An Explainable CNN-Transformer Hybrid Framework for Region-Based Crop Yield Prediction Using Structured Agricultural Data

Name: An Explainable CNN-Transformer Hybrid Framework for Region-Based Crop Yield Prediction Using Structured Agricultural Data
Creator: Ramesh V
Published: 2026-06-16T23:54:51.913Z
Keywords: Crop Yield, Agriculture

V, Ramesh; P, Kumaresan

doi:10.17632/tbyxbrhr5b.1

An Explainable CNN-Transformer Hybrid Framework for Region-Based Crop Yield Prediction Using Structured Agricultural Data

Published: 16 June 2026| Version 1 | DOI: 10.17632/tbyxbrhr5b.1

Contributors:

Ramesh V, Kumaresan P

Description

This dataset uses a large-scale structured agricultural dataset comprising one million records (1,000,000 × 10 attributes). The dataset is for four agricultural administrative regions (north, south, east and west), six crop types (cotton, rice, barley, soybean, wheat, maize), and six soil types (sandy, clay, loam, silt, peaty, and chalky).

An Explainable CNN-Transformer Hybrid Framework for Region-Based Crop Yield Prediction Using Structured Agricultural Data

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