進階搜尋


下載電子全文  
系統識別號 U0026-1002202020195000
論文名稱(中文) 使用類神經網路以光學衛星影像推估稻米產量
論文名稱(英文) Rice Grain Yield Estimation using Artificial Neural Network with Optical Satellite Imagery
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 108
學期 1
出版年 109
研究生(中文) 娜莎瓦
研究生(英文) Salwa Nabilah
學號 P66077034
學位類別 碩士
語文別 英文
論文頁數 63頁
口試委員 指導教授-林昭宏
口試委員-郭佩棻
口試委員-徐逸祥
中文關鍵字 水稻產量估算  類神經網絡  SPOT-7衛星影像 
英文關鍵字 Rice yield estimation  artificial neural network  SPOT-7 imagery 
學科別分類
中文摘要 對於作物管理、食品安全、作物交易與相關政策而言,預估稻米單位產量實屬重要。傳統方式為至現場檢查作物生長狀況並評估其品質以及產量,但如此方法不僅耗時且成本高;為解決傳統方法的缺點,現今方法已改使用光學衛星影像,其提供了豐富的光譜信息,再將其信息轉換成植生指標,作為迴歸模型的輸入來源並預測作物產量。然而,植生指標無法充分描述圖像特徵且植生指標與稻米產量之間關係為非線性,此會導致稻米單位產量的預估結果不理想,因此本研究提出使用灰度共生矩陣 (Grey-Level Co-occurrence Matrix ,GLCM)與植生指標相互結合來豐富圖像特徵信息,並使用人工神經網絡 (Artificial Neural Network ,ANN)來計算圖像特徵信息與稻米單量產量的非線性關係,另外,灰度共生矩陣亦可提取圖像中水稻的形狀、邊緣、粗糙性等紋理特徵信息。在人工神經網絡的設計上,本研究將其區分為三層:輸入層、單一或多個隱藏層以及輸出層;輸入層中包含了47個神經元,分別為4個原始影像波段、11個植生指標特徵和32個灰度共生矩陣特徵。為尋找估計水稻產量的最佳模型,本研究對ANN模型中的隱藏層層數和相對應的神經元數量進行調整且相互比較。本研究使用了兩張分別於不同年份拍攝但影像位置相近的SPOT-7衛星影像進行交叉驗證,而由於樣品數量上的限制,採用10折交叉驗證來評估本研究提出的模型的好壞。本研究的實驗區域位於臺灣彰化縣二林鄉。根據均方根誤差 (Root Mean Square Error ,RMSE)的定量評估準確度(Quantitative Accuracy Assessment)得以證明本研究提出之模型的可行性與性能。實驗結果顯示於所有模型中,單一隱藏層且包含47個神經元的模型擁有最低的均方根誤差值0.6128,為最適模型。
英文摘要 Estimation of rice yield is important for crop management, food security, trade, and policy. Visiting rice fields on a daily basis to check the quality of rice grain is time-consuming and labor sensitivity. Instead, many researchers utilized vegetation indices (VIs) from optical satellite images with a regression model to predict rice yield. However, the VIs are insufficient to delineate the features in the image and the relationship between indices and rice grain yield is generally nonlinear and complex, which may result in an un-optimal estimation. In this study, texture features extracted by Grey-Level Co-occurrence Matrix (GLCM) are utilized in addition to VIs, and Artificial Neural Network (ANN) is adopted to model the nonlinear relationship between extracted image features and the rice yields. GLCM is able to extract texture features that represent shape, edge, and roughness of the paddy rice in the image. The design of this ANN model contains input, single or multiple hidden, and output layers. The input layer consists of 47 neurons with 4 original bands, 11 VIs, and 32 GLCM features. Furthermore, the best model of estimating the rice yield can be produced by adjusting and comparing the number of hidden layers and corresponding neurons in the ANN models. Two SPOT-7 images acquired in two different days but similar stage of rice growing were used, and a 10-fold cross validation was performed to evaluate the proposed model because of the limited in-situ samples. The study area is Erlin Township, Changhua County, Taiwan that contains many rice fields. Quantitative accuracy assessments were conducted to demonstrate the feasibility and performance of the proposed model, in terms of Root Mean Square Error (RMSE). The experiments results show that the best ANN model of all models that have been tried is the ANN model that uses one hidden layer with 47 neurons. It has a RMSE value of 0.6128 tons.
論文目次 CONTENTS
ABSTRACT i
CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES ix
Chapter 1 Introduction 1
Chapter 2 Background 8
2.1 Image Features 8
2.1.1 Vegetation Indices 9
2.1.2 Gray-Level Co-occurrence Matrix (GLCM) 12
2.2 Machine Learning 16
2.2.1 Stepwise Regression 17
2.2.2 Artificial Neural Network (ANN) 18
Chapter 3 Data and Methodology 22
3.1 Study Area and Data 22
3.2 Pre-Processing 26
3.3 Developing the Yield Estimation Model 33
Chapter 4 Experimental Results and Discussion 37
4.1 Rice yield prediction using ANN 37
4.2 Comparison with Stepwise Regression 46
4.3 Discussion 49
Chapter 5 Conclusions 53
References 55

參考文獻 References
Abrahamsen, E., Brastein, O.M., Lie, B., 2018. Machine Learning in Python for Weather Forecast based on Freely Available Weather Data. Proc. 59th Conf. imulation Model. (SIMS 59), 26-28 Sept. 2018, Oslo Metrop. Univ. Norw. 153, 169–176. https://doi.org/10.3384/ecp18153169
Akhand, K., Nizamuddin, M., Roytman, L., 2018. An Artificial Neural Network-Based Model for Predicting Boro Rice Yield in Bangladesh Using AVHRR-Based Satellite Data. Int. J. Agric. For. 8, 16–25. https://doi.org/10.5923/j.ijaf.20180801.04
Al-Kilidar, S.H.S., George, L.E., 2017. Texture recognition using co-occurrence matrix features and neural network. J. Theor. Appl. Inf. Technol. 95, 5949–5961.
Allen, M., 2017. The SAGE Encyclopedia of Communication Research Methods, SAGE Publication, Inc. https://doi.org/10.4135/9781483381411.n506
Bandumula, N., 2018. Rice Production in Asia: Key to Global Food Security. Proc. Natl. Acad. Sci. India Sect. B - Biol. Sci. 88, 1323–1328. https://doi.org/10.1007/s40011-017-0867-7
Barde, P., Barde, M., 2012. What to use to express the variability of data: Standard deviation or standard error of mean? Perspect. Clin. Res. 3, 113. https://doi.org/10.4103/2229-3485.100662
Castellanos, A.P., 2018. Wave to pulse generation. From oscillatory synapse to train of action potentials.
Charoenjit, K., Zuddas, P., Allemand, P., Pattanakiat, S., Pachana, K., 2015. Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand. J. Appl. Remote Sens. 9, 096072. https://doi.org/10.1117/1.jrs.9.096072
Chen, C., Quilang, E.J.P., Alosnos, E.D., Finnigan, J., 2011. Rice area mapping, yield, and production forecast for the province of Nueva Ecija using RADARSAT imagery. Can. J. Remote Sens. 37, 1–16. https://doi.org/10.5589/m11-024
Cheng, L., Zang, H., Ding, T., Sun, R., Wang, M., Wei, Z., Sun, G., 2018. Ensemble recurrent neural network based probabilistic wind speed forecasting approach. Energies 11. https://doi.org/10.3390/en11081958
COA, 2015. Overview-Council of Agriculture, Executive Yuan, R.O.C.(Taiwan) [WWW Document]. eng.coa.gov.tw. URL https://eng.coa.gov.tw/ws.php?id=9501 (accessed 2.6.20).
EOS, 2012. SPOT 6/7 Satellite Imagery [WWW Document]. Earth Obs. Syst. URL https://eos.com/spot-6-and-7/ (accessed 2.6.20).
Gandhi, N., Petkar, O., Armstrong, L.J., 2016. Rice crop yield prediction using artificial neural networks. Proc. - 2016 IEEE Int. Conf. Technol. Innov. ICT Agric. Rural Dev. TIAR 2016 105–110. https://doi.org/10.1109/TIAR.2016.7801222
Gonçalves, A.B., Souza, J.S., Da Silva, G.G., Cereda, M.P., Pott, A., Naka, M.H., Pistori, H., 2016. Feature extraction and machine learning for the classification of Brazilian Savannah pollen grains. PLoS One 11, 1–20. https://doi.org/10.1371/journal.pone.0157044
Gowri, L., Manjula, K.R., 2019. Evaluation of various vegetation indices for multispectral satellite images. Int. J. Innov. Technol. Explor. Eng. 8, 3494–3500. https://doi.org/10.35940/ijitee.J9195.0881019
Han, I., Chuang, C.-M., 2014. Spatial and Temporal Dimensions of Collaborative Interfirm Relationships in the Taiwanese Rice Industry. J. Econ. Manag. 10, 27–48.
Hashim, N., Hamid, J.R.A., Saraf, N.M., Naharudin, N., Halim, M.A., Razali, M.H., 2019. Spectral Information Extraction from Worldview-2 Image for Urban Features Identification. ICSGRC 2019 - 2019 IEEE 10th Control Syst. Grad. Res. Colloquium, Proceeding 76–81. https://doi.org/10.1109/ICSGRC.2019.8837079
Hsing, Y., 2014. Encyclopaedia of the History of Science, Technology, and Medicine in Non-Western Cultures. Encycl. Hist. Sci. Technol. Med. Non-Western Cult. 5–8. https://doi.org/10.1007/978-94-007-3934-5
Hsu, C.H., Cheng, F.Y., 2019. Synoptic weather patterns and associated air pollution in Taiwan. Aerosol Air Qual. Res. 19, 1139–1151. https://doi.org/10.4209/aaqr.2018.09.0348
Huang, J., Wang, X., Li, X., Tian, H., Pan, Z., 2013. Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA’s-AVHRR. PLoS One 8, 1–13. https://doi.org/10.1371/journal.pone.0070816
Hughes, D., Correll, N., 2016. Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication.
Hurwitz, J., Kirsch, D., 2018. Machine Learning for dummies, IBM Limited Edition.
Ioannou, I., Gilerson, A., Gross, B., Moshary, F., Ahmed, S., 2011. Neural network approach to retrieve the inherent optical properties of the ocean from observations of MODIS. Appl. Opt. 50, 3168–3186. https://doi.org/10.1364/AO.50.003168
Jeong, S., Ko, J., Yeom, J.M., 2018. Nationwide projection of rice yield using a crop model integrated with geostationary satellite imagery: A case study in South Korea. Remote Sens. 10. https://doi.org/10.3390/rs10101665
Ji, B., Sun, Y., Yang, S., Wan, J., 2007. Artificial neural networks for rice yield prediction in mountainous regions. J. Agric. Sci. 145, 249–261. https://doi.org/10.1017/S0021859606006691
Kim, N., Ha, K.J., Park, N.W., Cho, J., Hong, S., Lee, Y.W., 2019. A comparison between major artificial intelligence models for crop yield prediction: Case study of the midwestern United States, 2006–2015. ISPRS Int. J. Geo-Information 8. https://doi.org/10.3390/ijgi8050240
Kreyszig, E., Kreyszig, H., Norminton, E.., 2011. Advanced Engineering Mathematics 10th Editi, 1283.
Kuriscak, E., Marsalek, P., Stroffek, J., Toth, P.G., 2015. Biological context of Hebb learning in artificial neural networks, a review. Neurocomputing 152, 27–35. https://doi.org/10.1016/j.neucom.2014.11.022
Lancashire, L.J., Lemetre, C., Ball, G.R., 2009. An introduction to artificial neural networks in bioinformatics - Application to complex microarray and mass spectrometry datasets in cancer studies. Brief. Bioinform. 10, 315–329. https://doi.org/10.1093/bib/bbp012
Maltarollo, V.G., Honorio, K.M., de Silva, A.B.F., 2013. Applications of artificial neural networks in chemical problems. InTech. https://doi.org/http://dx.doi.org/10.5772/51275
Moosavizadeh-Mojarad, R., Sepaskhah, A.R., 2012. Comparison between rice grain yield predictions using artificial neural networks and a very simple model under different levels of water and nitrogen application. Arch. Agron. Soil Sci. 58, 1271–1282. https://doi.org/10.1080/03650340.2011.577423
Mosleh, M.K., Hassan, Q.K., Chowdhury, E.H., 2015. Application of remote sensors in mapping rice area and forecasting its production: A review. Sensors (Switzerland) 15, 769–791. https://doi.org/10.3390/s150100769
Nakama, T., 2011. Comparisons of Single- and Multiple-Hidden-Layer Neural Networks. 8th Int. Symp. Neural Networks LNCS 6675, 270–279.
Narasimhamurthy, V., Kumar, A.P., 2017. Rice Crop Yield Forecasting Using Random Forest Algorithm SML. Int. J. Res. Appl. Sci. Eng. Technol. V, 1220–1225. https://doi.org/10.22214/ijraset.2017.10176
Nielsen, M., 2015. Neural Networks and Deep Learning, Determination Press. Determination Press. https://doi.org/10.1201/b22400-15
Noureldin, N.A., Aboelghar, M.A., Saudy, H.S., Ali, A.M., 2013. Rice yield forecasting models using satellite imagery in Egypt. Egypt. J. Remote Sens. Sp. Sci. 16, 125–131. https://doi.org/10.1016/j.ejrs.2013.04.005
Nuarsa, I.W., Nishio, F., Hongo, C., 2011. Rice Yield Estimation Using Landsat ETM+ Data and Field Observation. J. Agric. Sci. 4. https://doi.org/10.5539/jas.v4n3p45
P.S, S.K., V.S, D., 2016. Extraction of Texture Features using GLCM and Shape Features using Connected Regions. Int. J. Eng. Technol. 8, 2926–2930. https://doi.org/10.21817/ijet/2016/v8i6/160806254
Panda, S.S., Ames, D.P., Panigrahi, S., 2010. Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sens. 2, 673–696. https://doi.org/10.3390/rs2030673
Philpot, W., 2018. Why VIs ?
Pilla, S., 2011. Handbook of Bioplastics and Biocomposites Engineering Applications. Handb. Bioplastics Biocomposites Eng. Appl. https://doi.org/10.1002/9781118203699
Rahman, A., Khan, K., Krakauer, N.Y., Roytman, L., Kogan, F., 2011. Use of Remote Sensing Data for Estimation of Aman Rice Yield. Int. J. Agric. For. 1, 1–8. https://doi.org/10.5923/j.ijaf.20110101.01
Saito, A., Numata, Y., Hamada, T., Horisawa, T., Cosatto, E., Graf, H.P., Kuroda, M., Yamamoto, Y., 2016. A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix. J. Pathol. Inform. 7. https://doi.org/10.4103/2153-3539.189699
Saravanan, S., Kannan, S., Thangaraj, C., 2012. Forecasting India’s electricity demand using artificial neural network. IEEE-International Conf. Adv. Eng. Sci. Manag. ICAESM-2012 79–83.
Shahida, P., 2017. Rice technical manual for extension officers.
Sharma, E.K., Priyanka, E., Kalsh, E.A., Saini, E.K., 2015. GLCM and its Features. Int. J. Adv. Res. Electron. Commun. Eng. 4, 2180–2182.
Sharma, N., Zakaullah, M., Tiwari, H., Kumar, D., 2015. Runoff and sediment yield modeling using ANN and support vector machines: a case study from Nepal watershed. Model. Earth Syst. Environ. 1, 1–8. https://doi.org/10.1007/s40808-015-0027-0
Shiu, Y.S., Chuang, Y.C., 2019. Yield estimation of paddy rice based on satellite imagery: Comparison of global and local regression models. Remote Sens. 11, 1–18. https://doi.org/10.3390/rs11020111
Singh, S., Srivastava, D., Agarwal, S., 2017. GLCM and its application in pattern recognition. 5th Int. Symp. Comput. Bus. Intell. ISCBI 2017 20–25. https://doi.org/10.1109/ISCBI.2017.8053537
Son, N.T., Chen, C.F., Chen, C.R., Minh, V.Q., Trung, N.H., 2014. A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agric. For. Meteorol. 197, 52–64. https://doi.org/10.1016/j.agrformet.2014.06.007
Veeramuthu, A., Meenakshi, S., Darsini, V.P., 2015. Brain image classification using learning machine approach and brain structure analysis. Procedia Comput. Sci. 50, 388–394. https://doi.org/10.1016/j.procs.2015.04.030
Vinoth, B., Rajarathian, A., Baumgartner, A., Manju bargavi, S.K., 2016. Nonlinear Regression and Artificial Neural Network Based Model for Forecasting Paddy (OryzaSativa) Production in Tamil Nadu. J. Mob. Comput. Appl. 3, 1–6. https://doi.org/10.9790/0050-03030106
Wan, S., Chang, S.H., Peng, C.T., Chen, Y.K., 2017. A novel study of artificial bee colony with clustering technique on paddy rice image classification. Arab. J. Geosci. 10. https://doi.org/10.1007/s12517-017-2992-2
Wang, K., Chen, Z., 2016. Stepwise Regression and All Possible Subsets Regressionin Education. Electron. Int. J. Educ. Arts, Sci. 60–81.
Xue, J., Su, B., 2017. Significant remote sensing vegetation indices: A review of developments and applications. J. Sensors 2017. https://doi.org/10.1155/2017/1353691
Yaghouti, H., Pazira, E., Amiri, E., Masihabadi, M.H., 2017. The feasibility of using vegetation indices and soil texture to predict rice yield. Polish J. Environ. Stud. 28, 2473–2481. https://doi.org/10.15244/pjoes/81088
Yang, J., Du, L., Gong, W., Shi, S., Sun, J., Chen, B., 2018. Potential of vegetation indices combined with laser-induced fluorescence parameters for monitoring leaf nitrogen content in paddy rice. PLoS One 13, 1–15. https://doi.org/10.1371/journal.pone.0191068
Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H., Xu, B., 2017. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sens. 9. https://doi.org/10.3390/rs9070708
Zhang, X., Cui, J., Wang, W., Lin, C., 2017. A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm. Sensors (Switzerland) 17. https://doi.org/10.3390/s17071474
Zhao, Q., Lyu, S., Zhang, B., Feng, W., 2018. Multiactivation Pooling Method in Convolutional Neural Networks for Image Recognition. Wirel. Commun. Mob. Comput. 2018, 1–16. https://doi.org/10.1155/2018/8196906
Zheng, H., Cheng, T., Zhou, M., Li, D., Yao, X., Tian, Y., Cao, W., Zhu, Y., 2019. Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery. Precis. Agric. 20, 611–629. https://doi.org/10.1007/s11119-018-9600-7
Zhu, G., Ju, W., Chen, J.M., Liu, Y., 2014. A novel Moisture Adjusted Vegetation Index (MAVI) to reduce background reflectance and topographical effects on LAI retrieval. PLoS One 9. https://doi.org/10.1371/journal.pone.0102560
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2020-02-12起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2020-02-12起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw