ArticlePeerJ. Computer science2023
Drought stress detection technique for wheat crop using machine learning.
Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- An explainable vision transformer with transfer learning based efficient drought stress identification.Plant molecular biology · 2025Article
- A comprehensive review of crop stress detection: destructive, non-destructive, and ML-based approaches.Frontiers in plant science · 2025Review
- Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation.PloS one · 2024Article
- Explainable light-weight deep learning pipeline for improved drought stress identification.Frontiers in plant science · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also validated for the most accurate segmentation using intersection over union metric. For automated water stress detection, rapid prototyping of machine learning models revealed that there is a need only to explore nine models. After extensive grid search-based hyper-parameter tuning of machine learning algorithms and 10 K fold cross validation it was found that out of nine different machine algorithms tested, the random forest algorithm has the highest global diagnostic accuracy of 91.164% and is the most suitable for constructing water stress detection models.
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