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Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves

Information Processing in Agriculture 2017年第4期4卷 275-282页

作者： M.S.Borhan S.Panigrahi M.A.Satter H.Gu Research Specialist Agricultural and Biosystems Engineering DepartmentNorth Dakota State UniversityFargoND 58105USA Electrical and computer Engineering Technology Purdue University West LafayetteIN 47907USA Agricultural and Biosystems Engineering Department North Dakota State UniversityFargoND 58105USA Graduate Student Computer Science DepartmentNorth Dakota State UniversityFargoND 58105USA Senior Software Developer Psychology DepartmentNorth Dakota State UniversityFargoND 58105USA

Facilitating non-contact measurement,a computer-imaging system was devised and evaluated to predict the chlorophyll content in potato leaves.A charge-coupled device(CCD)camera paired with two optical filters and light chamber was used to acquire green(550±40 nm)and red band(700±40 nm)images from the same *** leaves from 15 plants differing in coloration(green to yellow)and age were selected for this *** based image features,such as mean and variances of green and red band images,were extracted from the *** analyses demonstrated that the variations in SPAD meter reading could be explained by themeangrayandvariances of gray scale *** fitted least square models based on the mean gray scale levels were inversely related to the chlorophyll content of the potato leaf with a R^2 of 0.87 using a green band image and with an R2 of 0.79 using a red band *** the extracted four image features,the developed multiple linear regression model predicted the chlorophyll content with a high R^2 of 0.88).The multiple regression model(using all features)provided an average prediction accuracy of 85.08% and amaximum accuracy of 99.8%.The prediction model using only mean gray value of red band showed an average accuracy of 81.6% with a maximum accuracy of 99.14%.

关键词： computer imaging Chlorophyll SPAD meter Regression Prediction accuracy

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