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
-
Effect of dew-irrigation on seed yield and physiological traits in chickpea
by: Saiede Sargol Hosseini, et al.
Published: (2024-12-01) -
Multi-Genotype Rice Yield Prediction Based on Time-Series Remote Sensing Images and Dynamic Process Clustering
by: Qian Li, et al.
Published: (2024-12-01) -
Lais bretons (xiie-xiiie siècles). Marie de France et ses contemporains, Paris, Honoré Champion, 2011
by: Anna Gęsicka
Published: (2019-01-01) -
Finding RB/Rpi-blb1/Rpi-sto1-like sequences in conventionally bred potato varieties
by: O. Y. Antonova, et al.
Published: (2018-09-01) -
A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (<italic>Phyllostachys pubescens</italic>) Forests From Sentinel-2 MSI Data
by: Zhanghua Xu, et al.
Published: (2025-01-01)