<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Castor bean (<italic>Ricinus communis</italic>) indigenous to the southeastern Mediterranean basin, eastern Africa and India is a crop having various industrial and medicinal applications. It is helpful in crop rotation and replenishing the soil nutrients due to less water consumption. The current study explores the utility of Sentinel-2A satellite image for mapping and yield prediction of castor beans. Several classification methods viz. migrating means clustering, maximum likelihood classifier, support vector machine and artificial neural network are used for the classification and mapping of different landscape categories. The overall classification accuracy was achieved to be highest for artificial neural network (85.81 %) subsequently support vector machine (80.12 %), maximum likelihood classifier (74.23 %) and migrating means clustering (73.03 %). The yield prediction is performed using Sentinel-2A-derived indices namely Normalized Difference Vegetation Index and Enhanced Vegetation Index-2. Further, the cumulative values of these two indices are investigated for castor bean yield prediction using linear regression from July 2017 to April 2018 in different seasons (pre-monsoon, post-monsoon, and winter). The regression model provided (adj R2=0.75) value using EVI-2 compared to (adj R2=0.55) using NDVI for yield prediction of <italic>Ricinus communis</italic> crop in the winter season. The methodology adopted in this study can serve as an effective tool to map and predict the productivity of <italic>Ricinus communis</italic>. The adopted methodology may also be extended to a wider spatial level and for other significant crops grown in semi-arid regions of world.</p>
</abstract>

Castor bean (Ricinus communis) indigenous to the southeastern Mediterranean basin, eastern Africa and India is a crop having various industrial and medicinal applications. It is helpful in crop rotation and replenishing the soil nutrients due to less water consumption. The current study explores the utility of Sentinel-2A satellite image for mapping and yield prediction of castor beans. Several classification methods viz. migrating means clustering, maximum likelihood classifier, support vector machine and artificial neural network are used for the classification and mapping of different landscape categories. The overall classification accuracy was achieved to be highest for artificial neural network (85.81 %) subsequently support vector machine (80.12 %), maximum likelihood classifier (74.23 %) and migrating means clustering (73.03 %). The yield prediction is performed using Sentinel-2A-derived indices namely Normalized Difference Vegetation Index and Enhanced Vegetation Index-2. Further, the cumulative values of these two indices are investigated for castor bean yield prediction using linear regression from July 2017 to April 2018 in different seasons (pre-monsoon, post-monsoon, and winter). The regression model provided (adj R2=0.75) value using EVI-2 compared to (adj R2=0.55) using NDVI for yield prediction of Ricinus communis crop in the winter season. The methodology adopted in this study can serve as an effective tool to map and predict the productivity of Ricinus communis. The adopted methodology may also be extended to a wider spatial level and for other significant crops grown in semi-arid regions of world.

Mapping and yield prediction of castor bean (<italic>Ricinus communis</italic>) using Sentinel-2A satellite image in a semi-arid region of india

Haryana Space Applications Centre (HARSAC), Citizen Resources Information Department, Govt. of Haryana

Department of Environmental Science & Engineering

Guru Jambheshwar University of Science & Technology

Journal of Landscape Ecology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mapping and yield prediction of castor bean () using Sentinel-2A satellite image in a semi-arid region of india

Castor bean (Ricinus communis) indigenous to the southeastern Mediterranean basin, eastern Africa and India is a crop having various industrial and medicinal...

Mapping and yield prediction of castor bean (Ricinus communis) using Sentinel-2A satellite image in a semi-arid region of india

Online veröffentlicht: 06. Okt. 2023

Seitenbereich: 1 - 23

Eingereicht: 03. Mai 2023

Akzeptiert: 12. Juni 2023

DOI: https://doi.org/10.2478/jlecol-2023-0008

Schlüsselwörter
Castor bean, Sentine 2, Classification, Regression, Yield

© 2023 Ritesh Kumar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mapping and yield prediction of castor bean (Ricinus communis) using Sentinel-2A satellite image in a semi-arid region of india

Online veröffentlicht: 06. Okt. 2023

Seitenbereich: 1 - 23

Eingereicht: 03. Mai 2023

Akzeptiert: 12. Juni 2023

DOI: https://doi.org/10.2478/jlecol-2023-0008

SchlüsselwörterCastor bean, Sentine 2, Classification, Regression, Yield

© 2023 Ritesh Kumar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Castor bean, Sentine 2, Classification, Regression, Yield