Erratum: Maresma, A., et al. Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service. Remote Sens. 2017, 9, 648

Maresma, Ángel; Ariza, Mar; Martínez, E.; Lloveras, J.; Martínez‐Casasnovas, José A.

doi:10.3390/rs10030368

Cited by 7 publications

(3 citation statements)

References 1 publication

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Four VIs were evaluated (Table 2) throughout the sugarcane development cycle, which included the NDVI [37], Normalized Difference Red-Edge Index (NDRE) [38], GNDVI [39], and Wide Dynamic Range Vegetation Index (WDRVI) [40]. As suggested by Abrahão et al [41] and Maresma et al [42], the value 0.1 was adopted for the weighting coefficient (a) of WDRVI. Table 2.…”

Section: Imagery Datamentioning

confidence: 99%

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

et al. 2021

View full text Add to dashboard Cite

Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.

show abstract

Section: Imagery Datamentioning

confidence: 99%

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

et al. 2021

View full text Add to dashboard Cite

show abstract

“…These methods are precise but expensive, destructive, time-consuming and require technical knowledge in sampling which is lacking among farmers. Some manual tools that have aroused interest for in-season N status assessment and crop yield prediction are Crop Circle ACS-470 sensor, Green-Seeker sensor, SPAD chlorophyll meter, CM-100 Chlorophyll meter, Yara N-Sensor, Crop Circle ACS-430 multiplex sensor, hand-held Field Spectro-Radiometer sensor (Analytical Spectral Devices, Inc., Boulder, CO, USA) and CropScan (Muñoz-Huerta et al, 2013;Cilia et al, 2014;Maresma et al, 2016Maresma et al, , 2018Chlingaryan et al, 2018;García-Martínez et al, 2020). Green-Seeker sensor and CM-100 Chlorophyll meter are mostly used as tools to assess in-season N status and predict crop yield in various crops due to their ease of use and portable aspect.…”

Section: Introductionmentioning

confidence: 99%

Use of GreenSeeker and CM-100 as manual tools for nitrogen management and yield prediction in irrigated potato (Solanum tuberosum) production

Satognon

Lelei

Owido

2021

Arch. Agric. Environ. Sci.

View full text Add to dashboard Cite

This study evaluated the possibility of the use of GreenSeeker sensor and CM-100 chlorophyll content meter for in-season N and yield prediction in order to promote timely split N application in potato production in Kenya. Four N-fertilization rates; N0 (0), N1 (60), N2 (90) and N3 (130 kg N/ha) were led out in a Randomized Complete Block Design (RCBD) in a Greenhouse for two seasons. The results showed that % N leaf content was significantly affected by N rates. The % N leaf content and potato leaf chlorophyll content decreased as the season continued whereas the Normalized Difference Vegetation Index (NDVI) increased as the season continued. CM-100 values were significantly correlated with % N leaf content at vegetative (r=0.86***) and tuber initiation (r=0.74***) growth stages of the crop whereas the NDVI values were only significantly correlated with % N leaf at tuber initiation (r=0.82***). A significant relationship was found between CM-100 values taken at different potato stages (end of vegetative, tuber initiation, bulking and maturation stages) and tuber yield (r=0.90***, 0.82***, 0.47* and 0.41*). The NDVI values at end of vegetative growth, tuber initiation and maturation of potato were also significantly correlated with tuber yield (r=0.81***, 0.43* and 0.54*), except at bulking stage (r=0.33). For efficient in-season N management and yield prediction, CM-100 and GreenSeeker are recommended at an early stage of the crop. Further research in the different potato growing areas in Kenya to establish the different thresholds at different potato growth stages is recommended.

show abstract

“…Four VIs were evaluated (Table 2) throughout the sugarcane development cycle, which included the NDVI (ROUSE et al, 1974), Normalized Difference Red-Edge Index (NDRE) (BARNES et al, 2000), GNDVI (GITELSON et al, 1996), and Wide Dynamic Range Vegetation Index (WDRVI) (GITELSON, 2004). As suggested by Abrahão et al (2009) and Maresma et al (2018) the value 0.10 was adopted for the weighting coefficient (a) of WDRVI. Barnes et al (2000) Green Normalized Difference Vegetation Index GNDVI = (NIR -Green) / (NIR + Green) Wide Dynamic Range Vegetation Index WDRVI = (a x NIR -Red)/(a x NIR + Red) Gitelson (2004) Red: reflectance in the red region (630 nm -685 nm); red-edge: reflectance in the transition region (690 nm -730 nm); NIR: reflectance in the near-infrared region (760 nm -1500 nm); Green: reflectance in the green region (542 nm -578 nm); a: weighting coefficient (0.10) Table 3 summarizes the dates of orbital images taken and the respective phenological stage of sugarcane according to Matsuoka and Stolf (2012).…”

Section: Imagery Datamentioning

confidence: 99%

High-resolution data for mapping the spatio-temporal variability of sugarcane fields

Canata¹

View full text Add to dashboard Cite

show abstract

Erratum: Maresma, A., et al. Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service. Remote Sens. 2017, 9, 648

Abstract: After publication of the research paper [1], the authors noticed an error and wish to make the following correction.[...]

Cited by 7 publications

References 1 publication

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

Use of GreenSeeker and CM-100 as manual tools for nitrogen management and yield prediction in irrigated potato (Solanum tuberosum) production

High-resolution data for mapping the spatio-temporal variability of sugarcane fields

Contact Info

Product

Resources

About