カスタムデータセットと機械学習によるラブラドール豆のハダニ検出
Detection of Spider Mites on Labrador Beans through Machine Learning Approaches Using Custom Datasets
RGBと近赤外(NIR)を組み合わせたRGBNデータセットを独自に構築し、YOLOv8とCNNを組み合わせた2段階モデルで植物の初期病害(ハダニ)を検出、RGBのみより精度が向上することを示した研究。
著者: Violet Liu, Jason Chen, Ans Qureshi, Mahla Nejati
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Amidst growing food production demands, early plant disease detection is essential to safeguard crops; this study proposes a visual machine learning approach for plant disease detection, harnessing RGB and NIR data collected in real-world conditions through a JAI FS-1600D-10GE camera to build an RGBN dataset. A two-stage early plant disease detection model with YOLOv8 and a sequential CNN was used to train on a dataset with partial labels, which showed a 3.6% increase in mAP compared to a single-stage end-to-end segmentation model. The sequential CNN model achieved 90.62% validation accuracy utilising RGBN data. An average of 6.25% validation accuracy increase is found using RGBN in classification compared to RGB using ResNet15 and the sequential CNN models. Further research and dataset improvements are needed to meet food production demands.
