scholar
• Ayan, E., and H. Murat Ünver. 2018. “Data Augmentation Importance for Classification of Skin Lesions via Deep Learning.” 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), 1–4. doi:10.1109/EBBT.2018.8391469.
   Google Scholar
• Baf, S., E. Im, and M. Bol. 2013. “Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background.“ Int. Journal of Engineering Research and Applications 3: 605–610.
   Google Scholar
• Beek, V., L. T. Jonathan, B. Somers, and P. Coppin. 2013. “Stem Water Potential Monitoring in Pear Orchards through WorldView-2 Multispectral Imagery.” In Remote Sensing Vol. 5 (12), 6647–6666. Multidisciplinary Digital Publishing Institute. doi:10.3390/rs5126647.
   Google Scholar
• Belgiu, M., and L. Drăguţ. 2016. “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” Isprs Journal of Photogrammetry and Remote Sensing 114 (April): 24–31. doi:10.1016/j.isprsjprs.2016.01.011.
   Web of Science ®Google Scholar
• Borges, D., L. Padua, I. Costa Azevedo, J. Silva, J. J. Sousa, I. Sousa–Pinto, and J. Alberto Gonçalves. 2021. “Classification of an Intertidal Reef by Machine Learning Techniques Using UAV Based RGB and Multispectral Imagery.” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 64–67. doi:10.1109/IGARSS47720.2021.9554221.
   Google Scholar
• Chandrashekar, G., and F. Sahin. 2014. “A Survey on Feature Selection Methods.” Computers & Electrical Engineering, 40th-Year Commemorative Issue 40 (1): 16–28. doi:10.1016/j.compeleceng.2013.11.024.
   Web of Science ®Google Scholar
• Chenari, A., Y. Erfanifard, M. Dehghani, and H. R. Pourghasemi. 2017. “Woodland Mapping at Single-Tree Levels Using Object-Oriented Classification of Unmanned Aerial Vehicle (Uav) Images.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, no. September: 43–49. doi:10.5194/isprs-archives-XLII-4-W4-43-2017.
   Google Scholar
• Chen, Z., F. Jiang, Y. Cheng, G. Xin, W. Liu, and J. Peng. 2018. “XGBoost Classifier for DDoS Attack Detection and Analysis in SDN-Based Cloud.” 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), 251–256. doi:10.1109/BigComp.2018.00044.
   Google Scholar
• Cordeiro, V., C. Alves, J. Vieira, and M. R. Barroso. 2005. “Evaluation of Almond Cultivar Adaptation in Trás-Os-Montes Region (Portugal).“ XIII GREMPA Meeting on Almonds and Pistachios, Zaragoza, 2005, 63: 5.
   Google Scholar
• Csillik, O., J. Cherbini, R. Johnson, A. Lyons, and M. Kelly. 2018. “Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks.” Drones 2 (4): 39. Multidisciplinary Digital Publishing Institute. doi:10.3390/drones2040039.
   Google Scholar
• Dainelli, R., P. Toscano, S. Filippo Di Gennaro, and A. Matese. 2021. “Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—a Systematic Review. Part II: Research Applications.” Forests 12 (4): 4–397. Multidisciplinary Digital Publishing Institute. doi:10.3390/f12040397.
   Web of Science ®Google Scholar
• Daponte, P., L. De Vito, L. Glielmo, L. Iannelli, D. Liuzza, F. Picariello, and G. Silano. 2019. “A Review on the Use of Drones for Precision Agriculture.” IOP Conference Series: Earth and Environmental Science, IOP Publishing, May 012022–275. doi:10.1088/1755-1315/275/1/012022.
   Google Scholar
• Davis, J., and M. Goadrich. 2006. “The Relationship Between Precision-Recall and ROC Curves.”Proceedings of the 23rd International Conference on Machine Learning - ICML, Pittsburgh, Pennsylvania: ACM Press 06, 233–240. doi:10.1145/1143844.1143874.
   Google Scholar
• de Herralde, F., R. Savé, C. Biel, I. Batlle, and F. J. Vargas. 2001. “Differences in Drought Tolerance in Two Almond Cultivars: ‘Lauranne’ and ‘Masbovera,’.“ XI GREMPA Seminar on Pistachios and Almonds, Zaragoza, 2001, 7: 149–154.
   Google Scholar
• Egea, J., E. Ortega, P. Martı́nez-Gómez, and F. Dicenta. 2003. “Chilling and Heat Requirements of Almond Cultivars for Flowering.” Environmental and Experimental Botany 50 (1): 79–85. doi:10.1016/S0098-8472(03)00002-9.
   Web of Science ®Google Scholar
• Eli-Chukwu, N. C. 2019. “Applications of Artificial Intelligence in Agriculture: A Review.” Engineering, Technology & Applied Science Research 9 (4): 4377–4383. doi:10.48084/etasr.2756.
   Web of Science ®Google Scholar
• Fassnacht, F. E., H. Latifi, K. Stereńczak, A. Modzelewska, M. Lefsky, L. T. Waser, C. Straub, and A. Ghosh. 2016. “Review of Studies on Tree Species Classification from Remotely Sensed Data.” Remote Sensing of Environment 186 (December): 64–87. doi:10.1016/j.rse.2016.08.013.
   Web of Science ®Google Scholar
• Fiorucci, F., F. Ardizzone, A. Cesare Mondini, A. Viero, and F. Guzzetti. 2018. “Visual Interpretation of Stereoscopic NDVI Satellite Images to Map Rainfall-Induced Landslides.” Landslides 1 (16): 165–174. doi:10.1007/s10346-018-1069-y.
   Web of Science ®Google Scholar
• Ghamisi, P., B. Rasti, N. Yokoya, Q. Wang, B. Hofle, L. Bruzzone, F. Bovolo, et al. 2019. “Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art.” IEEE Geoscience and Remote Sensing Magazine 7 (1): 6–39. doi:10.1109/MGRS.2018.2890023.
   Web of Science ®Google Scholar
• Gitelson, A. A., Y. J. Kaufman, and M. N. Merzlyak. 1996. “Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS.” Remote Sensing of Environment 58 (3): 289–298. doi:10.1016/S0034-4257(96)00072-7.
   Web of Science ®Google Scholar
• Gomes, L., T. Nobre, A. Sousa, F. Rei, and N. Guiomar. 2020. “Hyperspectral Reflectance as a Basis to Discriminate Olive Varieties—a Tool for Sustainable Crop Management.” Sustainability 12 (7): 3059. doi:10.3390/su12073059.
   Web of Science ®Google Scholar
• Grandini, M., E. Bagli, and G. Visani. August 2020. “Metrics for Multi-Class Classification: An Overview.” ArXiv: 2008 05756 [Cs, Stat]. http://arxiv.org/abs/2008.05756.
   Google Scholar
• Haara, A., and M. Haarala. 2002. “Tree Species Classification Using Semi-Automatic Delineation of Trees on Aerial Images.” Scandinavian Journal of Forest Research 17 (6): 556–565. Taylor & Francis. doi:10.1080/02827580260417215.
   Web of Science ®Google Scholar
• Horning, N. 2010. “Random Forests: An Algorithm for Image Classification and Generation of Continuous Fields Data Sets.“ International Conference on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, Japan, 2010, 911: 6.
   Google Scholar
• Huete, A. R. 1988. “A Soil-Adjusted Vegetation Index (SAVI).” Remote Sensing of Environment 25 (3): 295–309. doi:10.1016/0034-4257(88)90106-X.
   Web of Science ®Google Scholar
• Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira. 2002. “Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices.” Remote Sensing of Environment 83 (1): 195–213. doi:10.1016/S0034-4257(02)00096-2.
   Web of Science ®Google Scholar
• Inoue, H. 2018. “Data Augmentation by Pairing Samples for Images Classification.” ArXiv:1801.02929 [Cs, Stat], April. http://arxiv.org/abs/1801.02929.
   Google Scholar
• Jafarbiglu, H., and A. Pourreza. 2022. “A Comprehensive Review of Remote Sensing Platforms, Sensors, and Applications in Nut Crops.” Computers and Electronics in Agriculture 197: 23. doi:10.1016/j.compag.2022.106844.
   Web of Science ®Google Scholar
• Jha, K., A. Doshi, P. Patel, and M. Shah. 2019. “A Comprehensive Review on Automation in Agriculture Using Artificial Intelligence.” Artificial Intelligence in Agriculture 2 (June): 1–12. doi:10.1016/j.aiia.2019.05.004.
   Google Scholar
• Kok, Z. H., A. Rashid Mohamed Shariff, M. S. M. Alfatni, and S. Khairunniza-Bejo. 2021. “Support Vector Machine.” Computers and Electronics in Agriculture 191 (December): 106546. doi:10.1016/j.compag.2021.106546.
   Web of Science ®Google Scholar
• Laskari, M., G. Menexes, I. Kalfas, I. Gatzolis, and C. Dordas. 2022. “Water Stress Effects on the Morphological, Physiological Characteristics of Maize (Zea Mays L.), and on Environmental Cost.” Agronomy 12 (10): 2386. doi:10.3390/agronomy12102386.
   Web of Science ®Google Scholar
• Lisein, J., A. Michez, H. Claessens, and P. Lejeune. 2015. “Discrimination of Deciduous Tree Species from Time Series of Unmanned Aerial System Imagery.” In PLOS ONE. edited by M. Cristani, Vol. 10, e0141006. 11. doi: 10.1371/journal.pone.0141006.
   Google Scholar
• Loussaief, S., and A. Abdelkrim. 2016. “Machine Learning Framework for Image Classification.”2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, (SETIT), 58–61. doi:10.1109/SETIT.2016.7939841.
   Google Scholar
• Mateo-Aroca, A., G. García-Mateos, A. Ruiz-Canales, J. María Molina-García-Pardo, and J. Miguel Molina-Martínez. 2019. “Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture.” Water 11 (2): 255. Multidisciplinary Digital Publishing Institute. doi:10.3390/w11020255.
   Web of Science ®Google Scholar
• Mavridou, E., E. Vrochidou, G. A. Papakostas, T. Pachidis, and V. G. Kaburlasos. 2019. “Machine Vision Systems in Precision Agriculture for Crop Farming.” Journal of Imaging 5 (12): 89. doi:10.3390/jimaging5120089.
   PubMed Web of Science ®Google Scholar
• Miarnau, X., S. Alegre, and F. Vargas. 2010. “Productive Potential of Six Almond Cultivars Under Regulated Deficit Irrigation.“ XIV GREMPA Meeting on Pistachios and Almonds, Zaragoza, 2010, 94: 6.
   Google Scholar
• Michel, J., D. Youssefi, and M. Grizonnet. 2014. “Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 53 (2): 952–964. doi:10.1109/TGRS.2014.2330857.
   Web of Science ®Google Scholar
• Noble, W. S. 2006. “What is a Support Vector Machine?” Nature Biotechnology 24 (12): 1565–1567. doi:10.1038/nbt1206-1565.
   PubMed Web of Science ®Google Scholar
• Pádua, L., T. Adão, J. Hruška, J. J. Sousa, E. Peres, R. Morais, and A. Sousa. 2017. “Very High Resolution Aerial Data to Support Multi-Temporal Precision Agriculture Information Management.” Procedia Computer Science, CENTERIS 2017 - International Conference on ENTERprise Information Systems/ProjMAN 2017 - International Conference on Project MANagement/HCist 2017 - International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN/HCist 2017, 121 (January): 407–414. doi:10.1016/j.procs.2017.11.055.
   Google Scholar
• Pádua, L., P. Marques, L. Martins, A. Sousa, E. Peres, and J. J. Sousa. 2020. “Monitoring of Chestnut Trees Using Machine Learning Techniques Applied to UAV-Based Multispectral Data.” Remote Sensing 12 (18): 3032. doi:10.3390/rs12183032.
   Web of Science ®Google Scholar
• Pathak, T. B., M. L. Maskey, and J. P. Rijal. 2021. “Impact of Climate Change on Navel Orangeworm, a Major Pest of Tree Nuts in California.” The Science of the Total Environment 755 (February): 142657. doi:10.1016/j.scitotenv.2020.142657.
   PubMed Web of Science ®Google Scholar
• Pedro, M., L. Pádua, T. Adão, J. Hruška, E. Peres, A. Sousa, and J. J. Sousa. 2019. “UAV-Based Automatic Detection and Monitoring of Chestnut Trees.” Remote Sensing 11 (7): 855. doi:10.3390/rs11070855.
   Web of Science ®Google Scholar
• Pinter, P. J., Jr., J. L. Hatfield, J. S. Schepers, E. M. Barnes, M. Susan Moran, C. S. T. Daughtry, and D. R. Upchurch. 2003. “Remote Sensing for Crop Management.” Photogrammetric Engineering & Remote Sensing 69 (6): 647–664. doi:10.14358/PERS.69.6.647.
   Web of Science ®Google Scholar
• Preethi, C., N. C. Brintha, and C. K. Yogesh 2021. “An Comprehensive Survey on Applications of Precision Agriculture in the Context of Weed Classification, Leave Disease Detection, Yield Prediction and UAV Image Analysis.” In Advances in Parallel Computing, edited by D. J. Hemanth, M. Elhosney, T. N. Nguyen, and S. Lakshmanan. “An Comprehensive Survey on Applications of Precision Agriculture in the Context of Weed Classification, Leave Disease Detection, Yield Prediction and UAV Image Analysis.” in Advances in Parallel Computing. edited by. IOS Press. doi: 10.3233/APC210152.
   Google Scholar
• Probst, P., B. Bischl, and A.L. Boulesteix. 2018. “Tunability: Importance of Hyperparameters of Machine Learning Algorithms.” arXiv. http://arxiv.org/abs/1802.09596.
   Google Scholar
• Reid, A., F. Ramos, and S. Sukkarieh. 2011. “Multi-Class Classification of Vegetation in Natural Environments Using an Unmanned Aerial System.”2011 IEEE International Conference on Robotics and Automation, Shanghai, China: IEEE 2953–2959. doi:10.1109/ICRA.2011.5980061.
   Google Scholar
• Ren, S., X. Chen, and A. Shuai. 2017. “Assessing Plant Senescence Reflectance Index-Retrieved Vegetation Phenology and Its Spatiotemporal Response to Climate Change in the Inner Mongolian Grassland.” International Journal of Biometeorology 61 (4): 601–612. doi:10.1007/s00484-016-1236-6.
   PubMed Web of Science ®Google Scholar
• Rodrigues, P., A. Venâncio, and N. Lima. 2012. “Mycobiota and Mycotoxins of Almonds and Chestnuts with Special Reference to Aflatoxins.” Food Research International 48 (1): 76–90. doi:10.1016/j.foodres.2012.02.007.
   Web of Science ®Google Scholar
• Rouse, J. W., Jr., R. H. Haas, J. A. Schell, and D. W. Deering. 1974. “Monitoring Vegetation Systems in the Great Plains with Erts.” NASA Special Publication 351 (January): 309.
   Google Scholar
• Saito, T., M. Rehmsmeier, and G. Brock. 2015. “The Precision-Recall Plot is More Informative Than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets.” Plos One 10 (3): e0118432. doi:10.1371/journal.pone.0118432.
   PubMed Web of Science ®Google Scholar
• Soares, C., P. Rodrigues, S. W. Peterson, N. Lima, and A. Venâncio. 2012. “Three New Species of Aspergillus Section Flavi Isolated from Almonds and Maize in Portugal.” Mycologia 104 (3): 682–697. Taylor & Francis. doi:10.3852/11-088.
   PubMed Web of Science ®Google Scholar
• Sumner, D. A., W. A. Matthews, J. Medellín-Azuara, and A. Bradley University of California. 2016. The Economic Impacts of the California Almond Industry. California: Almond Board of California.
   Google Scholar
• Taha, B., and A. Shoufan. 2019. “Machine Learning-Based Drone Detection and Classification: State-Of-The-Art in Research.” IEEE Access 7: 138669–138682. doi:10.1109/ACCESS.2019.2942944.
   Web of Science ®Google Scholar
• Tucker, C. J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote Sensing of Environment 8 (2): 127–150. doi:10.1016/0034-4257(79)90013-0.
   Web of Science ®Google Scholar
• Yaping, X., V. Shrestha, C. Piasecki, B. Wolfe, L. Hamilton, R. J. Millwood, M. Mazarei, and C. Neal Stewart. 2021. “Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum Virgatum) Using UAV-Based Imagery.” In Plants Vol. 10 (12), 2726. Multidisciplinary Digital Publishing Institute. doi:10.3390/plants10122726.
   Google Scholar
• Zhou, H., X. Wang, and G. Schaefer. 2011. “Mean Shift and Its Application in Image Segmentation.” In Innovations in Intelligent Image Analysis, edited by H. Kwaśnicka and L. C. Jain, 291–312, Vol. 339. Studies in Computational Intelligence. Berlin, HeidelbergBerlin Heidelberg: Springer. doi:10.1007/978-3-642-17934-1_13.
   Google Scholar