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. https://doi.org/10.1016/j.jksuci.2020.01.005

• 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. https://doi.org/10.24327/IJRSR

• 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. https://doi.org/10.1016/j.inpa.2017.07.005

• 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. https://doi.org/10.1016/j.fcr.2004.05.002

• 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. https://doi.org/10.1088/1755-1315/475/1/012012

• 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. https://doi.org/10.5958/0974-360X.2018.00768.0

• 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. https://doi.org/10.1016/j.postharvbio.2005.12.018

• 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. https://doi.org/10.1007/s11627-017-9825-6

• 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. https://doi.org/10.1109/TSMC.1973.4309314

• 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. https://doi.org/10.1016/j.compag.2020.105433

• 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. https://doi.org/10.1088/1755-1315/230/1/012022

• 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. http://dx.doi.org/10.12928/telkomnika.v17i6.12689

• Hendrawan, Y., & Haruhiko, M. (2009). Precision irrigation for sunagoke moss production using intelligent image analysis. Environmental Control in Biology, 47, 21-36. https://doi.org/10.2525/ecb.47.21

• 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. http://dx.doi.org/10.12928/telkomnika.v16i6.10326

• 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. https://doi.org/10.1109/ICIECS.2010.5678413

• 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. https://doi.org/10.18517/ijaseit.9.1.7351

• 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. https://doi.org/10.1007/s00158-015-1246-8

• 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. https://doi.org/10.5120/18729-9963

• 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. https://doi.org/10.21917/IJSC.2014.0122

• 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. https://doi.org/10.1145/3136625

• 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. https://doi.org/10.1016/j.proche.2015.03.032

• 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. https://doi.org/10.1007/s11120-018-0561-5

• Mendoza, R. J. P., Daniel, R., & Luis, D. M. (2018). Distributed reliefF-based feature selection in spark. Knowledge and Information System, 57, 1-20. https://doi.org/10.1007/s10115-017-1145-y

• 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. https://doi.org/10.32615/ps.2019.046

• 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. https://doi.org/10.26656/fr.2017.3(5).306

• 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. https://doi.org/10.1080/24751839.2018.1479932

• 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. https://doi.org/10.1016/j.bjbas.2017.07.007

• 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. https://doi.org/10.2298/PIF1401021P

• 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. https://doi.org/10.1080/10942912.2019.1675692

• Rajalakshmi, K., & Narasimhan, B. (2013). Extraction and esrimation of chlorophyll from medicinal plants. International Journal of Science and Research, 4(11), 209-212. https://doi.org/10.21275/v4i11.nov151021

• 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. https://doi.org/10.1213/ANE.0000000000002864

• 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. https://doi.org/10.15575/join.v3i2.205

• 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. https://doi.org/10.1186/s40537-019-0197-0

• 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. https://doi.org/10.1007/s11120-006-9077-5

• 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. http://dx.doi.org/10.21082/ijas.v19n2.2018.p49-56

• 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. https://doi.org/10.1007/s41664-018-0068-2

• 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. https://doi.org/10.1007/s11240-009-9635-6