Remote sensing: A technology for assessment of sugarcane crop acreage and yield

Rao, P. V. Krishna; Rao, V.V. Appa; Venkataratnam, L.

doi:10.1007/bf02942689

Cited by 31 publications

(18 citation statements)

References 3 publications

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“…The few studies that have dealt with sugarcane were largely inspired by what was being done for cereal crops. Direct approaches that link crop biomass to multispectral measurements or vegetation indices were developed from field radiometry (Simoes et al 2005a), aircraft measurements (Schmidt et al 2000), high resolution satellite images (Almeida et al 2006, Ueno et al 2005, Krishna Rao et al 2002, and low resolution satellite images (Bastidas-Obando et al 2007, Schmidt et al 2000. Other indirect approaches that combine multispectral data with agrometerological, production or ecophysiological crop growth models (Rudorff et al 1990, Bastiaanssen et al 2003, Bappel et al 2005 have also been explored.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Bégué

Lebourgeois

Bappel

et al. 2010

International Journal of Remote Sensing

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Sugarcane is a semi-perennial grass whose cultivation is characterized by an extended harvest season lasting several months leading to very high spatio-temporal variability of the crop development and radiometry. The objective of this paper is to understand this variability in order to propose appropriate spectral indicators for yield forecast. To do this, we used ground observations and SPOT4 and SPOT5 time series acquired monthly over a 2-year period over Reunion Island and Guadeloupe (French West Indies). We showed that variations in the NDVI (Normalized Difference Vegetation Index) of sugarcane at the field scale are the result of the interaction between the sugarcane crop calendar and plant phenology in a given climatic environment. We linked these variations to crop variables measured in the field (LAI and leaf color), and derived simple, appropriate NDVI-based indicators of sugarcane yield components at the field scale (cane yield and sugar content). For biomass forecast, the best correlation (r² = 0.78) was obtained with images acquired about 2 months before the harvest season, when all the fields are fully developed but before the maturation stage. For sugar content, a polynomial relationship (r² = 0.75) was observed between the field NDVI acquired during the maturation stage and sugar content in the stalk.

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Section: Introductionmentioning

confidence: 99%

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Bégué

Lebourgeois

Bappel

et al. 2010

International Journal of Remote Sensing

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“…Signatures of sugarcane crop and other land cover features were identified using GT data, tone, texture, pattern and association from the satellite image. The maximum likelihood classifier was used, which calculates the probability of a pixel belonging to a particular class (Rao, 2002). Data from the training sets (signature) are assumed to be normally distributed, which allows the mean vector and the…”

Section: Area Estimation Methodologymentioning

confidence: 99%

Analysis of Sugarcane Acreage and Yield Estimates Derived From Remote Sensing Data and Other Hybrid Approaches Under Fasal Project

Sharma¹,

Saxena²,

Dubey³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Sugarcane (Saccharum spp.) is a most important cash crop of India. India has the largest area under sugarcane cultivation in the world and is the world's second largest producer of sugarcane next to Brazil. Uttar Pradesh stands as the leading producer of sugarcane in India followed by Maharashtra. Under FASAL project, the crop acreage and yield forecasts at National/State/District level, is issued at two levels. This study was carried out to analyze under FASAL Project at Mahalanobis National Crop Forecast Centre, New Delhi during year 2018. Five-year state and district level area, yield and production estimates were observed and analyzed using different approaches in 6 major sugarcane producing states of India. The state and district-wise sugarcane area yield and production estimates of MNCFC were compared with the DES Data of the corresponding year. Two statistical parameters were computed namely, Root Mean Square Error RMSE (%) and correlation coefficient (R). The results when compared to the DES statistics revealed that the RMSE (%) for district level analysis were 23.4%, 30% and 18.35%, while the correlation coefficient (R) between the DES and FASAL estimates was computed 0.94, 0.95 and 0.60 for area, production and yield respectively. These results revealed that RS based technique can be effectively used for state level acreage, yield and production estimation. The national level statistics revealed that the correlation of FASAL sugarcane area and production is in good agreement with the DES estimates as compared to Yield estimates.

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“…The satellite data was analyzed using supervised classification techniques for acreage estimation. The acreage estimation was possible after 100 days of planting of sugarcane and the cane crop could be identified on satellite data after April month (Krishna Rao et al 2002).…”

Section: Rs In Sugarcane Agriculturementioning

confidence: 99%

Application of GPS and GIS in Sugarcane Agriculture

2011

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Technology certainly plays a significant part in the precision agriculture scheme, but understanding the appropriateness of the different forms of technology in response to a management problem is an essential element for successful precision agriculture implementation. Many of the enabling technologies discussed are at varying stages of development with many having restricted application and usefulness when applied at the within-paddock level. However, these technologies can still contribute to largerscale forms of precision agriculture at the region, mill, farm or paddock level. If the sugar industry decides that using global positioning system and geographic information system to chase the benefits of selective harvesting is potentially worthwhile, sorting out the spatial and temporal interactions between yield and CCS will be a critical research issue. Thus the adoption of theses technologies in a precision conservation philosophy could assist in managing the interactions between cane farming and environmental protection in the sensitive coastal floodplain ecosystems.

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Remote sensing: A technology for assessment of sugarcane crop acreage and yield

Cited by 31 publications

References 3 publications

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Analysis of Sugarcane Acreage and Yield Estimates Derived From Remote Sensing Data and Other Hybrid Approaches Under Fasal Project

Application of GPS and GIS in Sugarcane Agriculture

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