Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery

References

• ARA. 2022. “Tequila libre de deforestación; acciones ante el cambio climático.” https://siga.jalisco.gob.mx/comunicacionsemadet/Mecanismo_de_Solucion_ARA.pdf.
   Google Scholar
• Bhakta, I., S. Phadikar, K. Majumder, H. Mukherjee, and A. Sau. 2022. “A novel plant disease prediction model based on thermal images using modified deep convolutional neural network.” Precision Agriculture 24 (1): 23–39. https://doi.org/10.1007/s11119-022-09927-x.
   Web of Science ®Google Scholar
• Calvario, G., T. E. Alarcón, O. Dalmau, B. Sierra, and C. Hernandez. 2020. “An Agave Counting Methodology Based on Mathematical Morphology and Images Acquired Through Unmanned Aerial Vehicles.” Sensors 20 (21): 6247. https://doi.org/10.3390/s20216247.
   PubMed Web of Science ®Google Scholar
• Calvario, G., B. Sierra, T. E. Alarcón, C. Hernandez, and O. Dalmau. 2017. “A Multi-Disciplinary Approach to Remote Sensing Through Low-Cost Uavs.” Sensors 17 (6): 1411. https://doi.org/10.3390/s17061411.
   PubMed Web of Science ®Google Scholar
• Chen, L., Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. 2018. “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” CoRr Abs/1802.02611. http://arxiv.org/abs/1802.02611.
   Google Scholar
• Cohn, D., Z. Ghahramani, and M. Jordan. 1994. “Active Learning with Statistical Models.” Advances in Neural Information Processing Systems 7:705–712.
   Google Scholar
• CRT. 2022. “Consejo Regulador del Tequila (CRT). Consejo Regulador del Tequila, A.C.” https://www.crt.org.mx/index.php/en/.
   Google Scholar
• De Silva, D., and D. Alahakoon. 2022. “An artificial intelligence life cycle: From conception to production.” Patterns 3 (6): 100489. https://doi.org/10.1016/j.patter.2022.100489.
   PubMedGoogle Scholar
• Dodge, S., and L. Karam. 2016. “Understanding How Image Quality Affects Deep Neural Networks.” In 2016 eighth international conference on quality of multimedia experience (QoMEX), 06 June 2016, Lisbon, Protugal, 1–6. IEEE.
   Google Scholar
• Escobar-Flores, J. G., S. Sandoval, and E. Gámiz-Romero. 2022. “Unmanned Aerial Vehicle Images in the Machine Learning for Agave Detection.” Environmental Science and Pollution Research 29 (41): 61662–61673. https://doi.org/10.1007/s11356-022-18985-7.
   PubMed Web of Science ®Google Scholar
• Flores, D., I. González-Hernández, R. Lozano, J. M. Vazquez-Nicolas, and J. L. Hernandez Toral. 2021. Automated Agave Detection and Counting Using a Convolutional Neural Network and Unmanned Aerial Systems. Drones 5 (1): 4. https://doi.org/10.3390/drones5010004.
   Google Scholar
• Gallardo, J. 2018. “Industria del tequila y generación de residuos.” Ciencia y Desarrollo CONACYT. http://wwwcienciaydesarrollomx.
   Google Scholar
• Garnica, J., R. Reich, E. T. Zuñiga, and C. A. Bravo. 2008. “Using remote sensing to support different approaches to identify agave (agave tequilana weber) crops.” Int Arch Photogramm Remote Sens Spat Inf Sci 37 (10): 941–944.
   Google Scholar
• He, K., G. Gkioxari, P. Dollár, and R. B. Girshick. 2017. “Mask R-CNN.” CoRr Abs/1703.06870. http://arxiv.org/abs/1703.06870.
   Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2015. “Deep Residual Learning for Image Recognition.” CoRR abs/1512.03385. http://arxiv.org/abs/1512.03385.
   Google Scholar
• Kingma, D. P., and J. Ba. 2015. “Adam: A Method for Stochastic Optimization.” https://arxiv.org/abs/1412.6980.
   Google Scholar
• Kitano, B. T., C. C. Mendes, A. R. Geus, H. C. Oliveira, and J. R. Souza. 2019. “Corn Plant Counting Using Deep Learning and UAV Images.” IEEE Geoscience and Remote Sensing Letters. 1–5 .
   Google Scholar
• Lin, T., P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie. 2016. “Feature Pyramid Networks for Object Detection.” CoRr Abs/1612.03144. http://arxiv.org/abs/1612.03144.
   Google Scholar
• Lin, T. Y., M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. 2014. “Microsoft Coco: Common Objects in Context.” In Computer Vision – ECCV 2014, edited by D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, 740–755. Cham: Springer International Publishing.
   Google Scholar
• Li, J., J. Tao, L. Ding, H. Gao, Z. Deng, Y. Luo, and Z. Li. 2018. “A New Iterative Synthetic Data Generation Method for Cnn Based Stroke Gesture Recognition.” Multimedia Tools & Applications 77 (13): 17181–17205. https://doi.org/10.1007/s11042-017-5285-6.
   Web of Science ®Google Scholar
• Maxwell, A. E., T. A. Warner, and L. A. Guillén. 2021. “Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 1: Literature Review.” Remote Sensing 13 (13): 2450. https://doi.org/10.3390/rs13132450.
   Web of Science ®Google Scholar
• Moya-Sánchez, E. U., S. Xambó-Descamps, A. S. Pérez, S. Salazar-Colores, and U. Cortés. 2021. “A Trainable Monogenic Convnet Layer Robust in Front of Large Contrast Changes in Image Classification.” Institute of Electrical and Electronics Engineers Access 9:163735–163746. https://doi.org/10.1109/ACCESS.2021.3128552.
   Google Scholar
• Quevedo, A., A. Sánchez, R. Nancláres, D. P. Montoya, J. Pacho, J. Martínez, and E. U. Moya-Sánchez. 2022. “Jalisco’s Multiclass Land Cover Analysis and Classification Using a Novel Lightweight Convnet with Real-World Multispectral and Relief Data.” https://arxiv.org/abs/2201.10985.
   Google Scholar
• Sandler, M., A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen. 2018. “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation.” CoRr Abs/1801.04381. http://arxiv.org/abs/1801.04381.
   Google Scholar
• Stadelmann, T., M. Amirian, I. Arabaci, M. Arnold, G. F. Duivesteijn, I. Elezi, and M. Geiger. 2018. “Deep Learning in the Wild.” In IAPR Workshop on Artificial Neural Networks in Pattern Recognition, edited by L. Pancioni, F. Schwenker, and E. Trentin, 17–38. Switzerland: Springer Nature.
   Google Scholar
• Tan, M., and Q. V. Le. 2019. “Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks.” CoRR abs/1905.11946. http://arxiv.org/abs/1905.11946.
   Google Scholar
• Whang, S. E., Y. Roh, H. Song, and J. G. Lee. 2023. “Data Collection and Quality Challenges in Deep Learning: A Data-Centric Ai Perspective.” The VLDB Journal 32 (4): 791–813. https://doi.org/10.1007/s00778-022-00775-9.
   Web of Science ®Google Scholar
• Zhang, W., A. K. Liljedahl, M. Kanevskiy, H. E. Epstein, B. M. Jones, M. T. Jorgenson, and K. Kent. 2020. “Transferability of the Deep Learning Mask R-Cnn Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and Uav Images.” Remote Sensing 12 (7): 1085. https://doi.org/10.3390/rs12071085.
   Web of Science ®Google Scholar
• Zhao, R., B. Qian, X. Zhang, Y. Li, R. Wei, Y. Liu, and Y. Pan. 2020. “Rethinking Dice Loss for Medical Image Segmentation.” In 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy, 851–860.
   Google Scholar
• Zhou, Z., M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang. 2018. “Unet++: A nested u-net architecture for medical image segmentation.” CoRr Abs/1807.10165. http://arxiv.org/abs/1807.10165.
   Google Scholar
• Zhu, L., Z. Xie, L. Liu, B. Tao, and W. Tao. 2021. “Iou-Uniform R-Cnn: Breaking Through the Limitations of Rpn.” Pattern Recognition 112:107816. https://doi.org/10.1016/j.patcog.2021.107816.
   Web of Science ®Google Scholar
• Zijdenbos, A., B. Dawant, R. Margolin, and A. Palmer. 1994. “Morphometric Analysis of White Matter Lesions in Mr Images: Method and Validation.” IEEE Transactions on Medical Imaging 13 (4): 716–724. https://doi.org/10.1109/42.363096.
   PubMed Web of Science ®Google Scholar