Aplicación de tecnologías de percepción remota para la estimación del rendimiento en caña de azúcar

Cruz, Guillermo Alberto Zenteno; Vélez, Enrique Palacios; Chávez, Leonardo Tijerina; Flores-Magdaleno, Héctor

doi:10.29312/remexca.v8i7.513

Cited by 7 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These values agree with those obtained by Sevilla and Erazo (2019), who studied the Opuntia ficus-indica cactus crop with Sentinel-2 images and obtained NDVI values in a range of 0.27 to 0.41. Zenteno et al (2017) obtained a similar trend when studying different varieties of corn at various stages of development.…”

Section: Phenological Stages Classification By Image Dronementioning

confidence: 52%

Monitoring flowering phenology of apple trees using remote sensing techniques

CARMONA-GARCÍA,

ARREDONDO-BUSTILLOS,

SALAS-SALAZAR

et al. 2024

Nova Geodesia

View full text Add to dashboard Cite

The apple orchards with large land extensions represent a challenge in monitoring crops. Remote sensing techniques can analyze and obtain information on the general state of crops using multispectral data and vegetation indexes. This study aimed to analyze the flowering phenological of three apple orchards using images from Sentinel-2 satellite and a Phantom 4 pro/pro+ drone in three apple-producing regions. The images were processed using ArcGIS software and the Normalized Difference Vegetation Index (NDVI). The results were statistically analyzed through discriminant to classify and identify significant differences between the flowering phenological stages. NDVI maps were obtained for the study areas, and the NDVI values ranged from 0.09 to 0.26 and from 0.22 to 0.35 for drone and satellite images, respectively. It was possible to differentiate between two groups of phenological stages in the apple orchards (pre-bloom and post-bloom). The information generated can be a complementary tool for monitoring the apple tree crop.

show abstract

Section: Phenological Stages Classification By Image Dronementioning

confidence: 52%

Monitoring flowering phenology of apple trees using remote sensing techniques

CARMONA-GARCÍA,

ARREDONDO-BUSTILLOS,

SALAS-SALAZAR

et al. 2024

Nova Geodesia

View full text Add to dashboard Cite

show abstract

“…In another study carried out with Landsat images, the indices that best-predicted maize crop yield were Enhanced Vegetation Index (EVI), SAVI and Optimized Soil-Adjusted Vegetation Index (OSAVI), which were different from the NDVI [62]. Through the use of Landsat-7 ETM+ and Spot 5 images, they found high correlation values of the NDVI index in the yield of sugarcane, sugar and barley [63,64].…”

Section: Discussionmentioning

confidence: 98%

Yield Predictions of Four Hybrids of Maize (Zea mays) Using Multispectral Images Obtained from UAV in the Coast of Peru

et al. 2022

View full text Add to dashboard Cite

Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability for the farmer’s economy. In this study, we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using vegetation indices (VIs). A total of 10 VIs (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. Highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA showed clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimating the performance, showing greater precision at 51 DAS. The use of unmanned aerial vehicles (UAVs) to monitor crops allows us to optimize resources and helps in making timely decisions in agriculture in Peru.

show abstract

“…In another study carried out with Landsat images, the indices that best predicted corn crop yield were Enhanced Vegetation Index (EVI), SAVI and Optimized Soil-Adjusted Vegetation Index (OSAVI), which were different from the NDVI [61]. Through the use of Landsat-7 ETM+ and Spot 5 images, they found high correlation values of the NDVI index in the yield of sugarcane, sugar and barley [62,63].…”

Section: Discussionmentioning

confidence: 99%

Yield Predictions of Four Hybrids of Maize (Zea mays) using Multispectral Images Obtained from RPAS in the Coast of Peru

Saravia¹,

Salazar²,

Valqui-Valqui³

et al. 2022

Preprint

View full text Add to dashboard Cite

Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability in the farmer's economy. In this study we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using remotely sensed spectral vegetation indices (VI). A total of 10 VI (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. In the present study, highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA indicated a clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimate the performance, showing greater precision at 51 DAS. The use of RPAS to monitor crops allows optimizing resources and helps in making timely decisions in agriculture in Peru.

show abstract

Aplicación de tecnologías de percepción remota para la estimación del rendimiento en caña de azúcar

Cited by 7 publications

References 8 publications

Monitoring flowering phenology of apple trees using remote sensing techniques

Monitoring flowering phenology of apple trees using remote sensing techniques

Yield Predictions of Four Hybrids of Maize (Zea mays) Using Multispectral Images Obtained from UAV in the Coast of Peru

Yield Predictions of Four Hybrids of Maize (Zea mays) using Multispectral Images Obtained from RPAS in the Coast of Peru

Contact Info

Product

Resources

About