利用葉片影像與卷積神經網路辨識殼斗科與樟科之物種

Identifying Fagaceae and Lauraceae species using leaf images and convolutional neural networks

吳璨妤(Tsan-Yu Wu)

指導教授 ： 郭彥甫

國立臺灣大學/生物資源暨農學院/生物機電工程學系/碩士(2020年)

https://doi.org/10.6342/NTU202003782

摘要

森林是陸域生態系重要的一環，不但孕育豐富的動植物，更能提供木材、調節氣候，對人類社會與環境帶來諸多效益。為了更有效率應用及管理這些資源，準確的物種辨識是不可或缺的步驟。殼斗科與樟科是台灣中低海拔的優勢樹種，因為台灣豐富的地形與氣候變化分化出許多台灣特有種。現今的植物物種辨識依靠比對遺傳標記的相似性，然而該方法昂貴並耗時。近年來卷積神經網路 (convolutional neural networks, CNNs) 廣泛被應用於各種複雜的機器視覺任務。因此本研究利用深度卷積神經網路自動辨識35種殼斗科與樟科物種，其中葉片影像由彩色平板掃描機所取得。本研究之自動辨識模型使用三種不同的深度卷積神經網路架構，包含：DenseNet-121、MobileNet V2與Xception。研究中卷積神經網路模型之準確率最高可達99.396%，而在圖像顯示卡 (Graphics Processing Unit, GPU) 最快之辨識速度可達17.1毫秒∕影像。

關鍵字

卷積神經網路 ； 深度學習 ； 殼斗科 ； 樟科 ； 植物物種辨識

並列摘要

Forests contain abundant resources and provide benefits to human societies, such as edible fruits, medicinal substances, and woods for construction. Species identification is an essential step for the management and utilization of the resources. Nowadays, the species identification relies on the examining the similarity of genetic markers. The approach is, however, time consuming, laborious, and costly. Fagaceae and Lauraceae are two woody plant families with high species richness that dominate the low and middle altitude regions in Taiwan. This paper proposed a machine vision approach for identifying the species of families Fagaceae and Lauraceae using leaf images. Leaf specimens of 35 Fagaceae and Lauraceae species were collected. The images of the leaves were acquired using flatbed scanners. Deep convolutional neural networks (DCNN) of three architectures, namely DenseNet-121, MobileNet V2, and Xception, were next trained to identify the species. Among all, Xception reached the highest mean accuracy of 99.396%, and MobileNetV2 required the least mean test time of 17.1 ms per image using an GPU.

並列關鍵字

Convolutional neural network ； Deep learning ； Fagaceae ； Lauraceae ； Plant identification

參考文獻

Anwar, S., Hwang, K., Sung, W. (2017). Structured pruning of deep convolutional neural networks. ACM Journal on Emerging Technologies in Computing Systems (JETC), 13(3), 1-18.

Backes, A. R., Casanova, D., Bruno, O. M. (2009). Plant leaf identification based on volumetric fractal dimension. International Journal of Pattern Recognition and Artificial Intelligence, 23(06), 1145-1160.

Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258).

De Boer, P. T., Kroese, D. P., Mannor, S., Rubinstein, R. Y. (2005). A tutorial on the cross-entropy method. Annals of operations research, 134(1), 19-67.

Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297-302.