# Modification of the YOLOv8 Model for Inference of Disease Detection and Nutritional Deficiency in Corn Plants on Edge Computing Devices
> Yusuf I.

URL kanonis: https://discover.unhas.ac.id/publications/modification-of-the-yolov8-model-for-inference-of-disease-detection-and-nutritio
Jurnal / Konferensi: 2025 12th International Conference on Information Technology Computer and Electrical Engineering Icitacee 2025
Tahun terbit: 2025
DOI: https://doi.org/10.1109/ICITACEE66165.2025.11232890
Citations: 1

## Authors
- Yusuf I.

## Abstract
Early detection of diseases and nutritional deficiencies in plants is crucial in supporting precision agriculture, particularly in corn cultivation. This study proposes modifying the YOLOv8 architecture with patch-based image segmentation techniques to improve edge computing devices' detection accuracy and inference efficiency. Four model configurations were evaluated: YOLOv8 baseline, YOLOv8 with patches, modified YOLOv8, and the proposed model, which is modified YOLOv8 with patches. Experimental results show that the proposed model achieves the best detection performance, with a mean mAP@0.5 (mAP@0.5) of 87%, precision of 0.98, recall of 0.77, and the highest F1-Score of 0.89. Additionally, this model demonstrates superior inference efficiency, with an execution time of 1.9 ms/image on an NVIDIA T4 GPU, 21 seconds/image on a Raspberry Pi, and 121.1 ms/image on a Jetson Nano. Memory consumption is also the lowest, at 308.62 MB on Raspberry Pi and 278.82 MB on Jetson Nano. These results indicate that modifying YOLOv8 with patch-based preprocessing improves detection accuracy and enables practical deployment on edge devices with limited computational resources. This makes the model highly suitable for real-time agricultural monitoring applications in resourceconstrained environments.

## Keywords
- Inference
- Preprocessor
- Computer science
- Edge detection
- Enhanced Data Rates for GSM Evolution
- Artificial intelligence
- Segmentation
- Image processing
- Precision and recall
- Inference system
- Raspberry pi
- Edge computing
- Image segmentation
- Machine learning
- Fuzzy inference
- Pattern recognition (psychology)
- Data mining
- Software deployment
- Fuzzy inference system
- Edge device
- Computer vision
- Benchmark (surveying)
- Randomness

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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