Rice Seedling Detection in UAV Images Using Transfer Learning and Machine Learning

Remote Sensing

卷:14

Dong-Hong Wu¹  Yu-Chun Hsu²  Hsin-Hung Tseng²  Ming-Der Yang²  R. Saminathan³  Chin-Ying Yang⁴ 

[1] Crop Science Division, Taiwan Agricultural Research Institute, Taichung 413, Taiwan;

[2] Department of Civil Engineering, National Chung Hsing University, Taichung 402, Taiwan;

[3] Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 402, Taiwan;

[4] Smart Multidisciplinary Agriculture Research and Technology Center, National Chung Hsing University, Taichung 402, Taiwan;

关键词: UAV;    machine learning;    deep learning;    object detection;    EfficientDet;    Faster R-CNN;   

DOI  :  10.3390/rs14122837

来源: DOAJ

【 摘 要 】

To meet demand for agriculture products, researchers have recently focused on precision agriculture to increase crop production with less input. Crop detection based on computer vision with unmanned aerial vehicle (UAV)-acquired images plays a vital role in precision agriculture. In recent years, machine learning has been successfully applied in image processing for classification, detection and segmentation. Accordingly, the aim of this study is to detect rice seedlings in paddy fields using transfer learning from two machine learning models, EfficientDet-D0 and Faster R-CNN, and to compare the results to the legacy approach—histograms of oriented gradients (HOG)-based support vector machine (SVM) classification. This study relies on a significant UAV image dataset to build a model to detect tiny rice seedlings. The HOG-SVM classifier was trained and achieved an F1-score of 99% in both training and testing. The performance of HOG-SVM, EfficientDet and Faster R-CNN models, respectively, were measured in mean average precision (mAP), with 70.0%, 95.5% and almost 100% in training and 70.2%, 83.2% and 88.8% in testing, and mean Intersection-over-Union (mIoU), with 46.5%, 67.6% and 99.6% in training and 46.6%, 57.5% and 63.7% in testing. The three models were also measured with three additional datasets acquired on different dates to evaluate model applicability with various imaging conditions. The results demonstrate that both CNN-based models outperform HOG-SVM, with a 10% higher mAP and mIoU. Further, computation speed is at least 1000 times faster than that of HOG-SVM with sliding window. Overall, the adoption of transfer learning allows for rapid establishment of object detection applications with promising performance.

【 授权许可】

Unknown