1. Astrand, B., and A. J. Baerveldt. 2002. An agricultural mobile robot with vision-based perception for mechanical weed control, Autonomous Robots 13: 21-35.
2. Ataieyan, P., P. Ahmadi Moghaddam, and E. Sepehr. 2018. Estimation of Soil Organic Carbon using Artificial Neural Network and Multiple Linear Regression Models based on Color Image Processing. Journal of Agricultural Machinery 8(1): 137-148. (In Farsi).
3. Cai, H., C. Haixin, S. Weitang, and G. Lihong. 2006. Preliminary study on photosynthetic pigment content and colour feature of cucumber initial blooms, Transactions of the CSAE 22: 34-38.
4. De’ath, G., and K. E. Fabricius. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81: 3178-3198.
5. Dey, A. K., M. Sharma, and M. R. Meshram. 2016. An Analysis of Leaf Chlorophyll Measurement Method Using Chlorophyll Meter and Image Processing Technique, Procedia Computer Science 85: 286-292.
6. Foody, G. M., and M. K. Arora. 1996. Incorporating mixed pixel in the training, allocation and testing stages of supervised classification. Pattern Recognition Letters 17: 1389-1398.
7. Garcia-Mateos, G., J. L. Hernandez-Hernandez, D. Escarabajal-Henarejos, S. Jaen-Terrones, and J. M. Molina-Martinez. 2015. Study and comparison of color models for automatic image analysis in irrigation management applications, Agricultural Water Management 151: 158-166.
8. Ghosh, I., and R. K. Samanta. 2003. TEAPEST: An expert system for insect pest management in tea. Applied Engineering in Agriculture 19 (5): 619.
9. Gitelson, A. A., G. P. Keydan, and M. N. Merzlyak. 2006. Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters 33 (11).
10. Gitelson, A. A., Y. Gritz, and M .N. Merzlyak. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves, Journal of Plant Physiology 160 (3): 271-282.
11. Golzarian1, M. R., F. Sadeghi, N. Ghanei, and F. Kazemi. 2014. A qualitative and quantitative approach to assessing the performance of contrast enhancing colour indices used in automatic computer vision plant identification system. Conference: The 8th National Congress on Agr. Machinery (Biosystem) Engineering and Mechanization, At Mashad, Iran, pp. 1579-1592. (In Farsi).
12. Gonzalez, R. C., R. E. Woods, and S. L. Eddins. 2004. Digital image processing using MATLAB, Pearson Education India.
13. Herold, N. D., G. Koeln, and D. Cunnigham. 2003. Mapping impervious surfaces and forest canopy using classification and regress tree (CART) analysis. In the American Society for Photogrammetry and Remote Sensing (ASPRS) 2003 Annual Conference Proceedings, Anchorage, Alaska.
14. Jalili Marandi, R. 2012. Post-harvest physiology (the displacement and maintenance offruits, vegetables, ornamental plants and medicinal plants). Urmia University Jihad Publications. P. 594. (In Farsi).
15. Kawashima, S., and M. Nakatani. 1998. An algorithm for estimating chlorophyll content in leaves using a video camera. Annals of Botany 81: 49-54.
16. Lawrence, L., and A. Wright. 2001. Rule-Based Classification Systems Using Classification and Regression Tree (CART) Analysis, Photogrammetric Engineering and Remote Sensing 67 (10): 1137-1142.
17. Maniezzo, V., R. Morpurgo, and S. Mussi. 1993. D-KAT: A Deep Knowledge Acquisition Tool. Expert Systems 10 (3):157-166.
18. Mercado-Luna, A., E. Rico-Garcia, A. Lara-Herrera, G. Soto-Zarazúa, R. Ocampo-Velazquez, R. Guevara- Gonzalez, R. Herrera-Ruiz, and I. Torres-Pacheco. 2010. Nitrogen determination on tomato (Lycopersicon esculentum Mill.) seedlings by colour image analysis (RGB). African Journal of Biotechnology 33: 5326-32.
19. Minolta, K. 1989. Chlorophyll meter SPAD-502 instruction manual, Minolta Co., Ltd., Radiometric Instruments Operations Osaka, Japan.
20. Moody, A., S. Gopal, and A. H. Strahler. 1996. Sensitivity of neural networks to subpixel land-cover mixtures in coarse-resolution satellite data, Remote Sensing of Environment 58: 329-343.
21. Moran, R. 1982. Formulae for determination of chlorophyllous pigments extracted with N, N-dimethylformamide, Plant Physiology 69 (6): 1376-1381.
22. Nadafzadeh, M., and S. Abdanan Mehdizadeh. 2017. Determination of the most suitable color space for intelligent water stress discrimination for plants inside the greenhouse (Case Study: Coleus), Iranian Journal of Biosystem Engineering 48 (4): pp.407-418. (In Farsi).
23. Nadafzadeh, M., S. Abdanan Mehdizadeh, M. A. Asoodar, and M. R. Salehi Salmi. 2017. Design and Development of an Intelligent Control System for Determination of Required Water needed by Plant in Greenhouse Using Machine Vision (Case Study: coleus), Iranian Journal of Biosystem Engineering, pp.285-297. (In Farsi).
24. Nematinia, E., S. Abdanan Mehdizade, and B .Nasehi. 2016. Meaurment Spaghetti colors parameters using machine vision system. Journal of Food Science and Technology, pp. 71-81. (In Farsi).
25. Sabzi, S., Y. Abbaspour-Gilandeh, and H. Javadikia. 2018. Detection of Two Types of Weed through Machine Vision System: Improving Site-Specific Spraying. Journal of Agricultural Machinery 8(1): 15-29. (In Farsi).
26. Settle, J., and N. A. Drake. 1993. Linear mixing and the estimation of ground cover proportions. International Journal of Remote Sensing 14: 1159-1177.
27. Story, D., M. Kacira, C. Kubota, A. Akoglu, and L. An. 2010. Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Computers and Electronics in Agriculture 74 (2): 238-243.
28. Su, C. H., C. C. Fu, Y. C. Chang, G. R. Nair, J. L. Ye, L. M. Chu, and W. T. Wu. 2008. Simultaneous estimation of chlorophyll a and lipid contents in microalgae by three colour analysis, Biotechnology Bioeng 99: 1034-1039.
29. Suzuki, T., H. Murase, and N. Honamin. 1999. Non-destructive growth measurement cabbage pug seedlings population by image information. Journal of Agriculture Mechanical Association 61: 45-51.
30. Vollmann, J., H. Walter, T. Sato, and P. Schweiger. 2011. Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean, Computers and Electronics in Agriculture 75: 190-195.
31. Wu, C., Z. Niu, Q. Tang, and W. Huang. 2008. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and forest meteorology 148 (8): 1230-1241.
32. Wylie, B. K., D. J. Meyer, M. J. Choate, L. Vierling, P. K. Kozak, and R. O. Green. 2000. Mapping Woody Vegetation and Eastern Red Cedar in the Nebraska Sand Hills using AVIRIS. In AVIRIS Airborne Geoscience Workshop. JPL Publication 00-18. Pasadena, CA: Jet Propulsion Laboratory, California Institute of Technology.
33. Xu, M., P. Watanachaturaporn, P .K. Varshney, and M. K. Arora. 2005. Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment 97 (3): 322-336.
34. Yadav, S. P., Y. Ibaraki, and S. D. Gupta. 2010. Estimation of the chlorophyll content of micro propagated potato plants using RGB based image analysis. Plant Cell, Tissue and Organ Culture 100: 183-188.
35. Yang, L., C. Huang, C. G. Homer, B. K. Wylie, and M. J. Coan. 2003. An approach for mapping large-area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery, Canadian Journal of Remote Sensing 29 (2): 230-240.
36. Yuzhu, H., W. Xiaomeil, and S. Shuyao. 2011. Nitrogen determination in pepper (Capsicum frutescens L.) Plants by colour image analysis (RGB). African Journal of Biotechnology 77: 17737-17741.
37. Zhao, Y., L. Gong, B. Zhou, Y. Huang, and C. Liu. 2016. Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis, Biosystems Engineering 148: 127-137.
38. Zring, A., T. Tounekti, A. Mohamed Vadel, H. Ben Mohamed, D. Valero, M. Serrano, C. Chatara, and H. Khemira. 2011. Possible involvement of polyphenols and polyamines in salt tolerance of almond rootstocks, Plant Physiology and Biochemistry 49: 1313-1322.
ارسال نظر در مورد این مقاله