Resumen
La Cuscuta spp. es una planta parásita que causa pérdidas estimadas en el 50 % del rendimiento de una amplia variedad de cultivos agrícolas, incluidos verduras, forrajes y árboles. En la búsqueda de alternativas para resolver este problema, los pequeños agricultores están explorando la aplicación de nuevas tecnologías en la producción de alimentos. Este trabajo presenta un modelo de clasificación de Cuscuta spp. con teledetección mediante imágenes aéreas recolectadas por UAV con las que se generan ortofotos donde se señalan las zonas infestadas. El modelo propuesto segmenta el color amarillento característico del tallo de Cuscuta spp. en el espacio de color HSV para llevar a cabo el proceso de entrenamiento de una Red Neuronal Convolucional (CNN) profunda. En los experimentos se incluyeron imágenes RGB de un cultivo de pimiento picante (Capsicum annuum Linnaeus) con presencia de Cuscuta spp. Además, realizamos la validación cruzada de 5 iteraciones del modelo con diferentes conjuntos de datos al emplear imágenes recolectadas durante tres semanas consecutivas para identificar el crecimiento de la zona afectada por la maleza. La arquitectura ResNet, de acuerdo con las métricas empleadas, resultó ser el mejor modelo para clasificar Cuscuta spp y no Cuscuta. El método propuesto permite a los pequeños productores identificar y localizar la maleza en las primeras fases de crecimiento para facilitar las labores de eliminación y mitigación.
2058 609- fotografía aérea
- cultivos de campo
- reconocimiento de patrones
- arquitectura ResNet
- vehículos aéreos no tripulados
- plagas
- Ahmadi, K., Omidi, H., Amini Dehaghi, M., & Naghdi Badi, H. (2019). A Review on the Botanical, Phytochemical and Pharmacological Characteristics of Kelussia odoratissima Mozaff. Journal of Medicinal Plants, 18(72), 30-45. https://doi.org/10.29252/jmp.4.72.S12.30
- Ali, Z. A., Yang, C., Israr, A., & Zhu, Q. (2023). A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery. Drones, 7(2), 97. https://doi.org/10.3390/drones7020097
- Aly, R., & Dubey, N. (2014). Weed Management for Parasitic Weeds. In B. Chauhan & G. Mahajan (Eds.), Recent Advances in Weed Management. Springer. https://doi.org/10.1007/978-1-4939-1019-9_14
- Bajwa, A. A., Farooq, M., Al-Sadi, A. M., Nawaz, A., Jabran, K., & Siddique, K. H. (2020). Impact of climate change on biology and management of wheat pests. Crop Protection, 137, 105304. https://doi.org/10.1016/j.cropro.2020.105304
- Blanco-Valdes, Y., Leyva-Galán, Á., & Castro-Lizazo, I. (2018). Determination of the critical period of weeds competition in pepper crop (Capsicum annum, L.). Cultivos Tropicales, 39(3), 18-24. https://ediciones.inca.edu.cu/index.php/ediciones/article/download/1461/pdf/7291
- Burkart, A., Hecht, V. L., Kraska, T., & Rascher, U. (2018). Phenological analysis of unmanned aerial vehicle-based time series of barley imagery with high temporal resolution. Precision Agriculture, 19, 134-146. https://doi.org/10.1007/s11119-017-9504-y
- Canicattì, M., & Vallone, M. (2024). Drones in Vegetable Crops: A Systematic Literature Review. Smart Agricultural Technology, 100396. https://doi.org/10.1016/j.atech.2024.100396
- Chollet, F. (2015). Keras: Deep Learning for humans. https://keras.io
- Chollet, F. (2017). Xception: Deep learning with depth wise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258). IEEE. https://doi.org/10.1109/CVPR.2017.195
- Cornejo-Velazquez, E., & Clavel-Maqueda, M. (2024). Set of images of chile crop areas collected with flight plans [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11123207
- Coulombe, J., Brown, P., White, S., Xu, C., & Koech, R. (2018, August). Detection of crop water status using UAV mounted sensor. In XXX International Horticultural Congress IHC2018: VII Conference on Landscape and Urban Horticulture, IV Conference, 1279, 271-278.
- Cournapeau, D. (2007). Scikit Learn: Machine Learning in Python. https://scikit-learn.org/stable/
- Gutiérrez-Lazcano, L., Camacho-Bello, C. J., Cornejo-Velazquez, E., Arroyo-Núñez, J. H., & Clavel-Maqueda, M. (2022). Cuscuta spp. Segmentation Based on Unmanned Aerial Vehicles (UAVs) and Orthomasaics Using a U-Net Xception-Style Model. Remote Sensing, 14(17), 4315. https://doi.org/10.3390/rs14174315
- Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv. https://doi.org/10.48550/arXiv.1704.04861
- Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708). IEEE. https://doi.org/10.1109/CVPR.2017.243
- Iqbal, M., Hussain, M., Abid, A., Ali, M., Nawaz, R., Waqar, M., Ashgar, M., & Iqbal, Z. (2014). A review: Cuscuta (Cuscuta planifora) major weed threat in Punjab–Pakistan. International Journal of Advanced Research in Biological Sciences, 4, 42-46. https://ijarbs.com/pdfcopy/july2014/ijarbs6.pdf
- Ishengoma, F. S., Rai, I. A., & Said, R. N. (2021). Identification of maize leaves infected by fall armyworms using UAV-based imagery and convolutional neural networks. Computers and Electronics in Agriculture, 184, 106124. https://doi.org/10.1016/j.compag.2021.106124
- Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial intelligence review, 53, 5455-5516. https://doi.org/10.1007/s10462-020-09825-6
- Krienke, B., Ferguson, R. B., Schlemmer, M., Holland, K., Marx, D., & Eskridge, K. (2017). Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor. Precision Agriculture, 18, 900-915. https://doi.org/10.1007/s11119-017-9534-5
- Le, Q. V., Tennakoon, K. U., Metali, F., Lim, L. B., & Bolin, J. F. (2015). Impact of Cuscuta australis infection on the photosynthesis of the invasive host, Mikania micrantha, under drought condition. Weed Biology and Management, 15(4), 138-146. https://doi.org/10.1111/wbm.12077
- Martínez, J., Egea, G., Agüera, J., & Pérez-Ruiz, M. (2017). A cost-effective canopy temperature measurement system for precision agriculture: A case study on sugar beet. Precision Agriculture, 18, 95-110. https://doi.org/10.1007/s11119-016-9470-9
- Mishra, J. S. (2009). Biology and management of Cuscuta species. Indian Journal of Weed Science, 41(1 & 2), 1-11. https://www.isws.org.in/IJWSn/File/2009_41_Issue-1&2_1-11.pdf
- OCV. (2023). OpenCV – Open Computer Vision Library. https://opencv.org
- ODM. (2023). WebODM - OpenDroneMap. https://www.opendronemap.org/webodm/
- Oldeland, J., Große-Stoltenberg, A., Naftal, L., & Strohbach, B. J. (2017). The potential of UAV derived image features for discriminating savannah tree species. In The Roles of Remote Sensing in Nature Conservation: A Practical Guide and Case Studies (pp. 183-201). Springer. https://doi.org/10.1007/978-3-319-64332-8_10
- Pandey, A., & Jain, K. (2022). An intelligent system for crop identification and classification from UAV images using conjugated dense convolutional neural network. Computers and Electronics in Agriculture, 192, 106543. https://doi.org/10.1016/j.compag.2021.106543
- Puteh, S., Rodzali, N. F. M., Abdul Majeed, A. P. P., Khairuddin, I. M., Ibrahim, Z. Z., & Razman, M. A. M. (2021). Classification of Capsicum frutescens health condition through features extraction from NDVI values using image processing. In RiTA 2020: Proceedings of the 8th International Conference on Robot Intelligence Technology and Applications. Springer Singapore. https://doi.org/10.1007/978-981-16-4803-8_41
- Qiao, L., Tang, W., Gao, D., Zhao, R., An, L., Li, M., Sun, H., & Song, D. (2022). UAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages. Computers and Electronics in Agriculture, 196, 106775. https://doi.org/10.1016/j.compag.2022.106775
- Sanz, D., Valente, J., del Cerro, J., Colorado, J., & Barrientos, A. (2015). Safe operation of mini UAVs: a review of regulation and best practices. Advanced Robotics, 29(19), 1221–1233. https://doi.org/10.1080/01691864.2015.1051111
- SIAP. (2021). Panorama agroalimentario. Secretaría de Agricultura de México.
- Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv. https://doi.org/10.48550/arXiv.1409.1556
- Smith, A. R. (1978). Color gamut transform pairs. ACM Siggraph Computer Graphics, 12(3), 12-19. https://doi.org/10.1145/965139.807361
- Sosa-Herrera, J. A., Vallejo-Pérez, M. R., Álvarez-Jarquín, N., Cid-García, N. M., & López-Araujo, D. J. (2019). Geographic object-based analysis of airborne multispectral images for health assessment of Capsicum annuum L. crops. Sensors, 19(21), 4817. https://doi.org/10.3390/s19214817
- Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826). IEEE. https://doi.org/10.1109/CVPR.2016.308
- Team, G. B. (2015). TensorFlow. https://www.tensorflow.org/
- Tetila, E. C., Machado, B. B., Astolfi, G., de Souza Belete, N. A., Amorim, W. P., Roel, A. R., & Pistori, H. (2020). Detection and classification of soybean pests using deep learning with UAV images. Computers and Electronics in Agriculture, 179, 105836. https://doi.org/10.1016/j.compag.2020.105836
- West, J. S., Canning, G. G., Perryman, S. A., & King, K. (2017). Novel technologies for the detection of Fusarium head blight disease and airborne inoculum. Tropical Plant Pathology, 42, 203-209. https://doi.org/10.1007/s40858-017-0138-4
- Winston, R. L., Schwarzländer, M., Hinz, H. L., Day, M. D., Cock, M. J., & Julien, M. H. (2014). Biological control of weeds: a world catalogue of agents and their target weeds (5th ed). USDA Forest Service, Forest Health Technology Enterprise Team. http://www.ibiocontrol.org/catalog/JulienCatalogueFHTET_2014_04.pdf
- Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1492-1500). IEEE. https://doi.org/10.1109/CVPR.2017.634
- Yee-Rendon, A., Torres-Pacheco, I., Trujillo-Lopez, A. S., Romero-Bringas, K. P., & Millan-Almaraz, J. R. (2021). Analysis of New RGB Vegetation Indices for PHYVV and TMV Identification in Jalapeño Pepper (Capsicum annuum) leaves using CNNs-based model. Plants, 10(10), 1977. https://doi.org/10.3390/plants10101977
- Zhang, L., Han, W., Niu, Y., Chavez, J. L., Shao, G., & Zhang, H. (2021). Evaluating the sensitivity of water stressed maize chlorophyll and structure based on UAV derived vegetation indices. Computers and Electronics in Agriculture, 185, 106174. https://doi.org/10.1016/j.compag.2021.106174
Detección de malas hierbas en cultivos de Capsicum Annuum L. mediante fotogrametría y algoritmos de aprendizaje profundo. (2024). Ciencia Y Tecnología Agropecuaria, 25(3). https://doi.org/10.21930/rcta.vol25_num3_art:3602
