AI-powered detection and quantification of Post-harvest Physiological Deterioration (PPD) in cassava using YOLO foundation models and K-means clustering
Post-harvest physiological deterioration (PPD) poses a significant challenge to the cassava industry, leading to substantial economic losses. This study aims to address this issue by developing a comprehensive framework in collaboration with cassava breeders. Advanced deep learning (DL) techniques s...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
BioMed Central
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/162791 |
Similar Items: AI-powered detection and quantification of Post-harvest Physiological Deterioration (PPD) in cassava using YOLO foundation models and K-means clustering
- Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models
- A review of global cassava (Manihot esculenta Crantz) production trends, post-harvest physiological deterioration (PPD) challenge, and control strategies
- Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI
- Effectiveness of cassava stem pruning for inducing delay in postharvest physiological deterioration (PPD) of fresh roots
- Mapping wound-response genes involved in post-harvest physiological deterioration (PPD) of cassava (Manihot esculenta Crantz)
- Training Report: South-South collaboration for strengthening capacities in assessing the postharvest physiological deterioration (PPD) of fresh cassava roots and technologies for shelf-life extension.