Designing and Developing a Machine Vision System to Predict the Chlorophyll and Carotenoid Content of Plant Leaves

ماشین های کشاورزی

Article 3, Volume 9, Issue 2 - Serial Number 18, 2019, Pages 279-293   

Document Type: Research Article

DOI: 10.22067/jam.v9i2.71716

Authors

H. Biabi¹; S. Abdanan Mehdizadeh^{* 1}; M. Nadafzadeh¹; M. Salehi Salmi²

¹Mechanics of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Iran

²Horticultural Science Department, Faculty of Agriculture, Agricultural Sciences and Natural Resources University of Khuzestan, Iran

Abstract

Introduction
Leaf color is usually used as a guide for assessments of nutrient status and plant health. Most of the existing methods that examined relationships between chlorophyll status and carotenoid of leaf color were developed for particular species. Different methods have been developed to measure chlorophyll status and carotenoid. However, the high cost and difficulty to use have restricted their application, whereas the handheld chlorophyll meters such as the SPAD has become popular in the last decade for non-destructive measurement of chlorophyll content. SPAD meter readings have found to be related to the plant’s nutrition status, seed protein content, types of nodulation, and photosynthetic rates of leaves.  Digital color (RGB) image analysis, another nondestructive technique is becoming increasingly popular with its potential in phenotyping various parameters of plant health status. The development of low-cost digital cameras that use charged-couple device (CCD) arrays to capture images offers an advantage of low-cost real-time monitoring process over optical sensor based SPAD meter. Gupta et al. (2012) estimated chlorophyll content, using simple leaf digital analysis procedure in parallel to a SPAD chlorophyll content meter. The chlorophyll content as determined by the SPAD meter was significantly correlated to the RGB values of leaf image analysis (RMSE = 3.97).
The aim of this research is developing a new inexpensive, hand-held and easy-to-use technique for detection of chlorophyll and carotenoid content in plants based on leaf color. This method provides rapid analysis and data storage at minimal cost and does not require any technical or laboratory skills.
Materials and Methods
Sample collection
In this research, 15 leaves were randomly selected from six types of plants (Shoeblackplant, Vitex, Spiderwort, Sacred fig, Vine and Lotus). Afterwards, the chlorophyll content of the leaf was measured in 3 different ways: 1) using a SPAD instrument; 2) using machine vision system (non-destructive method), and 3) laboratory test using a spectrophotometer.
 Chlorophyll and carotenoid content
 The chlorophyll content of the leaf was measured and recorded using SPAD chlorophyll meter (Hansatech, model CL-01, Japan) and spectrometer as explained by Dey et al. (2016). Furthermore, to measure the carotenoid content method described by Gitelson et al. (2006) was utilized. 
 Image processing
For estimation of chlorophyll using the image processing algorithm, sample images were taken using CCD (CASIO, model Exilim EX-ZR700, Japan) and transferred to the computer. The camera was mounted perpendicular to the horizontal plane at a fixed distance of 25 cm from the samples. In a consequence histogram of leaf, images were equalized and the average of each color channels from RGB, Lab, HSV, and I1I2I3 were extracted using Matlab 2016.
 Decision tree regression (DTR) algorithm
To develop a regression model to predict chlorophyll and carotenoid content, two decision tree were constructed. The average of each color channels from RGB, Lab, HSV, and I1I2I3 become the predictor variables or feature vector and the real known chlorophyll and carotenoid content become the target variable or the target vector of each regression tree. To develop the regression models, dataset (90 observations) was split into training (60 observations) and test (30 observations) data.
 Results and Discussion
According to the obtained results, a high correlation of 0.92 for chlorophyll and 0.85 for carotenoid was achieved, respectively, between the image processing method and the values measured by the spectrometer. Therefore, it can be said that the proposed image processing method has a desirable and acceptable performance for prediction of both chlorophyll content and carotenoid. The review points out a need for fast and precise leaf chlorophyll measurement technique. With this in mind, Dey et al. (2016) used image processing techniques to measure chlorophyll content. For the purpose of analysis of the proposed model, the model outcome was compared with the LEAF+ chlorophyll meter reading. Regression analysis proofed that there was a strong correlation between the proposed image processing technique and chlorophyll meter reading. Thus, it appears that the proposed image processing technique of leaf chlorophyll measurement will be a good alternative for measuring leaf chlorophyll rapidly and with ease.
 Conclusion
In this research, collections of images from six divers plants (Shoeblackplant, Vitex, Spiderwort, Sacred fig, Vine and Lotus) were analyzed to predict chlorophyll and carotenoid content at different color spaces (RGB, Lab, HSV, and I1I2I3). Based on the results, it was shown that there were high correlations of 0.92 for chlorophyll content as well as 0.85 for carotenoid between the image processing method and the values ​​measured by the spectrometer. Therefore, in general, it can be concluded that the proposed image processing method has a desirable and acceptable performance for prediction of chlorophyll content as well carotenoid.

Keywords

Carotenoid; Chlorophyll; Color space; Image processing

References

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.