2023-08-07
【學術亮點-頂級期刊論文】利用深度學習與無人機影像監測單株青花菜生長
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【學術亮點-頂級期刊論文】利用深度學習與無人機影像監測單株青花菜生長
Intelligent Cultivation: Crop digital twin for growth adversity index and disaster damage assessment【Department of Civil Engineering / Ming-Der Yang / Tenured Distinguished Professor】
智慧栽培：數位分身作物生長逆境指標與災損監測【土木工程學系楊明德終身特聘教授】
上架日期2023-03-06
Intelligent Cultivation: Crop digital twin for growth adversity index and disaster damage assessment【Department of Civil Engineering / Ming-Der Yang / Tenured Distinguished Professor】
智慧栽培：數位分身作物生長逆境指標與災損監測【土木工程學系楊明德終身特聘教授】
| 論文篇名 | 英文：Single-plant broccoli growth monitoring using deep learning with UAV imagery 中文：利用深度學習與無人機影像監測單株青花菜生長 |
| 期刊名稱 | Computers and Electronics in Agriculture (指標清單期刊) |
| 發表年份, 卷數, 起迄頁數 | 2023, 207, 107739 |
| 作者 | Lee, Cheng-Ju; Yang, Ming-Der(楊明德)*; Tseng, Hsin-Hung; Hsu, Yu-Chun; Sung, Yu; Chen, Wei-Ling |
| DOI | 10.1016/j.compag.2023.107739 |
| 中文摘要 | 本研究利用可見光及多光譜無人機影像與兩種深度學習方法，協助青花菜栽培之精準田間監測與管理。基於單株生長影像，可監測其生長情形，如灌溉及施肥不均、單株凋零等。所提出的方法可用於確定最佳肥料用量，觀察青花菜大小以確定最佳收穫時機。預期此方法可應用於其他作物的監測及管理，以提高精準農業的效率。 |
| 英文摘要 | Single-plant growth monitoring aids precision agricultural decision-making to reduce the costs related to pesticides, fertilizers, and labor. This study integrated visible/multi-spectral UAV imagery with two deep learning methods, object detection and semantic segmentation, to obtain a visualized map that could assist in precise field monitoring and management for broccoli cultivation. For plant detection, feature extraction was conducted using multiscale dilated convolution, which enabled the effective detection of broccoli in images taken under different photographic conditions and resolutions. Two crops of broccoli (cultivar: Broccoli No. 42) were planted in 2020 at Taichung Agricultural Research and Extension Station, in which the first crop was treated as the training data. The detection of individual broccoli plants was processed using a feature extraction architecture of the AlexNet-Like backend at the SSD frontend, where the input scale of the detector complies with the original SSD architecture. For the model test on the second crop, the recall and precision were 98.58% and 99.73%, respectively, after histogram matching based on the first crop images. Moreover, the proposed approach was applied to a real farming field to verify its robustness across different conditions, and achieved a recall of 61.13% using dilated convolution. This study also generated a visualized growth map on a single-plant basis, which allows operators to detect growth situations, such as uneven irrigation or fertilization and necrosis and apoptosis, to greatly enhance the viability of precision agriculture in the calculation of unit yield and intragroup differences for a regime. The proposed approach can be used to determine the optimal amount of fertilization and observe the size of broccoli heads to determine the optimal harvest time. Expectedly, the method may also be applied to the monitoring and management of other crops to improve the efficiency and reduce the labor demand for precision agriculture. |
| 發表成果與AI計畫研究主題相關性 | 以AI影像物件之類神經網路辨識青花菜並推論其生長狀態 |
