Application of the OpenCV library in indoor hydroponic plantations for automatic height assessment of plants

• Autorzy: Pietrzykowski Sławomir Krzysztof , Wymysłowski Artur

Identyfikatory

DOI

10.14313/JAMRIS/2-2022/16

• Języki publikacji: EN

Abstrakty

EN

This paper presents a method for automatically measuring plants’ heights in indoor hydroponic plantations using the OpenCV library and the Python programming language. Using the elaborated algorithm and Raspberry Pi-driven system with an external camera, the growth process of multiple pak choi cabbages (Brassica rapa L. subsp. Chinensis) was observed. The main aim and novelty of the presented research is the elaborated algorithm, which allows for observing the plants’ height in hydroponic stations, where reflective foil is used. Based on the pictures of the hydroponic plantation, the bases of the plants, their reflections, and plants themselves were separated. Finally, the algorithm was used for estimating the plants’ heights. The achieved results were then compared to the results obtained manually. With the help of a ML (Machine Learning) approach, the algorithm will be used in future research to optimize the plants’ growth in indoor hydroponic plantations.

Słowa kluczowe

EN

hydroponics; image analysis; automatics; mechatronic system; opencv; phenotyping

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2022
• Tom: Vol. 16, No. 2
• Strony: 55--63
• Opis fizyczny: Bibliogr. 27 poz., rys.

Twórcy

autor

Pietrzykowski Sławomir Krzysztof

• Faculty of Microsystem Electronics and Photonics, Wroclaw University of Science and Technology, Janiszewskiego Street 11/17, 50-372 Wroclaw, Poland

autor

Wymysłowski Artur

• Faculty of Microsystem Electronics and Photonics, Wroclaw University of Science and Technology, Janiszewskiego Street 11/17, 50-372 Wroclaw, Poland

Bibliografia

• [1] United Nations, “2030 Agenda for Sustainable Development”, available from: https://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E, accessed: 2022-06-21
• [2] Food and Agriculture Organization of the United Nations, “Farming in urban areas can boost food security”, 2005. Available from: https://reliefweb.int/report/world/farming-urban-areas-can-boost-food-security
• [3] Barbosa, Guilherme Lages et al. “Comparison of Land, Water, and Energy Requirements of Lettuce Grown Using Hydroponic vs. Conventional Agricultural Methods”, International journal of environmental research and public health vol. 12, no. 6, 2015, 6879–6891. .DOI:10.3390/ijerph120606879
• [4] Stefan P. et al. “A comparative cost-effectiveness analysis in different tested aquaponic systems”, Agriculture and Agricultural Science Procedia, vol. 10, 2016, 555–565. DOI: 10.1016/j.aaspro.2016.09.034.
• [5] Margaret S. et al. “Building sustainable societies trough vertical soilless farming: A cost-effectiveness analysis on a small-scale non-greenhouse hydroponic system”, Sustainable Cities and Society, vol. 83, 2022, 101882. DOI: 10.1016/j.scs.2022.103923.
• [6] A. Malik et al. “A review on the science of growing crops without soil (soilless culture) – a novel alternative for growing crops”, International Journal of Agriculture and Crop Sciences, vol. 7, 2014, 833–842.
• [7] X. E. Pantazi et al. “Detection of biotic and abiotic stresses in crops by using hierarchical self-organizing classifiers”, Precision agriculture, vol. 18 no. 3, 2017, 383–393. DOI:10.1007/s11119-017-9507-8.
• [8] S. Amatya et al. “Detection of cherry tree branches with full foliage in planar architecture for automated sweet-cherry harvesting”, Biosystems Engineering, vol. 146, 2016, 3–15. DOI: 10.1016/j.biosystemseng.2015.10.003.
• [9] M. Ebrahimi et al. “Vision-based pest detection based on SVM classification method”, Computers and Electronics in Agriculture vol. 137, 2017, 52–58 DOI: 10.1016/j.compag.2017.03.016.
• [10] X.-E. Pantazi et al. “Active learning system for weed species recognition based on hyperspectral sensing”, Biosystems Engineering, vol. 146, 2016, 193–202. DOI: 10.1016/j.biosystemseng.2016.01.014.
• [11] G. L. Grinblat et al. “Deep learning for plant identification using vein morphological patterns”, Computers and Electronics in Agriculture, vol. 127, 2016, 418–424. DOI: 10.1016/j.compag.2016.07.003.
• [12] F. M. Westmoreland et al. “Cannabis lighting: Decreasing blue photon fraction increases yield but efficacy is more important for cost effective production of cannabinoids”, PLOS ONE, vol. 16, no. 3, 2021. DOI: 10.1371/journal.pone.0248988
• [13] V. Rodriguez-Morrison, “Cannabis Yield, Potency, and Leaf Photosynthesis Respond Differently to Increasing Light Levels in an Indoor Environment”, Frontiers in Plant Science, vol. 12, 2021. DOI: 10.3389/fpls.2021.646020
• [14] M. W. Jenkins, “Cannabis sativa L. Response to Narrow Bandwidth UV and the Combination of Blue and Red Light during the Final Stages of Flowering on Leaf Level Gas-Exchange Parameters, Secondary Metabolite Production, and Yield”, Agricultural Sciences vol. 12, no. 12, 2021, 1414–1432. DOI: 10.4236/as.2021.1212090
• [15] M. Moher et al. “High Light Intensities Can Be Used to Grow Healthy and Robust Cannabis Plants During the Vegetative Stage of Indoor Production”, Preprints (2021). DOI: 0.20944/preprints202104.0417.v1
• [16] I. Culjak et al. “A brief introduction to OpenCV”, 2012 Proceedings of the 35th International Convention MIPRO, 2012, 1725–1730.
• [17] SimpleCV, http://www.simplecv.org, accessed: 2021-02-07.
• [18] BoofCV, http://boofcv.org/, accessed: 2021-02-07.
• [19] M. Gehan (Dong) et al. “PlantCV v2.0: Image analysis software for high-throughput plant phenotyping”. PeerJ, vol. 5, 2017. DOI: 10.7287/peerj.preprints.3225.
• [20] M.-T. Pham, T.-J. Cham, “Online learning asymmetric boosted classifiers for object detection”, 2007 IEEE Conference on Computer Vision and Pattern, Minneapolis, USA, 2007, 1–8. DOI: 10.1109/CVPR.2007.383083.
• [21] A. K. Hase et al. “Detection, categorization and suggestion to cure infected plants of tomato and grapes by using OpenCV framework for Android environment”, 2017 2nd International Conference for Convergence in Technology (I2CT), 2017, 956–959. DOI: 10.1109/I2CT.2017.8226270.
• [22] I. Suryawibawa et al. “Herbs recognition based on android using OpenCV, International Journal of Image, Graphics and Signal Processing”, vol. 7, 2015, 1–7. DOI: 10.5815/ijigsp.2015.02.01.
• [23] Y. Chen et al. “Research on pest image processing method based on android thermal infrared lens”, IFAC-PapersOnLine, vol. 51, no. 17, 2018, 173–178.DOI: 10.1016/j.ifacol.2018.08.083.
• [24] P. Hu et al. “Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding”, European Journal of Agronomy, vol. 95, 2018, 24–32. DOI: 10.1016/j.eja.2018.02.004
• [25] MA Hassan et al. “Accuracy assessment of plant height using an unmanned aerial vehicle for quantitative genomic analysis in bread wheat”, Plant Methods vol. 15, 2019, 15–37. DOI: 10.1186/s13007-019-0419-7
• [26] B. Franchetti et al. “Vision Based Modeling of Plants Phenotyping in Vertical Farming under Artificial Lighting”, Sensors, vol. 19, 2019. DOI:10.3390/s19204378
• [27] OpenCV – about, https://opencv.org/about/, accessed: 2021-02-07.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).