1. Ahmadi, H. R., and J. Amiri Parian. 2015. Detecting oranges on tree by applying a digital image processing based on the shadow density pattern brigh. Journal of Agricultural Machinery 5 (1): 92-100. (In Farsi).
2. Ahmadi, K., H. Gholizadeh, H. R. Ebadzadeh, F. Hatami, R. Hosainpur, H. Abdshah, M. M. Rezaei, and M. Fazl-Estebregh. 2017. Agricultural Statistics. Gardening Products 2017 (Vol. 3). Pages 138 in Technology SaI, ed. Ministry of Agriculture, Program and Budget Deputy Directorate, Department of Statistics and Information. (In Farsi).
3. Anantrasirichai, N., S. Hannuna, and N. Canagarajah. 2017. Automatic Leaf Extraction from Outdoor Images. arXiv preprint arXiv:1709.06437.
4. Barbedo, J. G. A. 2013. Digital image processing techniques for detecting quantifying and classifying plant diseases. Springer Plus 2 (1).
5. Barbedo, J. G. A. 2016. A review on the main challenges in automatic plant disease identification based on visible range images. Biosystems Engineering 144: 52-60.
6. Bundi, S. R., A. Varadharajan, and A. Chinnasamy. 2013. Performance evaluation of various statistical classifiers in detecting the diseased citrus leaves. International Journal of Engineering Science and Technology 5 (2): 298-307.
7. Çakır, Y., M. Kırcı, E. O. Güneş, and B. B. Üstündağ. 2013. Detection of oranges in outdoor conditions. Pages 500-503. 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics): IEEE.
8. Golzarian, M. R., F. Sadeghi, N. Ghanei, and F. Kazemi. 2014. A qualitative and quantitative approach to assessing the performance of contrast enhancing color indices used in automatic computer vision plant identification system. in 8th National Congress on Agriculture Machinery Engineering & Mechanization. Jan. 2014. Mashhad, Iran. (In Farsi).
9. Hassan, M., and K. Ahmad. 2017. Anthracnose Disease of Walnut-A Review. International Journal of Environment, Agriculture and Biotechnology 2.
10. Keshavarzi, M. 2011. Walnut diseases in Iran: Diagnosis and management. Agricultural education. Karaj, Iran. (In Farsi).
11. Kim, D. G., T. F. Burks, J. Qin, and D. M. Bulanon. 2009. Classification of grapefruit peel diseases using color texture feature analysis. International Journal of Agricultural and Biological Engineering 2 (3): 41-50.
12. Kurtulmus, F., W. S. Lee, and A. Vardar. 2014. Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network. Precision Agriculture 15: 57-79.
13. Lavaf Ghazavi, M., and F. Yaghmaei. 2015. Identification and detection of plant pests of pistachio tree leave using texture and color of the image. in Second National Conference on Applied Research in Computer Science and Information Technology. Tehran, Iran. (In Farsi).
14. Mahdiani, M., R. Tabatabaei-Kolor and, M. R. Golzarian. 2015. Detection of Lettuce and Cabbage from Images Taken under Different Lighting Conditions Using an Elliptic Thresholding. Journal of Agricultural Engineering Research 15: 13-26. (In Farsi).
15. Mahmoodi-Eshkaftaki, M., J. Khazaei, K. Vahdati, and M. Taleb. 2011. Walnut disease detection using machine vision. in 1th National Conference on Modern Agriculture Sciences and Technologies. 10-12 Sep. 2011. Zanjan, Iran. (In Farsi).
16. Matsunaga, T. M., D. Ogawa, F. Taguchi-Shiobara, M. Ishimoto, S. Matsunaga, and Y. Habu. 2017. Direct quantitative evaluation of disease symptoms on living plant leaves growing under natural light. Breeding Science: 16169.
17. 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 Biosystems Engineering 48: 407-418. (In Farsi).
18. Omrani, E., S. S. Mohtasabi, S. Rafiei, S. Hosainpur, and N. A. Nategh. 2014. Apple leaf diseases detection using image analysis techniques. in 8th National Congress on Agriculture Machinery Engineering & Mechanization. Jan. 2014. Mashhad, Iran. (In Farsi).
19. Öztürk, B., M. Kirci, and E. O. Güneş. 2016. Detection of green and orange color fruits in outdoor conditions for robotic applications. Pages 1-5. 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics): IEEE.
20. Pujari, J. D., R. Yakkundimath, and A. S. Byadgi. 2015. Image processing based detection of fungal diseases in plants. Procedia Computer Science 46: 1802-1808.
21. Pydipati, R., T. Burks, and W. Lee. 2006. Identification of citrus disease using color texture features and discriminant analysis. Computers and Electronics in Agriculture 52: 49-59.
22. Rouzegar, M. R., and M. R. Golzarian. 2015. The application of image processing to detect and classify diseases of plants and fruits. in 2th National Conference of Modern Topic in Agriculture. Oct. 2015. Tehran, Iran. (In Farsi).
23. Saremi, H., S. R. Rezazahshemi, and H. Jafari. 2002. Investigating the disease of walnut black (anthracnose) in northwestern Iran. Agricultural Sciences and Natural Resources 9 (4): 141-154. (In Farsi).
24. Singh, V., and A. K. Misra. 2017. Detection of plant leaf diseases using image segmentation and soft computing techniques. Information processing in Agriculture 4: 41-49.
25. Teixido, M., D. Font, T. Pallejà, M. Tresanchez, M. Nogues, and J. Palacin. 2012. Definition of linear color models in the RGB vector color space to detect red peaches in orchard images taken under natural illumination. Sensors 12: 7701-7718.
26. Tripathi, G., and J. Save. 2015. An image processing and neural network based approach for detection and classification of plant leaf diseases. Journal Impact Factor 6: 14-20.
)
ارسال نظر در مورد این مقاله