e-ISSN 2231-8526
ISSN 0128-7680

Home / Regular Issue / JST Vol. 29 (4) Oct. 2021 / JST-2551-2021


The Prediction of Chlorophyll Content in African Leaves (Vernonia amygdalina Del.) Using Flatbed Scanner and Optimised Artificial Neural Network

Retno Damayanti, Nurul Rachma, Dimas Firmanda Al Riza and Yusuf Hendrawan

Pertanika Journal of Science & Technology, Volume 29, Issue 4, October 2021


Keywords: African leaves, artificial neural network, chlorophyll, flatbed scanner

Published on: 29 October 2021

African leaves (Vernonia amygdalina Del.) is a nutrient-rich plant that has been widely used as a herbal plant. African leaves contain chlorophyll which identify compounds produced by a plant, such as flavonoids and phenols. Chlorophyll testing can be carried out non-destructively by using the SPAD 502 chlorophyll meter. However, it is quite expensive, so that another non-destructive method is developed, namely digital image analysis. Relationships between chlorophyll content and leaf image colour indices in the RGB, HSV, HSL, and Lab* space are examined. The objectives of this study are 1) to analyse the relationship between texture parameters of red, green, blue, grey, hue, saturation(HSL), lightness (HSL), saturation( HSV), value(HSV), L*, a*, and b* against the chlorophyll content in African leaves using a flatbed scanner (HP DeskJet 2130 Series); and 2) built a model to predict chlorophyll content in African leaves using optimised ANN through a feature selection process by using several filter methods. The best ANN topologies are 10-30-40-1 (10 input nodes, 40 nodes in hidden layer 1, 30 nodes in hidden layer 2, and 1 output node) with a trainlm on the learning function, tansig on the hidden layer, and purelin on the output layer. The selected topology produces MSE training of 0.0007 with R training 0.9981 and the lowest validation MSE of 0.012 with R validation of 0.967. With these results, it can be concluded that the ANN model can be potentially used as a model for predicting chlorophyll content in African leaves.

  • Abdulkadir, A. R., Sarwar, M. J., & Dhiya, D. Z. (2015). Effect of chlorophyll cotent and maturity on total phenolic, total flavonoid contents and antioxidant activity of Moringa oleifera Leaf (Miracle Tree). Journal of Chemical and Pharmaceutical Research, 7(5), 1147-1152.

  • Armi, L., & Shervan, F. E. (2019). Texture image analysis and texture classification methods - A review. International Online Journal of Image Processing and Pattern Recognition, 2(1), 1-29.

  • Barman, U., & Choudury, R. D., (In Press). Smartphone image based digital chlorophyll meter to estimate the value of citrus leaves chlorophyll using linear regression, LMBP-ANN and SCGBP-ANN. Journal of King Saud University – Computer and Information Sciences.

  • Barman, U., Ridip, D. C., Arunav, S., Susmita, D., Bijon, K. D., Barna, P. M., & Golap, G. B. (2018). Estimation of chlorophyll using image processing. International Journal of Recent Scientific Research, 9(3), 24850-24853.

  • Borhan, M. S., Panigrahi, S., Satter, M. A., & Gu, H. (2017). Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves. Information Processing in Agriculture, 4(4), 275-282.

  • Cartelat, A., Cerovic, Z. G., Goulas, Y., Meyer, S., Lelarge, C., Prioul, J. L., Barbottin, A., Jeuffroy, M. H., Gate, P., Agati, G., & Moya, I. (2005). Optically assessed contents of leaf polyphenolics and chlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.). Field Crops Research, 91, 35-49.

  • Dalen, G. V. (2006). Characterisation of rice using flatbed scanning and image analysis. Food Policy, Control, and Research, 6, 149-186.

  • Damayanti, R., Sandra, & Dahlena, E. (2020). The artificial neural network to predict chlorophyll content of cassava (Manihot esculenta) leaf. In IOP Conference Series: Earth and Environmental Science (Vol. 475, No. 1, p. 012012). IOP Publishing.

  • Danladi, S., Muhammad, A. H., Idris, A. M., & Umar, I. I. (2018). Vernonia amygdalina Del: A mini review. Research Journal of Pharmacy and Technology, 11(9), 4187-4190.

  • Garner, S. R. (1995, April 18-21). WEKA: The waikato environment for knowledge analysis. In Proceedings of the New Zealand computer science research students conference (Vol. 1995, pp. 57-64). University of Waikato, Hamilton.

  • Grunenfelder, L., Hiller, L. K., & Knowles, R. (2006). Color indices for the assessment of chlorophyll development and greening of fresh market potatoes. Postharvest Biology and Technology, 40(1), 73-81.

  • Gupta, S. D., & Pattanayak, A. K. (2017). Intelligent image analysis (IIA) using artificial neural network (ANN) for non-invasive estimation of chlorophyll content in micropropagated plants of potato. In Vitro Cellular & Developmental Biology-Plant, 53, 520-526.

  • Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610-621.

  • Hassanijalilian, O., Igathinathane, C., Doetkott, C., Bajwa, S., Nowatzki, J., & Esmaeili, S. A. H. (2020). Chlorophyll estimation in soybean leaves infield with smartphone digital imaging and machine learning. Computer and Electronics in Agriculture, 174, 1-12.

  • Hendrawan, Y., Amini, A., Maharani, D. M., & Sandra. (2019a). Intelligent non-invasive sensing method in identifying coconut (Coco nucifera var. Ebunea) ripeness using computer vision and artificial neural network. Pertanika Journal of Science & Technology, 27(3), 1317-1339.

  • Hendrawan, Y., Fauzi, M. R., Khoirunnisa, N. S., Andreane, M., Hartianti, P. O., Halim, T. D., & Umam, C. (2019b). Development of colour co-occurrence matrix (CCM) texture analysis for biosensing. IOP Conference Series: Earth and Environmental Science, 230, 1-8.

  • Hendrawan, Y., Widyaningtyas S., & Sucipto, S. (2019c). Computer vision for purity, phenol, and pH detection of Luwak Coffee green bean. TELKOMNIKA, 17(6), 3073-3085.

  • Hendrawan, Y., & Haruhiko, M. (2009). Precision irrigation for sunagoke moss production using intelligent image analysis. Environmental Control in Biology, 47, 21-36.

  • Hendrawan, Y., Sakti, I. M., Wibisono, Y., Rachmawati, M., & Sandra. (2018). Image analysis using color co-occurrence matrix textural features for predicting nitrogen content in spinach. TELKOMNIKA,16(6), 2711-2723.

  • Hu, H., Liu, H. Q., Zhu, J. H., Yao, X. G., Zhang, X. B., & Zheng, K. F. (2010). Assesment of chlorophyll content based on image color analysis, comparison wih SPAD-502. In 2010 2nd International Conference on Information Engineering and Computer Science (pp. 1-3). IEEE Publishing.

  • Jaber, A. A., Ahmed, A. M. S., & Hussein, F. M. A. (2019). Prediction of hourly cooling energy consumption of educational buildings using artificial neural network. International Journal on Advanced Science Engineering Information Technology, 9(1), 159-166.

  • Kato, J., Hiroya, H., Shinsuke, T., Kenjiro, T., & Takashi, K. (2015). Analytical sensitivity in toopology optimization for elastoplastic composites. Structural and Multidisciplinary Optimization, 52(3), 507-526.

  • Kaur, G., Salim, D., Amandeep, S. B., & Derminder, S. (2014). Scanner image analysis to estimate leaf area. International Journal of Computer Application, 107(3), 5-10.

  • Kumar, C. S., & Rama, R. J. S. (2014). Application of ranking based attribute selection filters to perform automated evaluation of descriptive answers through sequential minimal optimization models. ICTACT Journal on Soft Computing, 5(1), 860-868.

  • Li, J., Kewei, C., Suhang, W., Fred, M., Robert, P. T., Jiliang, T., & Huan, L. (2017). Feature selection: A data perspective. ACM Computing Surveys, 50(6), 94:1-94:45.

  • Limantara, L., Martin, D., Renny, I., Indriatmoko., & Tatas, H. P. B. (2015). Analysis on the chlorophyll content of commercial green leafy vegetables. Procedia Chemistry, 14, 225-231.

  • Luimstra, V. M., Schuurmans, J. M., Antonie, M. V., Klass, J. H., Jef, H., & Hans, C. P. M. (2018). Blue light reduce photosynthetic efficiency of cyanobacteria through an imbalance between photosystems I and II. Photosynthesis Research, 138(2), 177-189.

  • Mendoza, R. J. P., Daniel, R., & Luis, D. M. (2018). Distributed reliefF-based feature selection in spark. Knowledge and Information System, 57, 1-20.

  • Mohan, P. J., & Gupta, S. D. (2019). Intelligent image analysis for retrieval of leaf chlorophyll content of rice from digital images of smartphone under natural light. Photosyntheticia, 57(2), 388-398.

  • Nursuhaili, A. B., Nur, A. S. P., Martini, M. Y., Azizah, M., & Mahmud, T. M. M. (2019). A review: Medicina valur, agronomatic practices and post-harvest handlings of Vernonia amygdalina. Food Research, 3(5), 380-390.

  • Okafor, E., Lambert, S., & Marco, A. W. (2018). An analysis of rotation matrix and colour constancy data augmentation in classifiying image of animals. Journal of Information and Telecommunication, 2(4), 465-491.

  • Oyeyemi, I. T., Akinbiyi, A. A., Aderiike, A., Abimbola, O. A., & Oyetunde, T. O. (2017). Vernonia amygdalina: A folkloric herb with anthelminthic properties. Beni-Suef University Journal of Basic and Applied Sciences, 7(1), 43-49.

  • Pavlovic, D., Bogdan, N., Sanja, D., Hadi, W., Ana, A., & Dragana, M. (2014). ChlorSophyll as a measure of plant health: Agroecological aspects. Pestic Phytomed, 29(1), 21-34.

  • Peng, Y., & Yi, W. (2019). Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder. International Journal of Food Properties, 22(1), 1720-1732.

  • Rajalakshmi, K., & Narasimhan, B. (2013). Extraction and esrimation of chlorophyll from medicinal plants. International Journal of Science and Research, 4(11), 209-212.

  • Samli, R., Nuket, S., Selcuk, S., & Vildan, Z. K. (2014). Applying artificial neural networks for the estimation of chlorophyll-a concentrations along the Instanbul Coast. Polish Journal of Environmental Studies, 23(4), 1281-1287.

  • Schober, P., Christa, B., & Lothar, A. S. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768.

  • Setti, S., & Anjar, W. (2018). Analysis of backpropagation algorithm in predicting number of internet users in the world. Jurnal Online Informatika, 3(2), 110-115.

  • Shakeri, M., Mohammad, M. A., Nasima, S., Mamun, M., & Syedul, M. A. (2012). Advanced cmos based image sensors. Australian Journal of Basic and Applied Sciences, 6(7), 62-72.

  • Shorten, C., & Taghi, M. K. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(60), 1-48.

  • Uddling, J. Alfredsson, J. G., Piikki, K., & Pleijel, H. (2007). Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynthesis Research, 91(1), 37-46.

  • Wang S., Tang J., & Liu H. (2016). Feature selection. In C.Sammut & G. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (pp. 1-9). Springer.

  • Widiastuti, M. L., Aris, H., Endah, R. P., & Satriyas, I. (2018). Digital image analysis using flatbed scanning system for purity testing of rice seed and confirmation by grow out test. Indonesian Journal of Agricultural Science, 19(2), 49-56.

  • Xu, Y., & Royston, G. (2018). On splitting training and validation set: a comparative study of cross validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. Journal of Analysis and Testing, 2, 249-262.

  • Yadav, S., Yasuomi, I., & Snehasish, D. G. (2010). Estimation of the chlorophyll content of micropropagated potato plants using RGB based image analysis. Plant Cell Tissue and Organ Culture, 100, 183-188.

ISSN 0128-7680

e-ISSN 2231-8526

Article ID


Download Full Article PDF

Share this article

Recent Articles