Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrix

Ronnie S. Concepcion II, Sandy C. Lauguico, Jonnel D. Alejandrino, Elmer P. Dadios, Edwin Sybingco


Leaf canopy area is a fundamental crop growth characteristic that encompasses the spatial area covered by plants. However, non-destructive and automatic computation of lettuce canopy area is still open research. This study presents a vision-based system with color space thresholding and machine learning models in measuring the photosynthetic productivity of aquaponic lettuce based on canopy area derived from the numerical image textural features of Haralick and gray level co-occurrence matrix (GLCM). Lettuce images on different growth stages with varying photosynthetic pigment intensities and geometrical structures are extracted with contrast, correlation, energy, homogeneity, entropy, variance, and information measure of correlations 1 and 2 features. For multi-band color space thresholding, CIELab bested RGB, HSV, and YCbCr colour spaces in segmenting the lettuce plant with sensitivity and specificity measures of 94.77% and 97.16% respectively. For measuring the lettuce canopy area, RMSE was recorded as 50.23% for fitness function neural network (FFNN), 20.46% for radial basis function neural network (RBFNN), 15.11% for exact radial basic function neural network (RBEFNN) and 13.54% for generalized regression neural network (GRNN). Comparative analysis revealed that the two-hidden layer GRNN model with 0.09 spread value and 240 hidden neurons bested other machine learning models in terms of RMSE without overfitting.


Biosystems engineering; Computational intelligence; Intelligent systems; Lettuce; Machine vision

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Berk, P., Stajnko, D., Belsak, A., & Hocevar, M. (2020). Digital evaluation of leaf area of an individual tree canopy in the apple orchard using the LIDAR measurement system. Computers and Electronics in Agriculture, 169, 105158. crossref

Boulard, T., Roy, J.-C., Pouillard, J.-B., Fatnassi, H., & Grisey, A. (2017). Modelling of micrometeorology, canopy transpiration and photosynthesis in a closed greenhouse using computational fluid dynamics. Biosystems Engineering, 158, 110–133. crossref

Boyd, N. S., Gordon, R., & Martin, R. C. (2002). Relationship between leaf area index and ground cover in potato under different management conditions. Potato Research, 45, 117–129. crossref

Burgos-Artizzu, X. P., Ribeiro, A., Guijarro, M., & Pajares, G. (2011). Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 75(2), 337–346. crossref

Calangian, X. A. P., Gonzales, J. Y. C., Hilario, C. A. N., Lopez, J. M. M., Rulona, B. L. E., Valencia, I. J. C., … Dadios, E. P. (2018). Vision-based canopy area measurements. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1–4). Baguio City, Philippines: IEEE. crossref

Chebrolu, N., Lottes, P., Schaefer, A., Winterhalter, W., Burgard, W., & Stachniss, C. (2017). Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields. International Journal of Robotics Research, 36(10), 1045–1052. crossref

Concepcion, R. S., Lauguico, S. C., Tobias, R. R., Dadios, E. P., Bandala, A. A., & Sybingco, E. (2020). Estimation of Photosynthetic Growth Signature at the Canopy Scale Using New Genetic Algorithm-Modified Visible Band Triangular Greenness Index. 2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS). crossref

Dang, L. M., Hassan, S. I., Suhyeon, I., Sangaiah, A. Kumar, Mehmood, I., Rho, S., … Moon, H. (2018). UAV based wilt detection system via convolutional neural networks. Sustainable Computing: Informatics and Systems, 2018, 100250. crossref

de Luna, R. G., Dadios, E. P., Bandala, A. A., & Vicerra, R. R. P. (2019). Size classification of tomato fruit using thresholding, machine learning and deep learning techniques. AGRIVITA Journal of Agricultural Science, 41(3), 586–596. crossref

Fan, X., Kawamura, K., Guo, W., Xuan, T. D., Lim, J., Yuba, N., … Wang, Z. (2018). A simple visible and near-infrared (V-NIR) camera system for monitoring the leaf area index and growth stage of Italian ryegrass. Computers and Electronics in Agriculture, 144, 314–323. crossref

Fuentes-Pacheco, J., Torres-Olivares, J., Roman-Rangel, E., Cervantes, S., Juarez-Lopez, P., Hermosillo-Valadez, J., & Rendón-Mancha, J. M. (2019). Fig plant segmentation from aerial images using a deep convolutional encoder-decoder network. Remote Sensing, 11(10), 1157. crossref

Ge, Z.-M., Zhou, X., Kellomäki, S., Peltola, H., & Wang, K.-Y. (2011). Climate, canopy conductance and leaf area development controls on evapotranspiration in a boreal coniferous forest over a 10-year period: A united model assessment. Ecological Modelling, 222(9), 1626–1638. crossref

Gertphol, S., Chulaka, P., & Changmai, T. (2018). Predictive models for lettuce quality from internet of things-based hydroponic farm. In 2018 22nd International Computer Science and Engineering Conference (ICSEC) (pp. 1–5). Chiang Mai, TH: IEEE. crossref

Hamuda, E., Mc Ginley, B., Glavin, M., & Jones, E. (2017). Automatic crop detection under field conditions using the HSV colour space and morphological operations. Computers and Electronics in Agriculture, 133, 97–107. crossref

Hang, T., Lu, N., Takagaki, M., & Mao, H. (2019). Leaf area model based on thermal effectiveness and photosynthetically active radiation in lettuce grown in mini-plant factories under different light cycles. Scientia Horticulturae, 252, 113–120. crossref

Haug, S., & Ostermann, J. (2015). A crop/weed field image dataset for the evaluation of computer vision based precision agriculture tasks. In L. Agapito, M. M. Bronstein, & C. Rother (Eds.), Computer Vision - ECCV 2014 Workshops (pp. 105–116). Cham: Springer International Publishing. crossref

Hernández-Hernández, J. L., García-Mateos, G., González-Esquiva, J. M., Escarabajal-Henarejos, D., Ruiz-Canales, A., & Molina-Martínez, J. M. (2016). Optimal color space selection method for plant/soil segmentation in agriculture. Computers and Electronics in Agriculture, 122, 124–132. crossref

Jin, B. O., Kim, C. H., Kim, M. H., Baek, G. Y., Choi, E. G., Moon, B. E., … Kim, H. T. (2013). Estimate of CO2 consumption in lettuce according to the leaf area. In H. Itoh & S. Kuroki (Eds.), The 2013 IFAC Bio-Robotics Conference (pp. 71–74). Sakai, JP: Elsevier. crossref

Jindarat, S., & Wuttidittachotti, P. (2015). Smart farm monitoring using Raspberry Pi and Arduino. In 2015 International Conference on Computer, Communications, and Control Technology (I4CT) (pp. 284–288). Kuching, MY: IEEE. crossref

Kierzkowski, D., Runions, A., Vuolo, F., Strauss, S., Lymbouridou, R., Routier-Kierzkowska, A.-L., … Tsiantis, M. (2019). A growth-based framework for leaf shape development and diversity. Cell, 177(6), 1405–1418. crossref

Lauguico S. C., Concepcion, R. S., Alejandrino, J. D., Tobias, R. R., Macasaet, D. D., & Dadios, E. P. (2020). A comparative analysis of machine learning algorihms modeled from machine vision-based lettuce growth stage classification in smart aquaponics. International Journal of Enivronmental Science and Development, 11(9). crossref

Loresco, P. J., Valenzuela, I., Culaba, A., & Dadios, E. (2019). Viola-jones method of marker detection for scale-invariant calculation of lettuce leaf area. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1–5). Baguio City, Philippines: IEEE. crossref

Meng, R.-Q., Cui, S.-G., Zhang, Y.-L., Wu, X.-L., & He, L. (2018). 2In Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018 (pp. 6590–6594). Shenyang, China: IEEE. crossref

Mortensen, A. K., Bender, A., Whelan, B., Barbour, M. M., Sukkarieh, S., Karstoft, H., & Gislum, R. (2018). Segmentation of lettuce in coloured 3D point clouds for fresh weight estimation. Computers and Electronics in Agriculture, 154, 373–381. crossref

Rahman, S., Duursma, R. A., Muktadir, M. A., Roberts, T. H., & Atwell, B. J. (2018). Leaf canopy architecture determines light interception and carbon gain in wild and domesticated Oryza species. Environmental and Experimental Botany, 155, 672–680. crossref

Saleem, G., Akhtar, M., Ahmed, N., & Qureshi, W. S. (2019). Automated analysis of visual leaf shape features for plant classification. Computers and Electronics in Agriculture, 157, 270–280. crossref

Sinha, S. K., Padalia, H., Dasgupta, A., Verrelst, J., & Rivera, J. P. (2020). Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India. International Journal of Applied Earth Observation and Geoinformation, 86, 102027. crossref

Stoleru, V., Stratulat, C., Teliban, G., Padureanu, S., Patras, A., Burlica, R., … Beniuga, O. (2018). Morphological, physiological and productive indicators of lettuce under non-thermal plasma. In EPE 2018 - Proceedings of the 2018 10th International Conference and Expositions on Electrical And Power Engineering (pp. 0937–0942). Iasi, Romania: IEEE. crossref

Tian, Y.-W., & Wang, X.-J. (2009). Analysis of leaf parameters measurement of cucumber based on image processing. In 2009 WRI World Congress on Software Engineering (WCSE) (pp. 34–37). Xiamen, China: IEEE. crossref

Wang, Y., Jin, G., Shi, B., & Liu, Z. (2019). Empirical models for measuring the leaf area and leaf mass across growing periods in broadleaf species with two life histories. Ecological Indicators, 102, 289–301. crossref

Xie, Q., Huang, W., Liang, D., Chen, P., Wu, C., Yang, G., … Zhang, D. (2014). Leaf area index estimation using vegetation indices derived from airborne hyperspectral images in winter wheat. In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (pp. 3586–3594). China: IEEE. crossref

Zhang, L., Weckler, P., Wang, N., Xiao, D., & Chai, X. (2016). Individual leaf identification from horticultural crop images based on the leaf skeleton. Computers and Electronics in Agriculture, 127, 184–196. crossref

Zou, J., Zhang, Y., Zhang, Y., Bian, Z., Fanourakis, D., Yang, Q., & Li, T. (2019). Morphological and physiological properties of indoor cultivated lettuce in response to additional far-red light. Scientia Horticulturae, 257, 108725. crossref

DOI: http://doi.org/10.17503/agrivita.v42i3.2528

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