Designing and Developing a Machine Vision System to Predict the Chlorophyll and Carotenoid Content of Plant Leaves

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Biabi H.; ABDANAN MEHDIZADEH S.; Nadafzadeh m.; Salehi Salmi M.R.

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Journal: JOURNAL OF AGRICULTURAL MACHINERY Year:2020 | Volume:9 | Issue:2 (18) Page(s): 279-293

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Information Journal Paper

Title

Designing and Developing a Machine Vision System to Predict the Chlorophyll and Carotenoid Content of Plant Leaves

Author(s)

Biabi H. | ABDANAN MEHDIZADEH S. | Nadafzadeh m. | Salehi Salmi M.R. | Issue Writer Certificate

Keywords

CarotenoidQ1

ChlorophyllQ2

Color spaceQ1

Image processingQ1

Abstract

Introduction: Leaf color is usually used as a guide for assessments of nutrient status and plant health. Most of the existing methods that examined relationships between Chlorophyll status and Carotenoid of leaf color were developed for particular species. Different methods have been developed to measure Chlorophyll status and Carotenoid. However, the high cost and difficulty to use have restricted their application, whereas the handheld Chlorophyll meters such as the SPAD has become popular in the last decade for non-destructive measurement of Chlorophyll content. SPAD meter readings have found to be related to the plant’ s nutrition status, seed protein content, types of nodulation, and photosynthetic rates of leaves. Digital color (RGB) image analysis, another nondestructive technique is becoming increasingly popular with its potential in phenotyping various parameters of plant health status. The development of low-cost digital cameras that use charged-couple device (CCD) arrays to capture images offers an advantage of low-cost real-time monitoring process over optical sensor based SPAD meter. Gupta et al. (2012) estimated Chlorophyll content, using simple leaf digital analysis procedure in parallel to a SPAD Chlorophyll content meter. The Chlorophyll content as determined by the SPAD meter was significantly correlated to the RGB values of leaf image analysis (RMSE = 3. 97). The aim of this research is developing a new inexpensive, hand-held and easy-to-use technique for detection of Chlorophyll and Carotenoid content in plants based on leaf color. This method provides rapid analysis and data storage at minimal cost and does not require any technical or laboratory skills...

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APA: Copy

Biabi, H., ABDANAN MEHDIZADEH, S., Nadafzadeh, m., & Salehi Salmi, M.R.. (2020). Designing and Developing a Machine Vision System to Predict the Chlorophyll and Carotenoid Content of Plant Leaves. JOURNAL OF AGRICULTURAL MACHINERY, 9(2 (18) ), 279-293. SID. https://sid.ir/paper/380513/en

Vancouver: Copy

Biabi H., ABDANAN MEHDIZADEH S., Nadafzadeh m., Salehi Salmi M.R.. Designing and Developing a Machine Vision System to Predict the Chlorophyll and Carotenoid Content of Plant Leaves. JOURNAL OF AGRICULTURAL MACHINERY[Internet]. 2020;9(2 (18) ):279-293. Available from: https://sid.ir/paper/380513/en

IEEE: Copy

H. Biabi, S. ABDANAN MEHDIZADEH, m. Nadafzadeh, and M.R. Salehi Salmi, “Designing and Developing a Machine Vision System to Predict the Chlorophyll and Carotenoid Content of Plant Leaves,” JOURNAL OF AGRICULTURAL MACHINERY, vol. 9, no. 2 (18) , pp. 279–293, 2020, [Online]. Available: https://sid.ir/paper/380513/en

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