##plugins.themes.bootstrap3.article.main##

Utpal Barman

Abstract

This study presents the uprising of leaf chlorophyll estimation from traditional mechanical method to machine learning-based method. Earlier chlorophyll estimation techniques such as Spectrophotometer and Soil Plant Analysis Development (SPAD) meter demand cost, time, labour, skill, and expertise. A small-scale tea farmer may not afford these devices. The present study reports a low-cost digital method to predict the tea leaf chlorophyll using 1-D Convolutional Neural Network (1-D CNN). After capturing the tea leaf images using a digital camera in a natural light condition, a total of 12 different colour features were extracted from tea leaf images. A SPAD was used to estimate the original chlorophyll value of the tea leaves. The paper shows the correlation of original tea leaf chlorophyll with the extracted colour features of the tea leaf images. Apart from 1-D CNN, the Multiple Linear Regression (MLR) and K-Nearest Neighbor (KNN) were also applied to predict the tea leaf chlorophyll and compared their results with the 1-D CNN. The 1-D CNN model outperformed with an accuracy of 81.1%, Mean Absolute Error (MAE) of 3.01, and Root Mean Square Error (RMSE) of 4.18. The investigation system is very simple and cost-effective. It can be used in tea farming as a digital SPAD for faster and accurate leaf chlorophyll estimation in an easy way.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

##plugins.themes.bootstrap3.article.details##

##plugins.themes.bootstrap3.article.details##

Keywords

Chlorophyll, Deep learning, Machine learning, SPAD

References
Agarwal, A. & Gupta, S. D. (2018). Assessment of spinach seedling health status and chlorophyll content by multivariate data analysis and multiple linear regression of leaf image features. Computers and Electronics in Agriculture, 152, 281–289.
Ali, M. M., Al-Ani, A., Eamus, D. & Tan, D. K. (2012). A new image processing based technique to determine chlorophyll in plants. American-Eurasian Journal of Agricultural and Environmental Sciences, 12(10), 1323–1328.
Barman, U. & Choudhury, R. D. (2019). Soil texture classification using multi class support vector machine. Information Processing in Agriculture, 7 (2), 318-322
Barman, U. & Choudhury, R. D. (2020). 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., Choudhury, R. D., Sahu, D. & Barman, G. G. (2020). Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease. Computers and Electronics in Agriculture, 177, 105661.
Barman, U., Sarmah, A., Sahu, D. & Barman, G. G. (2021). Estimation of Tea Leaf Chlorophyll Using MLR, ANN, SVR, and KNN in Natural Light Condition. Proceedings of the International Conference on Computing and Communication Systems: I3CS 2020, NEHU, Shillong, India, 170, 287.
Choi, J.-H., Kim, J., Won, J. & Min, O. (2019). Modelling Chlorophyll-a Concentration using Deep Neural Networks considering Extreme Data Imbalance and Skewness. 2019 21st International Conference on Advanced Communication Technology (ICACT), 631–634.
Dey, A. K., Sharma, M. & Meshram, M. R. (2016). An analysis of leaf chlorophyll measurement method using chlorophyll meter and image processing technique. Procedia Comput. Sci, 85, 286–292.
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. Photosynthetica, 57, 388–398.
Peng, Y. & Wang, Y. (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.
Riccardi, M., Mele, G., Pulvento, C., Lavini, A., d’Andria, R. & Jacobsen, S.-E. (2014). Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components. Photosynthesis Research, 120(3), 263–272.
Rigon, J. P. G., Capuani, S., Fernandes, D. M. & Guimarães, T. M. (2016). A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis. Photosynthetica, 54(4), 559–566.
Vesali, F., Omid, M., Kaleita, A. & Mobli, H. (2015). Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging. Computers and Electronics in Agriculture, 116, 211–220.
Vesali, F., Omid, M., Mobli, H. & Kaleita, A. (2017). Feasibility of using smart phones to estimate chlorophyll content in corn plants. Photosynthetica, 55(4), 603–610.
Yadav, S. P., Ibaraki, Y., & Gupta, S. D. (2010). Estimation of the chlorophyll content of micropropagated potato plants using RGB based image analysis. Plant Cell, Tissue and Organ Culture (PCTOC), 100(2), 183–188.
Citation Format
How to Cite
Barman, U. (2021). Deep Convolutional neural network (CNN) in tea leaf chlorophyll estimation: A new direction of modern tea farming in Assam, India. Journal of Applied and Natural Science, 13(3), 1059 - 1064. https://doi.org/10.31018/jans.v13i3.2892
More Citation Formats:
Section
Research Articles