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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Vydané v:	Evolving systems Ročník 16; číslo 3; s. 108
Hlavní autori:	Subeesh, A., Chauhan, Naveen, Chandel, Narendra Singh, Rajwade, Yogesh
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2025 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
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