Field-scale Assessment of Sugarcane for Mill-level Production Forecasting using Indian Satellite Data

Kumar, Mukesh; Das, Ayan; Chaudhari, K. N.; Dutta, Sujay Kumar; Dakhore, K. K.; Bhattacharya, Bimal K.

doi:10.1007/s12524-021-01442-2

Cited by 5 publications

(7 citation statements)

References 18 publications

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“…Furthermore, the authors emphasized the use of agrometeorological products and satellite metrics derived from SAR in future studies. Different from [23], ref. [25] obtained a sugarcane yield model based on the relationship between the farm scale values of yield and LAI.…”

Section: Attributes Used In the Selected Papers That Made Use Of Stat...mentioning

confidence: 92%

“…Computational advances that lead to the use of machine learning and deep learning algorithms have expanded the development of agricultural crop yield models using empirical approaches and RS data [13,21]. Different strategies have been used to obtain sugarcane yield using empirical models, such as Linear Regression, Multiple Linear Regression, and Stepwise Multiple Regression [11,[22][23][24][25], Support Vector Machine (SVM) [11,18,26,27], Artificial Neural Networks (ANN) [11,28,29], and Random Forest (RF) [12,18,22,26,27,[30][31][32]. As input, they use RS, field, agrometeorological, and terrain data, among others.…”

Section: Empirical Modelsmentioning

confidence: 99%

“…Ref. [23] also considered adopting satellite metrics from SAR in their future work. Figure 10 shows the number of publications by satellite in the selected papers.…”

Section: Attributes Used In the Selected Papers That Made Use Of Stat...mentioning

confidence: 99%

“…Ref. [23] derived a sugarcane yield model using Resourcesat images at mill level. They could estimate the yield of the crop two months before harvest with a deviation of less than 10% from the reported sugarcane production values.…”

Section: Attributes Used In the Selected Papers That Made Use Of Stat...mentioning

confidence: 99%

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Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

de França e Silva,

Chaves,

Luciano

et al. 2024

Remote Sensing

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The sugarcane crop has great socioeconomic relevance because of its use in the production of sugar, bioelectricity, and ethanol. Mainly cultivated in tropical and subtropical countries, such as Brazil, India, and China, this crop presented a global harvested area of 17.4 million hectares (Mha) in 2021. Thus, decision making in this activity needs reliable information. Obtaining accurate sugarcane yield estimates is challenging, and in this sense, it is important to reduce uncertainties. Currently, it can be estimated by empirical or mechanistic approaches. However, the model’s peculiarities vary according to the availability of data and the spatial scale. Here, we present a systematic review to discuss state-of-the-art sugarcane yield estimation approaches using remote sensing and crop simulation models. We consulted 1398 papers, and we focused on 72 of them, published between January 2017 and June 2023 in the main scientific databases (e.g., AGORA-FAO, Google Scholar, Nature, MDPI, among others), using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We observed how the models vary in space and time, presenting the potential, challenges, limitations, and outlooks for enhancing decision making in the sugarcane crop supply chain. We concluded that remote sensing data assimilation both in mechanistic and empirical models is promising and will be enhanced in the coming years, due to the increasing availability of free Earth observation data.

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Section: Attributes Used In the Selected Papers That Made Use Of Stat...mentioning

confidence: 92%

Section: Empirical Modelsmentioning

confidence: 99%

“…Ref. [23] also considered adopting satellite metrics from SAR in their future work. Figure 10 shows the number of publications by satellite in the selected papers.…”

Section: Attributes Used In the Selected Papers That Made Use Of Stat...mentioning

confidence: 99%

Section: Attributes Used In the Selected Papers That Made Use Of Stat...mentioning

confidence: 99%

See 2 more Smart Citations

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

de França e Silva,

Chaves,

Luciano

et al. 2024

Remote Sensing

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“…Yield predictors were multi-date Landsat-8 derived NDVI, absorbed photosynthetically active radiation (APAR), canopy surface temperature and crop water stress index (CWSI). Similarly, Kumar et al, (2022) developed empirical models using NDVI and water scalar (WS) as predictors for sugarcane yield over four factory mill areas in Gujarat and Maharashtra. The successful multi-year evaluation suggests that such empirical approach would be easy to adopt be sugarcane factories to plan for their gate arrivals.…”

Section: Crop Yield Assessmentmentioning

confidence: 99%

Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

Ray

Dadhwal

Navalgund

2022

J Indian Soc Remote Sens

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The paper traces the evolution of agricultural remote sensing in India through its four phases: initial exploratory and aerial data based (1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980)(1981)(1982), IRS Utilization Program led (1983( -1995( ), post-IRS-1C launch (1996( -2011 and since the establishment of a dedicated institution for EO-based crop forecasting and other agricultural applications (2012 onward). Published field-scale studies are discussed before introducing the submissions to the special issue of the journal, namely Advanced Geospatial Technologies for Agriculture with Special emphasis on field level Applications. A total of 17 articles grouped under crop discrimination and mapping, crop yield estimation, crop suitability analysis, irrigation management and others (UAV, fodder and field boundary extraction) and 15 of these describe work over India and remaining two have Iran as their study area. These studies are linked to other published work as well as challenges under major issues of data access, use of UAV, use of smartphones, data processing and crop simulation models. Taken together, these articles report substantial improvements in capabilities of field-scale monitoring for developing new class of agricultural applications in India as well as other regions in the world with small farm holdings.

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AI-Enhanced Remote Sensing Applications in Indian Sugarcane Research: A Comprehensive Review

Vinayaka,

Prasad

2024

Sugar Tech

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Field-scale Assessment of Sugarcane for Mill-level Production Forecasting using Indian Satellite Data

Cited by 5 publications

References 18 publications

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

AI-Enhanced Remote Sensing Applications in Indian Sugarcane Research: A Comprehensive Review

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