# Rice Growth Phase Detection in Rice Field Plots Using Drone Imagery
> Astridevi

URL kanonis: https://discover.unhas.ac.id/publications/rice-growth-phase-detection-in-rice-field-plots-using-drone-imagery
Jurnal / Konferensi: 19th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2024
Tahun terbit: 2024
DOI: https://doi.org/10.1109/iSAI-NLP64410.2024.10799305
Citations: 2

## Authors
- Astridevi

## Abstract
This research aims to detect the growth phase of rice in paddy fields using drone images and the Mask R-CNN method. This algorithm excels in object detection and segmentation at the pixel level, thus enabling more efficient monitoring of rice growth compared to conventional methods. The growth phases detected include water, vegetative, reproductive, ripening, and bera phases. High-resolution drone imagery was used to identify growth phases in rice paddy plots. The evaluation results showed high performance in the vegetative phase with a Precision of 1.00, Recall of 0.77, and F1Score 0.87 and bera phase by achieving a Precision of 0.73, Recall of 0.89, and F1-Score 0.80, which was used to evaluate the accuracy of the model. The average mAP for the bounding box was 62.17, and the average mAP for segmentation was 65.84. This research shows the great potential of using drone imagery and deep learning in the automatic monitoring of rice growth phases.

## Keywords
- Drone
- Paddy field
- Aerial imagery
- Field (mathematics)
- Computer science
- Phase (matter)
- Remote sensing
- Artificial intelligence
- Computer vision
- Environmental science
- Agricultural engineering
- Agronomy
- Engineering
- Geography
- Mathematics
- Chemistry
- Biology
- Botany
- Pure mathematics
- Organic chemistry

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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