
Bine Rithika (Lead Researcher): Timely detection of crop stress is vital for global food security. This study introduces an integrated Digital Phenotyping (DP) and Material Science framework to enhance early pest/disease detection and bio-pesticide efficacy in the Manajipet agricultural fields. We employed a Deep Learning model (YOLOv7) on multispectral imagery, achieving a high mean Average Precision (93.5%) for stress identification.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
