Smartphone assist deep neural network model to recognize the high-quality tea using leaf maturity and its effect on leaf chlorophyll
Article Main
Abstract
Immature and tender tea leaves always produce high-quality tea than mature tea leaves. Depending on the maturity and age of the leaf, the colour and texture of the tea leaf are different. The photosynthesis capacity of the tea leaf also changes with the change of leaf maturity. Though the tea farmer plucks, classifies, and recognizes the best tea leaves (immature and tender) by viewing the visual symptoms and position of the leaves, the method is not authentic all time and leads to the overall degradation of the tea quality. The present study presents a smartphone assist tea leaf recognition system by analyzing the colour and texture properties of the tea leaf. The six different colour features and 4 Haralick texture features were extracted in the colour and grey domain of the leaf images. Three types of tea leaves, i.e., mature, immature, and tender, were classified using Deep Neural Network (DNN) with ADAM (Adaptive Moment Estimation) optimizer. With an accuracy of 97%, the DNN outperformed the Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The SVM and KNN reported a total of 94.42% and 95.53% accuracy, respectively. The investigated system using DNN with an average precision and recall value of 98.67 and 98.34, respectively, may detect and classify the tea leaf maturity status. The system also can be used in AI-based tea plucking robotic systems or machines.
Article Details
Article Details
Agri-informatics, ANN, DNN, Leaf maturity, Precision Farming, Smartphone
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.
Barman, U. & Choudhury, R. D. (2019). Soil texture classification using multi class support vector machine. Information Processing in Agriculture.
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.
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. Choudhury, R. D. & Uddin, I. (n.d.). Predication of Soil pH using K mean Segmentation and HSV Color Image Processing.
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.
Baruah, P. (2015). Types of tea, value addition and product diversification of Indian tea. Proceedings of the First International Conference on Tea Science and Development, pp 153-159
Behera, S. K., Rath, A. K. & Sethy, P. K. (2020). Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Information Processing in Agriculture, 8(1), DOI:10.1016/j.inpa.2020.0 5.0 03
Mubin, N. A., Nadarajoo, E., Shafri, H. Z. M. & Hamedianfar, A. (2019). Young and mature oil palm tree detection and counting using convolutional neural network deep learning method. International Journal of Remote Sensing, 40(19), 7500–7515.
Najeeb, T. & Safar, M. (2018). Dates maturity status and classification using image processing. 2018 International Conference on Computing Sciences and Engineering (ICCSE), 1–6.
Pourdarbani, R., Sabzi, S., Kalantari, D., Karimzadeh, R., Ilbeygi, E. & Arribas, J. I. (2020). Automatic non-destructive video estimation of maturation levels in Fuji apple (Malus Malus pumila) fruit in orchard based on colour (Vis) and spectral (NIR) data. Biosystems Engineering, 195, 136–151.
Sood, M., & Singh, P. K. (2020). Hybrid system for detection and classification of plant disease using qualitative texture features aAnalysis. Procedia Computer Science, 167, 1056–1065.
Sudana, O., Bayupati, I. P. A., & Yudiana, D. G. (2020). Classification of maturity level of the mangosteen using the Convolutional neural network (cnn) method. Learning, 135.
Wicaksono, B. A., Novamizanti, L., & Ibrahim, N. (2019). Tea leaf maturity levels based on ycbcr color space and clustering centroid. Journal of Physics: Conference Series, 1367(1), 012028.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) © Author (s)
