Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

Gumma, M K and Nukala, R M and Panjala, P and Bellam, P K and Gajjala, S and Dubey, S K and Sehgal, V K and Mohammed, I and Deevi, K C (2024) Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms. AgriEngineering, 6 (1). pp. 786-802. ISSN 2624-7402

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Abstract

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

Item Type:	Article
Divisions:	Global Research Program - Resilient Farm and Food Systems
CRP:	UNSPECIFIED
Uncontrolled Keywords:	crop yield, DSSAT, ML algorithms
Subjects:	Others > GIS Techniques/Remote Sensing Others > Crop Yield
Depositing User:	Mr Nagaraju T
Date Deposited:	07 Jun 2024 10:30
Last Modified:	07 Jun 2024 10:30
URI:	http://oar.icrisat.org/id/eprint/12703
Official URL:	https://www.mdpi.com/2624-7402/6/1/45
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	UNSPECIFIED
Links:	Google Scholar

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