MACHINE LEARNING APPROACH FOR KHARIF RICE YIELD PREDICTION INTEGRATING MULTI-TEMPORAL VEGETATION INDICES AND WEATHER AND NON-WEATHER VARIABLES

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 mon...
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Autor*in:	A. Chandra [verfasserIn] P. Mitra [verfasserIn] S. K. Dubey [verfasserIn] S. S. Ray [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Übergeordnetes Werk:	In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - Copernicus Publications, 2015, (2019), Seite 187-194
Übergeordnetes Werk:	year:2019 ; pages:187-194

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.5194/isprs-archives-XLII-3-W6-187-2019

Katalog-ID:	DOAJ046148612

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spellingShingle	A. Chandra misc TA1-2040 misc TA1501-1820 misc Technology misc T misc Engineering (General). Civil engineering (General) misc Applied optics. Photonics MACHINE LEARNING APPROACH FOR KHARIF RICE YIELD PREDICTION INTEGRATING MULTI-TEMPORAL VEGETATION INDICES AND WEATHER AND NON-WEATHER VARIABLES
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topic_title	TA1-2040 TA1501-1820 MACHINE LEARNING APPROACH FOR KHARIF RICE YIELD PREDICTION INTEGRATING MULTI-TEMPORAL VEGETATION INDICES AND WEATHER AND NON-WEATHER VARIABLES
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title	MACHINE LEARNING APPROACH FOR KHARIF RICE YIELD PREDICTION INTEGRATING MULTI-TEMPORAL VEGETATION INDICES AND WEATHER AND NON-WEATHER VARIABLES
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title_full	MACHINE LEARNING APPROACH FOR KHARIF RICE YIELD PREDICTION INTEGRATING MULTI-TEMPORAL VEGETATION INDICES AND WEATHER AND NON-WEATHER VARIABLES
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title_sort	machine learning approach for kharif rice yield prediction integrating multi-temporal vegetation indices and weather and non-weather variables
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title_auth	MACHINE LEARNING APPROACH FOR KHARIF RICE YIELD PREDICTION INTEGRATING MULTI-TEMPORAL VEGETATION INDICES AND WEATHER AND NON-WEATHER VARIABLES
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	MACHINE LEARNING APPROACH FOR KHARIF RICE YIELD PREDICTION INTEGRATING MULTI-TEMPORAL VEGETATION INDICES AND WEATHER AND NON-WEATHER VARIABLES
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up_date	2024-07-03T19:02:58.189Z
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