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| Main Authors: | , , , |
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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15771574 |
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Table of Contents:
- <p>A <em>full image of a chili tree canopy</em> refers to a complete visual capture of the upper part of a chili plant, including its leaves, branches, and, if applicable, its fruits. This image is typically taken from a top-down or angled side view using a digital camera, drone, or automated imaging system in an agricultural setting. It provides a holistic view of the plant's canopy structure.</p> <h3><strong>Purpose and Use of the Image:</strong></h3> <ol> <li> <p><strong>Variety Identification:</strong></p> <ul> <li> <p>Different chili varieties have distinctive canopy structures, such as leaf shape, branch density, and overall architecture.</p> </li> <li> <p>Full canopy images are essential for visual-based classification systems to distinguish between varieties like Tanjung, Ciko, Branang, or Lingga.</p> </li> </ul> </li> <li> <p><strong>Plant Health Monitoring:</strong></p> <ul> <li> <p>The color, texture, and distribution of leaves in the canopy help detect plant stress, nutrient deficiencies, or early signs of disease.</p> </li> <li> <p>Irregularities or damaged areas in the canopy often indicate agronomic problems.</p> </li> </ul> </li> <li> <p><strong>Growth and Biomass Estimation:</strong></p> <ul> <li> <p>These images are used in plant growth models to calculate metrics like Leaf Area Index (LAI) or canopy volume.</p> </li> <li> <p>Useful for predicting yield and making fertilizer or irrigation decisions.</p> </li> </ul> </li> <li> <p><strong>AI Model Training:</strong></p> <ul> <li> <p>Full canopy images serve as the main input for training automated classification or detection models using techniques like CNNs, YOLO, EfficientNet, or transformer-based image encoders.</p> </li> </ul> </li> </ol> <p> </p>