Moringa leaf chlorophyll content measurement system based on optimized artificial neural network
Abstract
This research aimed to measure the chlorophyll content of Moringa leaves using machine vision and an optimized artificial neural network (ANN). A total of 480 images were used, 70% as training data and 30% as validation data. Features extraction was used to extract color and textural features. ANN was used as a modeling method, and the filter method was used as a feature selection method to optimize the best ANN input. Sensitivity analysis was done by varying the attribute evaluator in the filter method, as well as the learning function, the activation function, the learning rate, the momentum, the number of hidden layers, and the number of hidden nodes in the ANN. The best ANN structure was 10 input nodes, 30 nodes in the hidden layer 1, 40 nodes in the hidden layer 2, and 1 output node when using a learning rate of 0.1, a momentum of 0.5, the traincgf learning function, a logsig activation function in the hidden layer, and a tansig activation function in the output layer. The correlation coefficient between predicted and real data in the training process was 0.9792 with the training mean square error (MSE) of 0.0100, and the correlation coefficient of the validation process was 0.9794 with the validation MSE of 0.0099.
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Barman, U., and Choudhury, R. D. (2022) ‘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, 34 (6), pp. 2938-2950
Bommert, A., Sun, X., Bischi, B., Rahnenfuhrer, J., and Lang, M. (2020) ‘Benchmark for filter methods for feature selection in high-dimensional classification data’, Computational Statistics & Data Analysis, 143, pp. 106839
Brown, L. A., Williams, O., and Dash, J. (2022) ‘Calibration and characterisation of four chlorophyll meters and transmittance spectroscopy for non-destructive estimation of forest leaf chlorophyll concentration’, Agricultural and Forest Meteorology, 323, pp. 109059
Damayanti, R., Rachma, N., Al Riza, D. F., and Hendrawan, Y. (2021) ‘The prediction of chlorophyll content in african leaves (Vernonia amygdalina Del.) using flatbed scanner and optimised artificial neural network’, Journal of Science & Technology, 29 (4), pp. 2509 - 2530
El-Hack, M. E. A., Alqhtani, A. H., Swelum, A. A., El-Saadony, M. T., Salem, H. M., Babalghith, A. O., Taha, A. E., Ahmed, O., Abdo, M., and El-Tarabily, K. A. (2022) ‘Pharmacological, nutritional and antimicrobial uses of Moringa oleifera Lam. leaves in poultry nutrition: an updated knowledge’, Poultry Science, In Press. Available at https://doi.org/10.1016/j.psj.2022.102031 (Accessed: 27 June 2022)
Fan, Y., Li, J., Guo, Y., Xie, L., and Zhang, G. (2021) ‘Digital image colorimetry on smartphone for chemical analysis: A review digital image colorimetry on smartphone for chemical analysis: A review’, Measurement, 171, pp. 108829
Gupta, S. D., and 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, pp. 520-526
Hendrawan, Y., and Al Riza, D. F. (2016) ‘Machine vision optimization using nature-inspired algorithms to model sunagoke moss water status’, International Journal on Advanced Science, Engineering and Information Technology, 6 (1), pp. 45-57
Hendrawan, Y., Amini, A., Maharani, D. M., and Sutan, S. M. (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), pp. 1317 - 1339
Hendrawan, Y., Fauzi, M. R., Khairunnisa, N. S., Andreane, M., Hartianti, P. O., Halim, T. D., and Umam, C. (2019b) ‘Development of colour co-occurrence matrix (CCM) texture analysis for biosensing’, IOP Conference Series: Earth and Environmental Science, 230, pp. 012022
Hendrawan, Y., Hawa, L. C., and Damayanti, R. (2018) ‘Fish swarm intelligent to optimize real time monitoring of chips drying using machine vision’, IOP Conference Series: Earth and Environmental Science, 131, pp. 012020
Hendrawan, Y., and Murase, H. (2011) ‘Neural-intelligent water drops algorithm to select relevant textural features for developing precision irrigation system using machine vision’, Computers and Electronics in Agriculture, 187, pp. 106272
Hendrawan, Y., Sakti, I. M., Wibisono, Y., Fauzy, M. R., Umam, C., and Sutan, S. M. (2019c) ‘Intelligent precision nitrogen fertilizer application based on speaking plant approach for environmental sustainability’, IOP Conference Series: Earth and Environmental Science, 239, pp. 012027
Hendrawan, Y., Sakti, I. M., Wibisono, Y., Rachmawati, M., and Sutan, S. M. (2018) ‘Image Analysis using Color Co-occurrence Matrix Textural Features for Predicting Nitrogen Content in Spinach’, TELKOMNIKA, 16 (6), pp. 2712-2724
Hendrawan, Y., Widyaningtyas, S.,and Sucipto, S. (2019d) ‘Computer vision for purity, phenol, and pH detection of luwak coffee green bean’, TELKOMNIKA, 17 (6), pp. 3073-3085
Kuang, F., Long, Z., Kuang, D., Liu, X., and Guo, R. (2022) ‘Application of back propagation neural network to the modeling of slump and compressive strength of composite geopolymers’, Computational Materials Science, 206, pp. 111241
Li, J., and Olevano, V. (2022) ‘Bethe-Salpeter equation insights into the photo-absorption function and exciton structure of chlorophyll a and b in light-harvesting complex II’, Journal of Photochemistry and Photobiology B: Biology, 232, pp. 112475
Lysenko, V., Kosolapov, A., Usova, E., Tatosyan, M., Vardunny, T., Dmitriev, P., Rajput, V., Krasnov, V.,and Kunitsina, A. (2021) ‘Chlorophyll fluorescence kinetics and oxygen evolution in Chlorella vulgaris cells: Blue vs. red light’, Journal of Plant Physiology, 258-259, pp. 153392
Mahato, D. K., Kargwal, R., Kamle, M., Sharma, B., Pandhi, S., Mishra, S., Gupta, A., Mahmud, M. M. C., Gupta, M. K., Singha, L. B., and Kumar, P. (2022) ‘Ethnopharmacological properties and Nutraceutical potential of Moringa oleifera’, Phytomedicine Plus, 2 (1), pp. 100168
Mark, H., Eibe, F., Geoffrey, H., Bernhard, P., Peter, R., Ian, H.W. (2009) ‘The WEKA Data Mining Software: An Update’, SIGKDD Explorations, 11 (1)
Mathworks. (2014) MATLAB Release 2014a. http://www.mathworks.com.
Palumbo, M., Pace, B., Cefola, M., Montesano, F.F., Colelli, G., and Attolico, G. (2022) ‘Non-destructive and contactless estimation of chlorophyll and ammonia contents in packaged fresh-cut rocket leaves by a computer vision system’, Postharvest Biology and Technology, 189, pp. 111910
Patricio, I. D., and Rieder, R. (2018) ‘Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review’, Computers and Electronics in Agriculture, 153, pp. 69-81
Qi, H., Wu, Z., Zhang, L., Zhou, J., Jun, Z., and Zhu, B. (2021) ‘Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction’, Computers and Electronics in Agriculture, 187, pp. 106292
Saha, K. K., and Sasse, M. Z. (2022) ‘Estimation of chlorophyll content in banana during shelf life using LiDAR laser scanner’, Postharvest Biology and Technology, 192, pp. 112011
Sharma, K., Kumar, M., Waghmare, R., Suhag, R., Gupta, O. P., Lorenzo, J. M., Prakash, S., Rais, N., Sampathrajan, V., Thappa, C., Anitha, T., Sayed, A. A. S., Abdel-Wahab, B. A., Senapathy, M., Pandiselvam, R., Dey, A., Dhumai, S., Amarowicz, R., and Kennedy, J. F. (2022) ‘Moringa (Moringa oleifera Lam.) polysaccharides: Extraction, characterization, bioactivities, and industrial application’, International Journal of Biological Macromolecules, 209, pp. 763-778
Suo, X. M., Jiang, Y. T., Li, S. K., Wang, K., and Wang, C. T. (2010) ‘Artificial neural network to predict leaf population chlorophyll content from cotton plant images’, Agricultural Sciences in China, 9 (1), pp. 38 - 45
Vesali, F., Omid, M., Kaleita, A., and Mobli, H. (2015) ‘Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging’, Computers and Electronics in Agricultur, 116, pp. 211-220
Wang, G., Zeng, F., Song, P., Sun, B., Wang, Q., and Wang, J. (2022) ‘Effects of reduced chlorophyll content on photosystem functions and photosynthetic electron transport rate in rice leaves’, Journal of Plant Physiologyology, 272, pp. 153669
Wang, Y. Y., Peng, C., Zhang, Y., Wang, Z. R., Chen, Y. M., Dong, J. F., Xiao, M. L., Li, D. L., Li, W., Zou, Q. J., Zhang, K., and Wei, P. (2022) ‘Optimization, identification and bioactivity of flavonoids extracted from Moringa oleifera leaves by deep eutectic solvent’, Food Bioscience, 47, pp. 101687
Yaseen, A. A., and Hajos, M. T. (2022) ‘Evaluation of moringa (Moringa oleifera Lam.) leaf extract on bioactive compounds of lettuce (Lactuca sativa L.) grown under glasshouse environment’, Journal of King Saud University - Science, 34 (4), pp. 101916
Zepka, L. Q., Lopes, E. J., and Roca, M. (2019) ‘Catabolism and bioactive properties of chlorophylls’, Current Opinion in Food Science, 26, pp. 94-100
Zhang, L., Han, W., Niu, Y., Chavez, J. L., Shao, G., and Zhang, H. (2021) ‘Evaluating the sensitivity of water stressed maize chlorophyll and structure based on UAV derived vegetation indices’, Computers and Electronics in Agriculture, 185, pp. 106174
Zhuang, J., Zhou, L., Wang, Y., and Chi, Y. (2021) ‘Nitrogen allocation regulates the relationship between maximum carboxylation rate and chlorophyll content along the vertical gradient of subtropical forest canopy’, Agricultural and Forest Meteorology, 307, pp. 108512
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