Using aerial images to estimate production in forage cactus cultivars

Palavras-chave: Opuntia ficus-indica Mill; estimated yield; pixels; regression model

Resumo

Predicting forage palm yield is a valuable tool for producers, aiding in harvest planning and crop management. This study aimed to evaluate the efficiency of using aerial images from a low-cost setup to estimate cladode production in four forage cactus cultivars. The experiment followed a randomized block design in a 4 x 4 factorial arrangement, with four replications. The first factor included the four cultivars: Giant, Miúda, Elephant Ear, and IPA Sertânia. The second factor involved four cladode harvest management strategies: (1) harvest at nine months, preserving the mother cladode; (2) harvest at nine months, preserving the mother and primary cladode; (3) harvest at 15 months, preserving the mother and primary cladode; and (4) harvest at 21 months, preserving the mother cladode. Before each harvest, aerial images were captured for each plot. The number of cladodes, fresh matter, and dry matter yield per harvest were calculated. Image processing was performed using the ExpImage package in the R software. The efficiency of predicting cactus yield using aerial images obtained with low-cost equipment was confirmed. Individually adjusted models for each cultivar provided greater precision in estimates. However, a single model for all four cultivars achieved a coefficient of determination greater than 77% for estimating fresh matter yield.

Downloads

Não há dados estatísticos.

Referências

Azevedo, A. M. (2022). “ExpImage”: ferramenta para análise de imagens em experimentos. https://cran.r-project.org/web/packages/ExpImage/index.html

Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. D. M., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711-728. https://doi.org/10.1127/0941-2948/2013/0507

Bayar, N., Friji, M., & Kammoun, R. (2018). Optimization of enzymatic extraction of pectin from Opuntia ficus indica cladodes after mucilage removal. Food Chemistry, 241, 127-134. https://doi.org/10.1016/j.foodchem.2017.08.051

Bertolin, N. O., Filgueiras, R., Venancio, L. P., & Mantovani, E. C. (2017). Predição da produtividade de milho irrigado com auxílio de imagens de satélite. Revista Brasileira de Agricultura Irrigada, 11(4), 1627-1638. https://doi.org/10.7127/rbai.v11n400567

Cargnelutti, A., Araujo, M. M., Gasparin, E., & Foltz, D. R. B. (2017). Dimensionamento amostral para avaliação de altura e diâmetro de plantas de timbaúva. Floresta e Ambiente, 25(1), 1-9. https://doi.org/10.1590/2179-8087.121314

Donato, P. E., Pires, A. J., Donato, S. L., Bonomo, P., Silva, J. A., & Aquino, A. A. (2014). Morfometria e rendimento da palma forrageira ‘Gigante’ sob diferentes espaçamentos e doses de adubação orgânica. Revista Brasileira de Ciências Agrárias, 9(1), 151-158. https://doi.org/10.5039/agraria.v9i1a3252

Ferraz, R. L. S., Silva Costa, P., Neto, J. D., Viegas, P. R. A., Melo, A. S., Silva Costa, F., Medeiros, A. S., Magalhães, I. D., Lima, A. S., Cavalcante Júnior, C. A., & Lima, V. L. A. (2019). Estimation of productivity gain by irrigated and fertilized forage palm plants (Opuntia ficus-indica (L.) Mill. and Nopalea cochenillifera (L.) Salm-Dyck): systematic review and meta-analysis. Australian Journal of Crop Science, 13(11), 1873-1882. https://doi.org/10.21475/ajcs.19.13.11.p2095

Guimarães, B. V., Donato, S. L., Azevedo, A. M., Aspiazú, I., & Junior, S. (2018). Predição da produtividade de palma forrageira ‘Gigante’ por caracteres morfológicos e redes neurais artificiais. Revista Brasileira de Engenharia Agrícola e Ambiental, 22(5), 315-319. https://doi.org/10.1590/1807-1929/agriambi.v22n5p315-319

Guimarães, B. V. C., Donato, S. L. R., Aspiazú, I., Azevedo, A. M., & Carvalho, A. J. (2019). Methods for estimating optimum plot size for 'Gigante 'cactus pear. Journal of Agricultural Science, 11(4), 205-215. https://doi.org/10.5539/jas.v11n14p205

Haque, S., Lobaton, E., Nelson, N., Yencho, G. C., Pecota, K. V., Mierop, R., Kudenov, M. W., Boyette, M., & Williams, C. M. (2021). Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery. Computers and Electronics in Agriculture, 182, 106011. https://doi.org/10.1016/j.compag.2021.106011

Hunt Jr., E. R., & Daughtry, C. S. (2018). What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing, 39(15-16), 5345-5376. https://doi.org/10.1080/01431161.2017.1410300

International Union of Soil Science Working [IUSS]. (2015). World reference base for soil resources (WRB). https://www.iuss.org/world-of-soils/

Lopes, L. A., Cardoso, D. B., Camargo, K. S., Silva, T. G. P., Souza, J. S. R., Silva, J. R. C., Morais, J. S., & Araújo, T. P. M. (2019). Palma forrageira na alimentação de ruminantes. Pubvet, 13(2), 1-10. https://doi.org/10.31533/pubvet.v13n3a277.1-10

Nhamo, L., Van Dijk, R., Magidi, J., Wiberg, D., & Tshikolomo, K. (2018). Improving the accuracy of remotely sensed irrigated areas using post-classification enhancement through UAV capability. Remote Sensing, 10(5), 1-12. https://doi.org/10.3390/rs10050712

Nhamo, L., Magidi, J., Nyamugama, A., Clulow, A. D., Sibanda, M., Chimonyo, V. G., & Mabhaudhi, T. (2020). Prospects of improving agricultural and water productivity through unmanned aerial vehicles. Agriculture, 10(7), 1-18. https://doi.org/10.3390/agriculture10070256

Noori, O., & Panda, S. S. (2016). Site-specific management of common olive: Remote sensing, geospatial, and advanced image processing applications. Computers and Electronics in Agriculture, 127, 680-689. https://doi.org/10.1016/j.compag.2016.07.031

Pereira, M. C. D. A., Azevedo, C. A. V., Neto, J. D., Pereira, M. D. O., Ramos, J. G., Nunes, K. G., Lyra, G. B. & Saboya, L. M. F. (2021). Production of forage palm cultivars (Orelha de Elefante Mexicana, IPA-Sertânia and Miúda) under different salinity levels in irrigation water. Australian Journal of Crop Science, 15(7), 977-982. https://doi.org/10.3316/informit.154387132281763

Prilianti, K. R., Brotosudarmo, T. H. P., Anam, S., & Suryanto, A. (2019). Performance comparison of the convolutional neural network optimizer for photosynthetic pigments prediction on plant digital image. AIP Conference Proceedings, 2084(1), 020020. https://doi.org/10.1063/1.5094284

Pound, M. P., Atkinson, J. A., Townsend, A. J., Wilson, M. H., Griffiths, M., Jackson, A. S., Bulat, A., Tzimiropoulos, G., Wells, D. M., Murchie, E. H., Pridmore, T. P., & French, A. P. (2017). Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience, 6(10), 1-10. https://doi.org/10.1093/gigascience/gix083

Ruwanpathirana, P. P., Sakai, K., Jayasinghe, G. Y., Nakandakari, T., Yuge, K., Wijekoon, W. M. C. J., Priyankara, A. C. P., Samaraweera, M. D. S., & Madushanka, P. L. A. (2024). Evaluation of sugarcane crop growth monitoring using vegetation indices derived from RGB-Based UAV images and machine learning models. Agronomy, 14(9), 2059. https://doi.org/10.3390/agronomy14092059

Sandra, Damayanti, R., & Inayah, Z. (2020). Nitrogen fertilizer prediction of maize plant with TCS3200 sensor based on digital image processing. IOP Conference Series: Earth and Environmental Science, 515, 1-10. https://doi.org/10.1088/1755-1315/515/1/012014

Shi, Y., Thomasson, J. A., Murray, S. C., Pugh, N. A., Rooney, W. L., Shafian, S., Rajan, N., Rouze, G., Morgan, C. L. S., Neely, H. L., Rana, A., Bagavathiannan, M. V., Henrickson, J., Bowden, E., Valasek, J., Olsenholler, J., Bishop, M. P., Sheridan, R., Putman, E. B., & Yang, C. (2016). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS ONE, 11(7), 1-26. https://doi.org/10.1371/journal.pone.0159781

Trindade, F. S., Carvalho Alves, M., Noetzold, R., Andrade, I. C., & Pozza, A. A. A. (2019). Relação espectro-temporal de índices de vegetação com atributos do solo e produtividade da soja. Revista de Ciências Agrárias Amazonian Journal of Agricultural and Environmental Sciences, 62, 1-11. https://doi.org/10.22491/rca.2019.2928

Volpe, M., Goldfarb, J. L., & Fiori, L. (2018). Hydrothermal carbonization of Opuntia ficus-indica cladodes: Role of process parameters on hydrochar properties. Bioresource Technology, 247, 310-318. https://doi.org/10.1016/j.biortech.2017.09.072

Yuan, W., Wijewardane, N. K., Jenkins, S., Bai, G., Ge, Y., & Graef, G. L. (2019). Early prediction of soybean traits through color and texture features of canopy RGB imagery. Scientific Reports, 9, 1-14. https://doi.org/10.1038/s41598-019-50480-x

Publicado
2025-06-13
Como Citar
Gomes , L. S. de P., Azevedo, A. M., Valadares, N. R., Alves, R. A., Fernandes, A. C. G., Rodrigues , C. H. O., Almeida Neta, M. N., & Guimarães, B. V. C. (2025). Using aerial images to estimate production in forage cactus cultivars. Acta Scientiarum. Agronomy, 47(1), e71490. https://doi.org/10.4025/actasciagron.v47i1.71490
Seção
Produção Vegetal

 

2.0
2019CiteScore
 
 
60th percentile
Powered by  Scopus

 

2.0
2019CiteScore
 
 
60th percentile
Powered by  Scopus