Feasibility of using smart phones to estimate chlorophyll content in corn plants

Vesali, Farshad; Omid, Mahmoud; Mobli, Hossein; Kaleita, Amy L.

doi:10.1007/s11099-016-0677-9

Cited by 33 publications

(35 citation statements)

References 23 publications

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“…The coefficient of determination (R 2 ) for the same colour indices in current work (when red LED light passed through leaf) was found to be as 0.3047, 0.8355 and 0.797. The coefficient of determination (R 2 ) of the present study for the linear regression among the R, G and B values with leaf Chl content are also higher than the results of Dey et al (2016), Rigon et al (2016) and Vesali et al (2017). When comparing the Chl a and Chl b in the present study, the estimation of the Chl a was higher than the Chl b.…”

Section: Discussioncontrasting

confidence: 69%

“…Most of the methods used for the determination of the leaf chlorophyll and/or nutrient contents are destructive ones, which are time-consuming and costly (Muñoz-Huerta et al, 2013). Recent studies showed that non-destructive methods (Nicolai et al, 2007) can be used to develop chlorophyll metres, that is, Konica Minolta SPAD-502 ® , for the determination of leaf chlorophyll content and to estimate the nitrogen status of some crops (Scharf et al, 2006;Miao et al, 2009;Vesali et al, 2015;Vesali et al, 2017). Chlorophyll metres use two wavebands, infrared light (at 930 nm) and red light (650 nm) to assess the chlorophyll content (Blackmer et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

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Mathematical models for the estimation of leaf chlorophyll content based on RGB colours of contact imaging with smartphones: A pomegranate example

Ozrecberoglu

Kahramanoğlu

2020

Folia Horticulturae

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The objective of this study was to develop a mathematical model for the non-destructive, fast estimation of the leaf chlorophyll (Chl) content of pomegranate trees. For this reason, contact images of the leaf samples were firstly captured with smartphones and the RGB colours of the images were used for the estimation of the leaf Chl contents. Here, different methods were used for the contact imaging. In the present study, two closed boxes with a small hole (equal to the dimensions of a smartphone camera) on each were formed. Samples were inserted into the hole; and a red LED light and white LED light, separately, were passed through the hole and the leaf. Furthermore, a series of models were tested to best estimate the leaf chlorophyll content of the pomegranate trees by using the RGB colours of contact imaging. Results showed that the use of red LED light sources, instead of white LED light sources, during contact imaging, provides a better estimation of the leaf Chl content. Results also suggest that colour values are highly related to the total weight of the contact imaging area. According to the results obtained, the best estimation of the leaf Chl content (of a given area) is possible by using both the G and B colour values with multiple regression models. It is also found to be important to use the weight of the sampled area for the estimation of the leaf chlorophyll content in mg ∙ g−1.

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Section: Discussioncontrasting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Mathematical models for the estimation of leaf chlorophyll content based on RGB colours of contact imaging with smartphones: A pomegranate example

Ozrecberoglu

Kahramanoğlu

2020

Folia Horticulturae

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“…They can offer valuable, accurate and rapid information, calculations, measurements, or evaluations of agriculture attributes. Numerous studies have used mobile device applications (apps) to assess green crop cover [ 24 ], ground cover [ 25 ], fractional green canopy cover [ 26 ], turf coverage [ 27 ], leaf area index [ 28 , 29 ], or chlorophyll content in corn plants [ 30 ], or citizen science and/or participatory sensing [ 22 , 31 , 32 ]. One advantage of these devices is that they are equipped with digital cameras and processors, which allow users (i.e., farmers and/or crop advisors) to capture images and perform crop residue analysis efficiently.…”

Section: Introductionmentioning

confidence: 99%

Using a Mobile Device “App” and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields

Laamrani

Lara

Berg

et al. 2018

Sensors

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Quantifying the amount of crop residue left in the field after harvest is a key issue for sustainability. Conventional assessment approaches (e.g., line-transect) are labor intensive, time-consuming and costly. Many proximal remote sensing devices and systems have been developed for agricultural applications such as cover crop and residue mapping. For instance, current mobile devices (smartphones & tablets) are usually equipped with digital cameras and global positioning systems and use applications (apps) for in-field data collection and analysis. In this study, we assess the feasibility and strength of a mobile device app developed to estimate crop residue cover. The performance of this novel technique (from here on referred to as “app” method) was compared against two point counting approaches: an established digital photograph-grid method and a new automated residue counting script developed in MATLAB at the University of Guelph. Both photograph-grid and script methods were used to count residue under 100 grid points. Residue percent cover was estimated using the app, script and photograph-grid methods on 54 vertical digital photographs (images of the ground taken from above at a height of 1.5 m) collected from eighteen fields (9 corn and 9 soybean, 3 samples each) located in southern Ontario. Results showed that residue estimates from the app method were in good agreement with those obtained from both photograph–grid and script methods (R2 = 0.86 and 0.84, respectively). This study has found that the app underestimates the residue coverage by −6.3% and −10.8% when compared to the photograph-grid and script methods, respectively. With regards to residue type, soybean has a slightly lower bias than corn (i.e., −5.3% vs. −7.4%). For photos with residue <30%, the app derived residue measurements are within ±5% difference (bias) of both photograph-grid- and script-derived residue measurements. These methods could therefore be used to track the recommended minimum soil residue cover of 30%, implemented to reduce farmland topsoil and nutrient losses that impact water quality. Overall, the app method was found to be a good alternative to the point counting methods, which are more time-consuming.

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“…Currently, the smartphone applications in agriculture mainly focus on specific farming activities, such as water and fertilizer calculation, disease detection and diagnosis, pest and weed control, chlorophyll content estimation [28], and tractor-navigation, etc. The available apps which are ready to use include MyPestGuide (), Di@gnoPlant (), WISE (Water Irrigation Scheduling for Efficient Application) [29], Cotton SmartIrrigation App [30], LCFSS (land consolidation field survey system) [31], among others.…”

Section: Improving Als Observation Based On Existing Sensing Technmentioning

confidence: 99%

eFarm: A Tool for Better Observing Agricultural Land Systems

Shi

Tang

et al. 2017

Sensors

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Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human–land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies.

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Feasibility of using smart phones to estimate chlorophyll content in corn plants

Cited by 33 publications

References 23 publications

Mathematical models for the estimation of leaf chlorophyll content based on RGB colours of contact imaging with smartphones: A pomegranate example

Mathematical models for the estimation of leaf chlorophyll content based on RGB colours of contact imaging with smartphones: A pomegranate example

Using a Mobile Device “App” and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields

eFarm: A Tool for Better Observing Agricultural Land Systems

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