Multimodal sequential cross-modal transformer for predicting plant available water capacity (PAWC) from time series of weather and crop biological data

Multimodal sequential cross-modal transformer for predicting plant available water capacity (PAWC) from time series of weather and crop biological data

Deep learning (DL) and machine learning (ML) have been applied widely to satellite data of vegetation indices to infer indirect features associated with soil characteristics that affect crop performance in rain-fed environments. In this paper, we propose a DL model for prediction of plant available...

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Bibliographic Details
Main Authors: Dung Nguyen, Peter de Voil, Andries Potgieter, Yash P. Dang, Thomas G. Orton, Duc Thanh Nguyen, Thanh Thi Nguyen, Scott C. Chapman
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:Agricultural Water Management
Subjects:
Cross-modal attention
Transformer
Machine learning
APSIM
Soil
Plant
Online Access:http://www.sciencedirect.com/science/article/pii/S0378377424004608
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