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Estimating Morphological Features of Plant Growth Using Machine Vision

Estimating Morphological Features of Plant Growth Using Machine Vision

Himanshu Gupta, Roop Pahuja
Copyright: © 2019 |Volume: 10 |Issue: 3 |Pages: 24
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781522566748|DOI: 10.4018/IJAEIS.2019070103
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MLA

Gupta, Himanshu, and Roop Pahuja. "Estimating Morphological Features of Plant Growth Using Machine Vision." IJAEIS vol.10, no.3 2019: pp.30-53. http://doi.org/10.4018/IJAEIS.2019070103

APA

Gupta, H. & Pahuja, R. (2019). Estimating Morphological Features of Plant Growth Using Machine Vision. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 10(3), 30-53. http://doi.org/10.4018/IJAEIS.2019070103

Chicago

Gupta, Himanshu, and Roop Pahuja. "Estimating Morphological Features of Plant Growth Using Machine Vision," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 10, no.3: 30-53. http://doi.org/10.4018/IJAEIS.2019070103

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Abstract

Motivated by the fact that human visionary intelligence plays a vital role in guiding many of the agriculture practices, this article represents an effective use of machine vision technology for estimating plant morphological features to ascertain its growth and health conditions. An alternative to traditional, manual and time-consuming testing methods of plant growth parameters, a novel online plant vision system is proposed and developed on the platform of virtual instrumentation. Deployed in real time, the system acquires plant images using digital camera and communicates the raw image to host PC on Wi-Fi network. The dedicated application software with plant user interface, effective image processing and analysis algorithms, loads the plant images, extracts and estimates certain morphological features of the plant such as plant height, leaf area, detection of flower onset and fall foliage. The system was tested and validated under real-time conditions using different plants and leaves. Further, the performance of the system was statistically analysed to show promising results.