Deep autoencoder-driven feature learning and meta-heuristic optimized machine learning modelling for crop water stress identification

Accurate identification of water stress in crops is essential for maintaining sustainable, high-quality crop production and minimizing the risk of severe crop losses. The erroneous and subjective nature of human expert-based crop stress identification can be effectively replaced by machine learning...

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Bibliographic Details
Published in:	Evolving systems Vol. 16; no. 3; p. 108
Main Authors:	Subeesh, A., Chauhan, Naveen, Chandel, Narendra Singh, Rajwade, Yogesh
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2025 Springer Nature B.V
Subjects:	Accuracy Agricultural production Applications programs Artificial Intelligence Climate change Complex Systems Complexity Crop production Data analysis Data processing Decision support systems Deep learning Engineering Heuristic Heuristic methods Identification Machine learning Mobile computing Optimization techniques Original Paper Performance enhancement Physiology Prediction models Voting Water Deep learning Precision agriculture Ensemble model Autoencoders Abiotic stress
ISSN:	1868-6478, 1868-6486
Online Access:	Get full text
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