Remote Sensing-Based Yield Forecasting for Sugarcane (Saccharum officinarum L.) Crop in India

Dubey, S. K.; Gavli, A. S.; Yadav, S. K.; Sehgal, S.; Ray, S. S.

doi:10.1007/s12524-018-0839-2

Cited by 48 publications

(31 citation statements)

References 20 publications

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“…A wealth of information on yield predictions using historical NDVI data for different crops from several countries is available. Some of the studies using NDVI for yield predictions are by Dubey et al (2018) for sugarcane, Lopresti et al (2015) and Pantazi et al (2016) for wheat and Chang et al (2005) and Huang et al (2013) for rice yield. Rice yield prediction at regional scale in Indo-Gangetic river basin through NDVI is attempted by Cai & Sharma (2010).…”

Section: _______________________ * Corresponding Authormentioning

confidence: 99%

Machine Learning Approach for Kharif Rice Yield Prediction Integrating Multi-Temporal Vegetation Indices and Weather and Non-Weather Variables

Chandra

Mitra

Dubey³

et al. 2019

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

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The development of kharif rice yield prediction models was attempted through Machine Learning approaches such as Artificial Neural Network and Random Forest for the 42 blocks covering 13,141 sq km upland rainfed area of Purulia and Bankura district, West Bengal. Models were developed integrating monthly NDVI with weather and non-weather variables at block-level for the period 2006 to 2015. The model correlation obtained was 0.702 with MSE 0.01. Though the weather variables vs NDVI models are quite satisfactory, NDVI vs kharif rice yield models however, show relatively less correlation, about 0.6 revealing the requirement of varied additional farmer-controlled inputs. Development of NDVI vs crop yield models for different crop growth stages or fortnightly over a larger data set with selective adding of weather and non-weather variables to NDVI would be the most appropriate.This paper focus on developing kharif rice yield prediction models for rainfed upland area through machine learning techniques integrating block (sub-district) level NDVI with weather and non-weather variables.

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Section: _______________________ * Corresponding Authormentioning

confidence: 99%

Machine Learning Approach for Kharif Rice Yield Prediction Integrating Multi-Temporal Vegetation Indices and Weather and Non-Weather Variables

Chandra

Mitra

Dubey³

et al. 2019

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

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“…District level R&M crop yield was estimated using three different procedures: i) Agro-meteorological regression models (Singh et al, 2017), ii) Remote sensing index (VCI) based empirical models (Dubey et al, 2018) and iii) Semi physical Model (Tripathy et al, 2014;Chaurasiya et al, 2017). First approach was used by IMD (in collaboration with state agricultural universities), the second and third approaches were used by MNCFC.…”

Section: Yield Estimationmentioning

confidence: 99%

Evaluation of Pre-Harvest Production Forecasting of Mustard Crop in Major Producing States of India, Under Fasal Project

Latwal¹,

Saxena²,

Dubey³

et al. 2019

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

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Abstract. Rapeseed-mustard (Brassica spp.) is the major rabi oilseed crop of India. India is fourth largest contributor of oilseeds and Rapeseed-mustard contributing to around 11% of world’s total production and about 28.6% in total oilseeds production of the country. More than 85% Rapeseed-mustard production comes from 5 States viz. Rajasthan [48%], Haryana [12%], MP [10%], UP [9%] and West Bengal [7%]. In the previous few years, remote sensing technique has been progressively more considered for evolving as an alternative, standardized, possibly cheaper and faster technology for crop acreage estimation. Furthermore, satellite remote sensing data have strong advantages in comparison with other monitoring techniques because it provides timely, synoptic and latest information of crop at various stages over large scales. Therefore, under FASAL project, cloud free crop season’s images of different satellites (Sentinel-2, Resourcesat-2 and Landsat-8) were used and mustard crop was discriminated using Maximum Likelihood Classifier (MLC). Yield was estimated using different methods such as remote sensing derived NDVI, Agrometeorological yield model and Semi-Physical Model. The RMSE values for state level were found to be 4&ndash;17%, 8&ndash;19% and 13&ndash;23% for area, yield and production, respectively. The correlation coefficient (r) between DES and FASAL estimates were close to 0.9 in all the cases. The results of t-test at 5% level of significance inferred that FASAL and DES results were not significantly different. These results show that RS and weather-based techniques can be effectively used for pre-harvest acreage, yield and production estimation of mustard crop at district, state and national level.

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“…District level Rabi sorghum yield has been estimated using two procedures -i) Agro-meteorological regression models (Ghosh et al, 2014), ii) Remote sensing (NDVI, NDWI, VCI) based empirical models (Dubey et al, 2016).…”

Section: Yield Estimationmentioning

confidence: 99%

An Analysis of National-State-District Level Acreage and Production Estimates of Rabi Sorghum Under Fasal Project

Dolly¹,

Saxena²,

Dubey³

et al. 2019

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

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Sorghum (Sorghum vulgare Pers.) popularly known as jowar is the most important food and fodder crop of dry land agriculture. It is generally grown in both Kharif and Rabi season, with sown area of 25.3 and 38.3 lakh ha, respectively, at National level. The major states contributing to national sorghum acreage are Karnataka and Maharashtra. Crop acreage and yield estimation is of great significance to develop food policies and economic plans for a country. In this regard, under FASAL project, Sorghum production estimation is being carried out since 2013-14. Data from December to January is used for classification considering the peak season of crop. The satellite data used for classification has been gradually changed from Resourcesat 2 LISS III/ Landsat 8 OLI to Sentinel 2. Ground Truth has played major role to support the classification methodology. Total 1,409 GT points have been collected so far for both the states, with the help of state agriculture departments. NDVI based Crop Cutting Experiment (CCE) points are also generated and given to State for conducting the experiments at ground level. District level Sorghum crop yield is estimated using two procedures -i) Agro-meteorological regression models, ii) Remote sensing index based empirical models. The state and district level estimates were compared with government estimates. At state level the RMSE was found ranging from 6.5% to 34.7% and correlation (r) was found ranging from 071 to 0.87. At district level, the correlation between FASAL project estimates and government estimates ranged between 0.80 to 0.95. Overall, the paper discusses, the experience gained from the Rabi sorghum production estimation, in last 5 years, under FASAL project and the scope of improvement.

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Remote Sensing-Based Yield Forecasting for Sugarcane (Saccharum officinarum L.) Crop in India

Cited by 48 publications

References 20 publications

Machine Learning Approach for Kharif Rice Yield Prediction Integrating Multi-Temporal Vegetation Indices and Weather and Non-Weather Variables

Machine Learning Approach for Kharif Rice Yield Prediction Integrating Multi-Temporal Vegetation Indices and Weather and Non-Weather Variables

Evaluation of Pre-Harvest Production Forecasting of Mustard Crop in Major Producing States of India, Under Fasal Project

An Analysis of National-State-District Level Acreage and Production Estimates of Rabi Sorghum Under Fasal Project

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