Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models
Abstract Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different levels of wilt disease on the biophysica...
Saved in:
| Main Authors: | RN Singh, P. Krishnan, C. Bharadwaj, Sonam Sah, B. Das |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87134-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predicting yellow mosaic disease severity in yardlong bean using visible imaging coupled with machine learning model
by: Abhishek Kumar Dubey, et al.
Published: (2025-07-01) -
Screening for psychotherapy readiness with the University of Rhode Island Change Assessment Scale and the Readiness for Psychotherapy Index
by: Oliver Rumle Hovmand, et al.
Published: (2025-08-01) -
Comparison of contemporaneous Sentinel-2 and EnMAP data for vegetation index-based estimation of leaf area index and canopy closure of a boreal forest
by: Jussi Juola, et al.
Published: (2024-12-01) -
Finding RB/Rpi-blb1/Rpi-sto1-like sequences in conventionally bred potato varieties
by: O. Y. Antonova, et al.
Published: (2018-09-01) -
Retrieving the Leaf Area Index of Dense and Highly Clumped Moso Bamboo Canopies from Sentinel-2 MSI Data
by: Weiliang Fan, et al.
Published: (2025-05-01)