臺灣博碩士論文加值系統

本研究以產自台灣中部、東部以及南部的台農秈20號、台中秈10號、台農67號、台稉8號以及台稉9號五種水稻品種，收穫於1997、1998、1999夏天的一期稻作為樣本，分析樣本的近紅外線反射光譜的吸收值，及樣本的外觀幾何型態和顏色為變數，以判別分析和倒傳遞類神經網路建立鑑別模式，並鑑別產於上述相同地區收穫於2000年夏天的一期水稻樣本。
以1100至2500nm近紅外線反射光譜值，每隔3 nm擷取一個吸收值為變數。全部351個變數建立判別分析以及倒傳遞類神經網路鑑別模式，平均鑑別率分別為98.1%及92.5%。以逐步排除法選取69個變數建立判別分析以及倒傳遞類神經網路鑑別模式，平均鑑別率分別為98.5%及85.5%。以變數之間的相關矩陣選取69個變數建立判別分析以及倒傳遞類神經網路鑑別模式，平均鑑別率分別為72.0%以及72.2%。以變數在第一和第二主成分軸上的Loading值選取69個變數建立判別分析以及倒傳遞類神經網路鑑別模式，平均鑑別率分別為69.1%及60.6%。在近紅外線的波長選擇方法中，以逐步排除法挑選的變數所建立的模式，鑑別率優於相關矩陣法及loading值法所建立的模式，不僅能減少變數的數目，同時鑑別率仍可達到使用351個變數的準確性。使用相同的變數時，判別分析法的鑑別能力優於類神經網路法，且具顯著差異。
使用五種水稻的外觀形狀及顏色作為變數，包括單一穀粒的面積、周長、形狀係數(4π×面積/周長2)、面積/周長、最大寬度值、最長軸值、最長軸/最大寬度、每粒稻穀的平均紅色光度值(R)、平均綠色光度值(G)、平均藍色光度值(B)，以及最長軸上等距之50個寬度值，共計60個水稻特徵。以倒傳遞類神經網路訓練品種鑑別模式。模式1使用60個變數的平均鑑別率為92%。模式2 使用在第一主成分軸上的Loading值較大的50個變數，平均鑑別率為90.0%。模式3使用以相關矩陣選取35個相關性較低的變數，平均鑑別率為91.0%。模式4以變數對網路訓練的影響度，由大到小挑選20個影響度較大的變數，平均鑑別率為91.8%。在變數的選擇方法中，以變數對網路訓練的影響度所挑選的變數所建立的鑑別模式，在進行水稻的品種鑑別時不僅可使用較少的變數，同時也具有較佳的穩定性。
結合以逐步排除法從60個外觀形狀及顏色變數裡選出17個，以及從351個近紅外線反射光譜的吸收值選出54個變數，共71個變數，使用倒傳遞類神經網路建立鑑別模式。依變數在第一主成分軸上的Loading 值由大到小每隔5個變數分別選取10至71 個變數建立鑑別模式1，平均驗證率分別為76.2%、82.4%、92.9%、92.7%、95.5%、95.8%、96.5%、94.1%、95.5%、95.5%、95.5%、95.5%及96.5%，平均鑑別率分別為78.0%、78.5%、83.5%、51.9%、66.8%、51.2%、77.1%、73.7%、59.0%、82.0%、83.2%、78.6%及79.3%。依變數之間的相關性由低至高每隔5個變數分別選取10至71 個變數建立鑑別模式2，平均驗證率分別為83.3%、83.5%、83.5%、93.9%、93.4%、96.2%、96.0%、96.7%、97.2%、95.8%、97.2%、96.7%及96.2%，平均鑑別率分別為87.0%、85.4%、76.4%、70.4%、54.4%、45.0%、49.8%、55.3%、62.0%、62.0%、61.9%、76.8%及79.0%。依變數對類神經網路訓練的影響度由大到小每隔5個變數分別選取10至71個變數建立鑑別模式3，平均驗證率為85.2%、91.5%、91.1%、92.5%、94.1%、95.3%、96.2%、95.5%、96.0%、95.5%、96.0%、96.0%及96.2%，平均鑑別率分別為65.1%、66.3%、64.9%、62.0%、74.7%、70.8%、66.6%、76.4%、78.8%、78.5%、72.5%、71.8%及85.1%。結合近紅外線反射光譜的吸收值與外觀性狀及顏色作為變數所建立的鑑別模式，使用的變數較少時，其鑑別率反不如單獨使用近紅外線反射光譜的吸收值或單獨以外觀性狀及顏色作為變數所建立的鑑別模式的鑑別率，推測其主要原因乃是因為在擷取近紅外線反射光譜的吸收值時，是掃描約8g重(約90至100粒)的散裝樣本置於容器之平均值，擷取外觀形狀及顏色變數時，則以單粒的榖粒取像之10粒平均值。對樣本不同的處理方法的資料結合建立模式時，可能造成相互干擾，反而降低鑑別率。進一步研究掃描單粒擷取近紅外線反射光譜的吸收值，是否可以提升鑑別率。總之，以逐步排除法、變數之間的相關性、主成分軸上的Loading 值以及變數對類神經網路訓練時的影響度為依據選取變數，較能選出具品種特徵和品種區分能力的變數，降低鑑別模式所需使用變數的數目。以判別分析與倒傳遞類神經網路建立的鑑別模式，能有效鑑別水稻的品種。

From five paddy rice cultivars Tainung Sen 20, Taichung Sen 10, Tainung 67, Taikeng 8, and Taikeng 9 grown in central, eastern and south Taiwan, and harvested in the summers of 1997, 1998, and 1999, calibrated models were established by discriminant analysis and backpropagation neural network program through near infrared absorbance and external morphological and color features selection. The calibrated models were used to classify the above mentioned five paddy rice cultivars harvested in the same area in the summer of 2000.
From 1100 nm to 2500 nm in 3-nm steps, the reflectance spectrum absorbance was collected as a variable. Totally three hundred fifty-one variables were used to develop the discriminant analysis and backpropagation neural network models, and the average classification rates were 98.1% and 92.5%, respectively. Sixty-nine variables were selected by using stepwise discrimination to develop the discriminant analysis and backpropagation neural network models, and the average classification rates were 98.5% and 85.5%, respectively. Sixty-nine variables were selected by using the correlation matrix to develop the discriminant analysis and backpropagation neural network models, and the average classification rates were 72.0% and 72.3%, respectively. Sixty-nine variables were selected by loading on the first and second principal components to develop the discriminant analysis and backpropagation neural network models, and the average classification rates were 69.1% and 60.6%, respectively. In selecting wavelength of near infrared spectroscopy for establishing models, the classification rate by stepwise discrimination method was superior to the results by correlation matrix and loading value method. Reducing 351 variables to 69, the same classification rate was still kept. Using the same variables the classification by discrimation method was better than that by backpropagation neural network.
Using morphological and color features of five paddy rice cultivars, there were 60 variables including single kernel area, perimeter, shape factor (4π×area/perimeter2), area/perimeter, maximum width, maximum length, maximum length/ maximum width, average intensities of red, green, and blue, and 50 widths on the maximum length. The following models were trained by backpropagation neural network program to establish classification models. With 60 features, the average classification rate of Model 1 was 92%. With the most effective 50 features, by loading in the first principal component, the average classification rate of Model 2 was 90.0%. With 35 features selected from the correlation coefficient matrix, the average classification rate of Model 3 was 91.0%. With the most effective 20 features of area, area/perimeter, 48th width, shape factor, maximum length/maximum width, average intensity of blue, maximum length, average intensity of green, 47th width, 50th width, average intensity of red ,1st width, 19th width, 5th width, 6th width, 29th width, perimeter, 46th width, 42nd width, and 4th width based on the contribution of the training model, the average classification rate of Model 4 was 91.8% and would be recommended for classifying five paddy rice cultivars of set trading prices because it required fewer features and held a stable classification rate.
17 variables from 60 morphological and color features and 54 variables from 351 near infrared reflectance absorbance were selected by stepwise discriminant method. Totally 71 variables were input into backpropagation neural network to establish classification model. Selecting 71 variables according to loading value on the first principal component from high to low, the number of variables were used from 10 to 71 with 5 steps were input backpropagation artificial neural network program to establish Model 1. The average validation rates of model were 76.2%, 82.4%, 92.9%, 92.7%, 95.5%, 95.8%, 96.5%, 94.1%, 95.5%, 95.5%,95.5%,95.5%,and 96.5%, respectively, and the average classification rates were 78.0%, 78.5%, 83.5%, 51.9%, 66.8%, 51.2%, 77.1%, 73.7%, 59.0%, 82.0%, 83.2%, 78.6%, and 79.3%, respectively. With 71 variables arranged according to correlation coefficient from low to high, and with the number of variables from 10 to 71 with 5 steps to establish Model 2, the average validation rates were 83.3%, 83.5%, 83.5%, 93.9%, 93.4%, 96.2%, 96.0%, 96.7%, 97.2%, 95.8%, 97.2%, 96.7%, and 96.2%, respectively, and the average classification rates were 87.0%, 85.4%, 76.4%, 70.4%, 54.4%, 45.0%, 49.8%, 55.3%, 62.0%, 62.0%, 61.9%, 76.8%, and 79.0%, respectively. According to contribute to backpropagation neural network model from high to low, and the number of variables from 10 to 71 with 5 steps were used to establish Model 3. The average validation rates were 85.2%, 91.5%, 91.1%, 92.5%, 94.1%, 95.3%, 96.2%, 95.5%, 96.0%, 95.5%, 96.0%, 96.0%, and 96.2%, respectively, and the average classification rates were 65.1%, 66.3%, 64.9%, 62.0%, 74.7%, 70.8%, 66.6%, 76.4%, 78.8%, 78.5%, 72.5%, 71.8%, and 85.1%, respectively. Collecting image features from single kernel and average 10 kernel data, and scanning the near-infrared absorbance in a vessel (8 g, about 90-100 kernels), two different data treatment method were combined to establish models, it may exist some interference and reduce the classification rates only by image features or near infrared absorbance. Further studying scanning the near-infrared absorbance in single kernel see whether can improve the classification rates.

CONTENTS

Chapter I Introduction……………………………………………1

Chapter II Wavelength Selection for Classifying Paddy Rice
by Near-Infrared Spectroscopy……………………………………12

Chapter III Classifying Paddy Rice by Morphological and
Color Features Using Machine Vision……………………………31

Chapter IV Classifying Paddy Rice by Near-Infrared Spectroscopy and Machine Vision…………………………………52

Chapter V Conclusions……………………………………………………………69

Appendix I Matlab Program for Variables Selection by
Stepwise Discriminant………………………………………………72

Appendix II Matlab Program for Variables Selection by
Correlation Matrix…………………………………………………78

Appendix III Matlab Program for Variables Selection by Loading…………………………………………………………………81

Appendix IV Matlab Program for Variables Selected by Contribution…………………………………………………………87

Appendix V Compressed Version in Chinese…………………………………………………………………91

Chang, H. W., Shaw J. T., Lee, S. M., and Hong, M. C. 2000. Classifying the varieties of paddy rice by near infrared and image processing techniques. J. Agric. Mach. 9:1-16.
Granitto, P. M., Verdes, P. F., and Ceccatto, H. A. 2005. Large-scale investigation of weed seed identification by machine vision. Computers and Electronics in Agriculture. 47: 15–24.
Jin, T., Liu, L., Tang, X., and Chen, H. 1995. Differentiation of male, female and dead silkworms while in the cocoon by near infrared spectroscopy. J. Near Infrared Spectrosc. 3: 89–95.
Kwon, Y. K., and Cho, R. K. 1998. Identification of rice variety using near infrared spectroscopy. J. Near Infrared Spectrosc. 6: 67–73.
Liu, M. Q., and Shaw, J. T. 1995. The detection of rice moisture and protein content with near-infrared spectrophotometer. J. Agric. Mach. 4:1-14.
Li, W. S., and Shaw, J. T. 1996. Data processing affecting the NIR calibration curves of major constituents of rough rice taste. J. Agric. Mach. 5:19-34.
Li, W. S., and Shaw, J. T. 1997. Determining the fat acidity of rough rice by near-infrared reflectance spectroscopy. Cereal Chem. 74:556-560.
Lin, C. T., and Lee, C. S. 1999. Neural fuzzy systems. Prentice-Hall (Singapore) Pte Ltd. P236-239.
Liu, C. C., Shaw, J. T., Poong, K. Y., Hong, M. C., and Shen, M. L. 2005. Classifying paddy rice by morphological and color features using machine vision. Cereal Chem. 82:649-653.
Liu, C. C., Shaw, J. T., Poong, K. Y., Hong, M. C., and Shen, M. L. 2006. Wavelength selection for classifying paddy rice by near-infrared spectroscopy. Cereal Chem. 83: 335-339.
Osborne, B.G., Mertens, B., Thompson, M., and Fearnc, T. 1993. The authentication of Basmati rice using near infrared spectroscopy. J. Near Infrared Spectrosc. 1: 77–83.
Paliwal, J., Shashidhar, N. S., and Jayas, D.S. 1999. Grain kernel identification using kernel signature. Trans. ASAE 42: 1921-1924.
Rencher, A. C. 1995. Methods of multivariate analysis. John Wiley and Sons, Inc.New York. P.257.
Shatadal, P., and Tan, J. 2003. Identifying damaged soybeans by color image analysis. Applied Engineering in Agriculture. Vol. 19: 65-69.
Sirieix, A., and Downey, G. 1993. Commercial wheat flour authentication by discriminant analysis of near infrared reflectance spectra. J. Near Infrared Spectrosc. 1:187–197.
Wan, Y. N. 2002a. Kernel handling performance of an automatic quality inspection system. Trans. ASAE 45: 369-377.
Wan, Y. N., Lin, C. M., and Chiou, J. F. 2002b. Rice quality classification using an automatic grain quality inspection system. Trans. ASAE 45: 379-387.
Wang, D., Dowell, F. E., and Lacey, R. E. 1999. Single wheat kernel color classification using neural networks. Trans. ASAE 43:233-240.
Wang, D., Ram, M. S., and Dowell, F. E. 2002. Classification of damaged soybean seeds using near-infrared spectroscopy. Trans. ASAE. 45: 1943–1948.
Chang, H. W., Shaw, J. T., Lee, S. M., and Hong, M. C. 2000. Classifying varieties of paddy rice by near-infrared and image processing technique. J. Agric. Mach. 9:1-16.
Corbella, E., and Cozzolino, D. 2005. The use of visible and near-infrared spectroscopy to classify the floral origin of honey samples produced in Uruguay. J. NIRS 13:63-68.
Delwiche, S. R., Chen, Y. R., and Hruschka, W. R. 1995. Differentiation of hard red wheat by near-infrared analysis of bulk samples. Cereal Chem. 72:243-247.
Liu, M. Q, and Shaw, J. T. 1995. The detection of rice moisture and protein content with near-infrared spectrophotometer. J. Agric. Mach. 4:1-14.
Li, W. S., and Shaw, J. T. 1996. Data processing affecting the NIR calibration curves of major constituents of rough rice taste. J. Agric. Mach. 5:19-34.
Li, W. S., and Shaw, J. T. 1997. Determining the fat acidity of rough rice by near-infrared reflectance spectroscopy. Cereal Chem. 74:556-560.
Liu, C. C., Shaw, J. T., Poong, K. Y., Hong, M. C., and Shen, M. L. 2005. Classifying paddy rice by morphological and color features using machine vision. Cereal Chem. 82:649-653.
Paliwal, J., Visen, N. S., Jayas, D. S., and White, N. D. G. 2003. Comparison neural network and a non-parametric classifier for grain kernel identification. Biosys. Eng. 85:405-413.
Peng, Z. Y. 2000. SAS and statistical analysis (in Chinese). 2nd Ed. Pages 41-2. Ru-lin Publisher: Taipei, Taiwan.
Rencher, C. A. 1995. Discriminant analysis: Description of group separation. Pages 296-325 In: Methods of Multivariate Analysis. John Wiley and Sons: New York.
Rittiron, R., Saranwong, S., and Kawano, S. 2005. Detection of variety contamination in milled Japanese rice using a single kernel near infrared technique in transmittance mode. J. NIRS 13:9-25.
Shen, M. L. 1999. Introduction to statistics (in Chinese), 3rd Ed. Pages 177-178. Chiu-chow Publisher: Taipei, Taiwan.
Song, H., Delwiche, S. R., and Chen, Y. R. 1995. Neural network classification of wheat using single kernel near-infrared transmittance spectra. Opt. Eng. 34:2927-2934.
Velleman, P. F., and Welsch, R. E. 1981. Efficient computing of regression diagnostics. Am. Statist. 35:234-242.
Wang, D., Ram, M. S., and Dowell, F. E. 2002. Classification of damaged soybean seeds using neat infrared spectroscopy. Trans. ASAE 45:1943-1948.
Barker, D. A., Vuori, T. A., and Myers, D. C. 1992. The use of slice and aspect ratio parameters for the discrimination of Australian wheat varieties. Plant Varieties Seeds 5:47-52.
Burks, T. F., Shearer, S. A., Gates, R. S., and Donohue, K. D. 2000. Backpropagation neural network design and evaluation for classifying weed species using color image texture. Trans. ASAE 43:1029-1037.
Chang, H. W., Shaw, J. T., Lee, S. M., and Hong, M. C. 2000. Classifying varieties of paddy rice by near-infrared and image processing technique. J. Agric. Mach. 9:1-16.
Corbella, E., and Cozzolino, D. 2005. The use of visible and near-infrared spectroscopy to classify the floral origin of honey samples produced in Uruguay. J. NIRS 13:63-68.
Delwiche, S. R., Chen, Y. R., and Hruschka, W. R. 1995. Differentiation of hard red wheat by near-infrared analysis of bulk samples. Cereal Chem. 72:243-247.

Liu, M. Q, and Shaw, J. T. 1995. The detection of rice moisture and protein content with near-infrared spectrophotometer. J. Agric. Mach. 4:1-14.
Li, W. S., and Shaw, J. T. 1996. Data processing affecting the NIR calibration curves of major constituents of rough rice taste. J. Agric. Mach. 5:19-34.
Li, W. S., and Shaw, J. T. 1997. Determining the fat acidity of rough rice by near-infrared reflectance spectroscopy. Cereal Chem. 74:556-560.
Luo, X., Jayas, D. S., and Symons, S. J. 1999. Comparison of statistical and neural network methods for classifying cereal grains using machine vision. Trans. ASAE 42:413-419.

Liu, C. C., Shaw, J. T., Poong, K. Y., Hong, M. C., and Shen, M. L. 2005. Classifying paddy rice by morphological and color features using machine vision. Cereal Chem. 82:649-653.
Majumdar, S., and Jayas, D. S. 2000. Classification of cereal grains using machine vision: Combined morphology, color, and texture models. Trans. ASAE 43:1689-1694.
Paliwal, J., Visen, N. S., and Jayas, D. S. 2001. Evaluation of neural network architectures for cereal grain classification using morphological features. J. Agric. Eng. Res. 79:361-370.
Paliwal, J., Visen, N. S., Jayas, D. S., and White, N. D. G. 2003. Comparison neural network and a non-parametric classifier for grain kernel identification. Biosys. Eng. 85:405-413.
Peng, Z. Y. 2000. SAS and statistical analysis (in Chinese). 2nd Ed. Pages 41-2. Ru-lin Publisher: Taipei, Taiwan.
Rencher, C. A. 1995. Discriminant analysis: Description of group separation. Pages 296-325 In: Methods of Multivariate Analysis. John Wiley and Sons: New York.
Rittiron, R., Saranwong, S., and Kawano, S. 2005. Detection of variety contamination in milled Japanese rice using a single kernel near infrared technique in transmittance mode. J. NIRS 13:9-25.
Song, H., Delwiche, S. R., and Chen, Y. R. 1995. Neural network classification of wheat using single kernel near-infrared transmittance spectra. Opt. Eng. 34:2927-2934.
Shatadal, P., Jayas, D. S., and Bulley, N. R. 1995. Digital image analysis for software separation and classification of touching grains. II. Classification. Trans. ASAE 38:645-649.

Shen, M. L. 1999. Introduction to statistics (in Chinese), 3rd Ed. Pages 177-178. Chiu-chow Publisher: Taipei, Taiwan.
Shen, M. L. 2003. Introduction to Statistics. 4th Ed. Pages 376-390. (In Chinese) Chiu-chow Publisher: Taipei, Taiwan.
Travis, A. J., and Draper, S. R. 1985. A computer based system for the recognition of seed shape. Seed Sci. Technol. 14:715-724.
Tsang, M., Hsia, I., and Chen, C. 1990. Identification of rice cultivars by means of quantifying the grain shape. College of Agriculture, National Taiwan University: Taiwan.
Velleman, P. F., and Welsch, R. E. 1981. Efficient computing of regression diagnostics. Am. Statist. 35:234-242.
Wang, D., Ram, M. S., and Dowell, F. E. 2002. Classification of damaged soybean seeds using neat infrared spectroscopy. Trans. ASAE 45:1943-1948.
Wang, D., Dowell, F. E., and Lacey, R. E. 1999. Single wheat kernel color classification using neural networks. Trans. ASAE 43:233-240.
Zayas, I., Pomeranz, Y., and Lai, F. S. 1986. Discrimination between wheat classes and varieties by image analysis. Cereal Chem. 63:52-56.
Burks, T. F., Shearer, S. A., and Donohue, K. D. Backpropagation neural network design and evaluation for classifying weed species using color image texture. Trans. ASAE 43: 1029-1037.
Chang, H. W., Shaw, J. T., Lee, S. M., and Hong, M. C. 2000. Using near infrared rays and imaging technology to validate varieties of paddy rice. J. Agric. Mach. 9:1-16.
Lee, S. M. 1998.Using near infrared rays and imaging technology to validate varieties of paddy rice. Graduate Thesis. Taipei: Department of Bio-Industrial Mechatronics Engineering of National Taiwan University.
Liu, C. C., Shaw, J. T., Poong, K. Y., Hong, M. C., and Shen, M. L. 2005. Influences of Paddy Rice Classifying of Choose of Variables. J. Agric. Mach. 14:17-32.
Liu, C. C., Shaw, J. T., Poong, K. Y., Hong, M. C., and Shen, M. L. 2005. Influences of Paddy Rice Variety Validating of Choosing of Near Infrared Spectra Wavelength. J. Agric. Mach. 14: 27-38.
Liu, C. C., Shaw, J. T., Poong, K. Y., Hong, M. C., and Shen, M. L. 2005. Wavelength selection for classifying paddy rice by near-infrared spectroscopy. Cereal Chem. 83: 335-339.
Peng, C. Y. 2000. SAS and Statistic Analysis. 6th Version. Taipei: Scholar Books Co., Ltd.
Paliwal J., Shashidhar, N. S., and Jayas, D. S. 1999. Grain Kernel Identification using Kernel Signature. Trans. ASAE. 42: 1921-1924.
Paliwal J., Visen, N. S., and Jayas, D. S. 2001. Evaluation of Neural Network Architectures for Cereal Grain Classification using Morphological Variables. J. Agric. Engng Res. 79: 361-370.
Paliwal, J., Visen, N. S., Jayas, D. S., and White, N. D. G. 2003. Comparison neural network and a non-parametric classifier for grain kernel identification. Biosystems Engineering. 85: 405-413.
Tsung, M. T., Hsieh, Y. H., and Chen, C. Y. 1990. Using the average number of outer image characteristics of paddy rice to judge the cultivating variety of paddy rice. Research Report of College of Bio-Resources and Agriculture of National Taiwan University. 30:58-65.
沈明來。1999。生物統計學入門。3版。177-178。台北：九州出版社。
李汪盛、蕭介宗。1996。資料處理對於稻穀食味主要成分之近紅外線校正線之影響。農業機械學刊 5:19-34。
曾美倉、謝英雄、陳正義。1990。利用榖粒外形影像特徵之樣品平均數來判別水稻栽培品種。台大農學院研究報告 30：58-65。
張鴻文、蕭介宗、李盛銘、洪梅珠。2000。以近紅外線及影像技術鑑別水稻品種。農業機械學刊 9：1-16。
彭昭英。2000。SAS與統計分析。11版。台北：儒林圖書有限公司。
劉民卿、蕭介宗。1995。以近紅外線光譜儀感測稻米之含水率及蛋白質含量。農業機械學刊 4:1-14。
Burks, T. F., Shearer, S. A. and Donohue, K. D. 2000. Backpropagation neural network design and evaluation for classifying weed species using color image texture. Trans. ASAE. 43: 1029-1037.
Liu, C. C., Shaw, J. T., Poong, K. Y., Hong, M. C., and Shen, M. L. 2005. Classifying paddy rice by morphological and color features using machine vision. Cereal Chem. 82:649-653.
Liu, C. C., Shaw, J. T., Poong, K. Y., Hong, M. C., and Shen, M. L. 2005. Wavelength selection for classifying paddy rice by near-infrared spectroscopy. Cereal Chem. 83: 335-339.
Paliwal, J., Visen, N. S., Jayas, D. S. and White, N. D. G. 2003. Comparison neural network and a non-parametric classifier for grain kernel identification. Biosystems Engineering 85: 405-413.
Paliwal J., Shashidhar, N. S. and Jayas, D. S. 1999. Grain Kernel Identification using Kernel Signature. Trans. ASAE. 42: 1921-1924.
Paliwal J., Visen, N. S. and Jayas, D. S. 2001. Evaluation of Neural Network Architectures for Cereal Grain Classification using Morphological Parameters. J. Agric. Engng Res. 79: 361-370.
Song H., Delwiche, S. R. and Chen, Y.R. 1995 Neural network classification of wheat using single kernel near-infrared transmittance spectra. Optical Engineering 34:2927-2934.
Velleman, P. F. and Welsch, R. E. 1981. Efficient computing of regression diagnostics. The Amer. Statistician 35:234-242.