Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/141236
|
| Title: | 以深度學習偵測植物病害之研究－以稻熱病為例
Plant Disease Detection Using Deep Learning – A Case Study of Rice Blast |
| Authors: | 曾信維
Tseng, Hsin-Wei |
| Contributors: | 詹進發
Jan, Jihn-Fa
曾信維
Tseng, Hsin-Wei |
| Keywords: | 稻熱病
影像辨識
深度學習
卷積神經網路
紅外線相機
Rice Blast
Image Recognition
Deep Learning
Convolutional Neural Network
Infrared-enabled Camera |
| Date: | 2022 |
| Issue Date: | 2022-08-01 18:24:48 (UTC+8) |
| Abstract: | 稻熱病作為水稻之主要病害之一，目前防治稻熱病之主要方法為抗病品種之栽培以及殺菌劑之施用，雖然前者具有較低之生產成本，且對於環境所造成之負擔也較低，然而過度依賴單一品種或是不當的施肥管理，將使其抗病性逐年下降。而殺菌劑之施用雖然較為有效，則需要注意施藥之劑量以及範圍，避免過度施藥所增加之生產成本及環境衝擊。
傳統在執行稻熱病害的偵測時多以人力進行地面調查，無論是時間上或是金錢上都需要花費大量成本，對於病徵判斷之精確度亦有限。隨著機器學習技術之發展，如深度學習這類具有高學習力及高辨識力之技術，可以提供使用者一次性進行大量資料的訓練，且其”end-to-end”之特性使資料不須進行預處理，可以節省大量時間成本，因此本研究欲使用深度學習之技術開發一套稻熱病病徵之影像辨識系統，先針對田野調查所拍攝之影像進行專家判釋，建立病害病徵之影像資料庫，並用其進行深度學習，建立稻熱病辨識之卷積神經網路模型。同時，本研究使用經紅外線改機之相機影像進行深度學習，探討此類資料對於稻熱病之判釋能力。
本研究根據所蒐集之資料，將影像分為健康、稻熱病及胡麻葉枯病三個分類，並將可見光及紅外光影像分別進行深度學習之模型訓練。模型進行遷移學習以提升訓練效率，並使用DenseNet121作為模型架構。模型完成訓練後進行測試資料之預測，並以混淆矩陣進行辨識成果的展示，同時計算相關指標以進行成果精度評估。結果顯示利用可見光影像進行訓練之分類模型精度可達0.98，紅外線影像之分類模型精度達0.94，可見光影像在病徵辨識上較有優勢，而透過紅外線改機所獲得之紅外線影像亦能取得不錯的辨識精度，顯示此類影像應用於病徵辨識之潛力。
Rice blast is the major disease of rice in Taiwan, the current main method to control rice blast are the application of blast-resistant cultivars and use of fungicide. Although the former method has lower cost and lower impact to the environment, over-producing single cultivar or improper fertilizing management might cause the disease resistance decreasing. On the other hand, though the application of fungicide is more effective, it has concern of the dosing amount and area, to avoid the growing producing cost and environmental impact.
Traditionally, diagnosis of rice blast is usually done by manual, which cost highly in either money or time, and the precision of the diagnosis is also limited. As the progression of machine learning, technique like deep learning that has high learning and recognizing ability can be supplied for training with large database, and its “end-to-end” characteristic can provide learning solution without data pre-processing which can highly speed up the procedure. As a result, this study used deep learning technique to develop a image recognition system for rice blast lesion, by manually interpreting the images from fieldwork through expert, and establish an image database of disease symptoms, then used it for deep learning training to develop a CNN model for rice blast recognition. Meanwhile, this study combined images from modified infrared-enabled camera for deep learning, the reliability of these data for rice blast recognition was tested.
The acquired data was divided into three classes including healthy, rice blast and brwonspot, and both visible light and near-infrared images were trained by a CNN model respectively. The training process was executed by transfer learning to reduce training cost, and a DensNet121 structure was implemented as the model structure. Examination of model performance was done with confusion matrix to display the recognition result, and related indices were calculated for futher estimation. The results show that the accuracy of visible light model is about 0.9812, and the accuracy of near-infrared model was about 0.9400. According to the result, the visible light image has better performance for rice blast lesion detection, and the near-infrared image obtained by modified camera has great potential for related utilization. |
| Reference: | 一、中文參考文獻
吳啟南、蕭國鑫、徐偉城、廖子毅、陳大科、劉治中，2002，「衛星及地面遙測資料應用於水稻生長及產量監測初步研究」，『農業試驗所特刊』，101：19-38。
周思儀、廖大經，2018，「2009至2014年台灣水稻新育成品種（系）對於稻熱病罹病反應之研究」，『台灣農業研究』，67(1):82-93。
陳文德，2000，「我國精準農業的發展方向與策略」，『水稻精準農業(耕)體系之研究』：7-14。
陳隆澤、陳一心、程永雄，2004，「1990 至 2002 年臺灣水稻品種 (系) 抗稻熱病檢定」， 『中華農業研究』，53：269~283。
黃竣吉，2003，「稻株含氮量多光譜影像遥測系統之硏究」，國立台灣大學生物產業機電工程學研究所碩士論文：台北。
詹鈞評、饒見有，2018，「無人機多鏡頭多光譜相機系統之穩健自適應波段套合法」，『航測及遙測學刊』，23(3) ：157-172。
鄒博堯，2017，「多光譜衛星影像之雲成分移除及水深反演」，交通大學土木工程系所碩士論文：新竹。
楊純明、林俊義，2002，「應用於水稻精準農業體系之知識與技術」，『農業試驗所特刊』，101：1-221。
楊純明、鄭清煥、張義璋、余志儒，2002，「利用植被光譜特徵辨識水稻遭受瘤野螟及稻熱病之危害」，『農業試驗所特刊』，101：1-18。
楊純明、林俊義，2003，「水稻精準農業體系之研究」，『農業試驗所特刊』，105：1-12。
楊明德、莊子毅、韓仁毓，2018，「結合光學與紅外線熱影像正射鑲嵌處理」，『Journal of Photogrammetry and Remote Sensing』, 23(2)：71-81。
楊志維、許明晃、黃文達、楊智旭、蔡養正、楊棋明、張新軒，2004， 「水稻營養生長期農藝性狀與衛星遙測植生指數 NDVI 之灰關聯分析」，『作物, 環境與生物資訊』，1(3)：199-206。
劉振榮、陳哲俊、林唐煌，2000，「遙測科技在精準農業之應用一遙測水稻種植分布之實例」，『水稻精準農業(耕)體系之研究』：79-92。
劉振榮、林唐煌、郭宗華、梁志綱、梁隆鑫，2002，「機載多頻譜遙測系統之建構與應用」，『應用於水稻精準農業體系之知識與技術』，『農業試驗所特刊』，101：51-64。
劉建慧、楊純明、劉振榮，2001「SPOT 衛星影像之大氣改正模式及水稻反射率反演精度之評估」，『中華農業氣象』，8：1-9。
蔡武雄，1976，「稻熱病在不同藥劑處理下之消長」，『中華農業研究』，25(3):199-205。
蔡武雄、黃杉芪，1990，「水稻葉稻熱病預測」，『技術服務』，4:4-7
蔡武雄，2009，「水稻稻熱病研究回顧」，『農業試驗所特刊』，138：1-12。
盧福明，2000，「精準農業體系之農機研發趨勢」，『水稻精準農業(耕)體系之研究』：105-110。
二、外文參考文獻
Baldi, P., & Sadowski, P. J., 2013, " Understanding dropout " , neural information processing systems, 26.
Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M. L., & Bareth, G., 2015, "Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley", International Journal of Applied Earth Observation and Geoinformation, 39: 79-87.
Chellapilla, K., Puri, S., & Simard, P., 2006, "High performance convolutional neural networks for document processing", International Workshop on Frontiers in Handwriting Recognition, La Baule ,France, October 23-26.
Chen, Z., Wang, X., & Liang, R., 2014), "RGB-NIR multispectral camera", Optics express, 22(5): 4985-4994.
Chien, C. C., 1974, "Studies on the physiologic races of rice blast fungus, Pyricularia oryzae Cav", Journal of Taiwan Agricultural Research, 23:16-37
Dadley-Moore, D., 2006, "Understanding rice blast disease", Nature Reviews Microbiology, 4(5): 323-323.
Dare, P. M., 2008, "Small format digital sensors for aerial imaging applications" Paper presneted at the 21st ,ISPRS Congress, Beijing, China, July 3-11.
Gnyp, M. L., Bareth, G., Li, F., Lenz-Wiedemann, V. I., Koppe, W., Miao, Y., Hennig, S. D., Jia, L., Laudien, R., Chen, X.& Zhang, F., 2014, "Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain", International Journal of Applied Earth Observation and Geoinformation, 33: 232-242.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q., 2017, "Densely connected convolutional networks" In Proceedings of the IEEE conference on computer vision and pattern recognition: 4700-4708.
Huang, Y., Thomson, S. J., Lan, Y., & Maas, S. J.,2010, "Multispectral imaging systems for airborne remote sensing to support agricultural production management", International Journal of Agricultural and Biological Engineering, 3(1): 50-62.
Huete, A. R., 1988, "A soil-adjusted vegetation index (SAVI)", Remote sensing of environment, 25(3): 295-309.
Ioffe, S., & Szegedy, C., 2015, " Batch normalization: Accelerating deep network training by reducing internal covariate shift.", Paper presneted at the 11th, International Conference on machine learning , Lille, France, July 7-9.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P., 1998, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, 86(11): 2278-2324.
Liang, W. J., Zhang, H., Zhang, G. F., & Cao, H. X., 2019, "Rice blast disease recognition using a deep convolutional neural network", Scientific reports: 9(1), 1-10.
Lin, M., Chen, Q., & Yan, S., 2013, " Network in network" ,arXiv, 1312:4400.
Lu, Y., Yi, S., Zeng, N., Liu, Y., & Zhang, Y., 2017, "Identification of rice diseases using deep convolutional neural networks", Neurocomputing, 267:378-384.
Ma, J., Du, K., Zheng, F., Zhang, L., Gong, Z., & Sun, Z., 2018, "A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network", Computers and electronics in agriculture, 154: 18-24.
Peñuelas, J., Gamon, J. A., Griffin, K. L., & Field, C. B., 1993, "Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance", Remote Sensing of Environment, 46(2): 110-118.
Petach, A. R., Toomey, M., Aubrecht, D. M., & Richardson, A. D., 2014, "Monitoring vegetation phenology using an infrared-enabled security camera", Agricultural and forest meteorology, 195: 143-151.
Phadikar, S., & Sil, J., 2008, "Rice disease identification using pattern recognition techniques", Paper presneted at the 11th, International Conference on Computer and Information Technology , Khulna, Bangladesh, December 24-27.
Qiu, J., Lu, X., Wang, X., & Hu, X. (2021, March). Research on Rice Disease Identification Model Based on Migration Learning in VGG Network. In IOP Conference Series: Earth and Environmental Science (Vol. 680, No. 1, p. 012087). IOP Publishing.Robert, P. C.,1997, "Remote sensing- A potentially powerful technique for precision agriculture" Land satellite information in the next decade II: Sources and applications.
Rabatel, G., Gorretta, N., & Labbe, S., 2014,"Getting simultaneous red and near-infrared band data from a single digital camera for plant monitoring applications: Theoretical and practical study", Biosystems Engineering, 117: 2-14.
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W., 1974. "Monitoring vegetation systems in the Great Plains with ERTS", NASA special publication, 351(1974): 309.
Shibayama, M., Sakamoto, T., Takada, E., Inoue, A., Morita, K., Takahashi, W., & Kimura, A., 2011, "Estimating paddy rice leaf area index with fixed point continuous observation of near infrared reflectance using a calibrated digital camera", Plant Production Science, 14(1): 30-46.
Sonnentag, O., Hufkens, K., Teshera-Sterne, C., Young, A. M., Friedl, M., Braswell, B. H., Milliman, T., O`keefe , J., Richardson, A. D., 2012, "Digital repeat photography for phenological research in forest ecosystems". Agricultural and Forest Meteorology, 152: 159-177.
Tucker, C. J.,1979, "Red and photographic infrared linear combinations for monitoring vegetation", Remote sensing of Environment, 8(2):127-150.
Waheed, A., Goyal, M., Gupta, D., Khanna, A., Hassanien, A. E., & Pandey, H. M., 2020, "An optimized dense convolutional neural network model for disease recognition and classification in corn leaf", Computers and Electronics in Agriculture, 175, 105456.
三、網頁參考文獻
Canon (2021). EOS 600D / EOS 800D on the World Wide Web： https://tw.canon/zh_TW/consumer
Dell (2021). Aurora R7 on the World Wide Web： https://tw.canon/zh_TW/consumer
Micasense (2021). RedEdge MX on the World Wide Web：https://micasense.com/
NVIDIA (2021). Geforce GTX 1080 on the World Wide Web： https://www.nvidia.com/zh-tw/
Tetracam(2021). Agricultural Digital Camera on the World Wide Web：https://www.tetracam.com/ |
| Description: | 碩士
國立政治大學
地政學系
109257032 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109257032 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202201011 |
| Appears in Collections: | [地政學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 703201.pdf | | 4479Kb | Adobe PDF2 | 10 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
