Methods of image acquisition and software development for leaf area measurements in pastures

Tech, Adriano Rogério Bruno; Silva, André Luis Céspedes da; Meira, Luiz Antonio; Oliveira, Marcelo Eduardo de; Pereira, Lilian Elgalise Techio

doi:10.1016/j.compag.2018.08.025

Cited by 17 publications

(10 citation statements)

References 13 publications

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“…The measured results were influenced by graphic shape. The calculation of leaf area mostly adopted was the fixed reference method in previous studies [29], and the leaf area was determined by the proportion of pixels between the leaf and the reference object. However, in the actual measurement process, if the reference object was small, the actual size represented by a single pixel would weigh higher, and the reference object would have fewer pixels, and the conversion error of reference object would be larger; if the reference object was large, the actual size of a single pixel would be lower, and the conversion error would be also larger.…”

Section: Discussionmentioning

confidence: 99%

A Plant Leaf Geometric Parameter Measurement System Based on the Android Platform

Liu

Tao

et al. 2019

Sensors

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Automatic and efficient plant leaf geometry parameter measurement offers useful information for plant management. The objective of this study was to develop an efficient and effective leaf geometry parameter measurement system based on the Android phone platform. The Android mobile phone was used to process and measure geometric parameters of the leaf, such as length, width, perimeter, and area. First, initial leaf images were pre-processed by some image algorithms, then distortion calibration was proposed to eliminate image distortion. Next, a method for calculating leaf parameters by using the positive circumscribed rectangle of the leaf as a reference object was proposed to improve the measurement accuracy. The results demonstrated that the test distances from 235 to 260 mm and angles from 0 to 45 degrees had little influence on the leafs’ geometric parameters. Both lab and outdoor measurements of leaf parameters showed that the developed method and the standard method were highly correlated. In addition, for the same leaf, the results of different mobile phone measurements were not significantly different. The leaf geometry parameter measurement system based on the Android phone platform used for this study could produce high accuracy measurements for leaf geometry parameters.

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Section: Discussionmentioning

confidence: 99%

A Plant Leaf Geometric Parameter Measurement System Based on the Android Platform

Liu

Tao

et al. 2019

Sensors

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“…The image collecting table was built using a white matte background (to avoid the interference of brightness on the image), with a fixed support for the cell phone to acquire three images at a height of 23 cm from the base. A black square measuring 1 cm 2 (1 × 1 cm) was placed on the left side in the collecting table, which is used as the reference for the software USPLeaf® to measure the leaf area of the samples (Tech et al, 2018). The digital images were stored as 24 bit colour images with resolution of 800 × 600 pixels and saved in RGB colour space in JPEG format.…”

Section: Methodsmentioning

confidence: 99%

“…(1 Â 1 cm) was placed on the left side in the collecting table, which is used as the reference for the software USPLeaf ® to measure the leaf area of the samples (Tech et al, 2018). The digital images were stored as 24 bit colour images with resolution of 800 Â 600 pixels and saved in RGB colour space in JPEG format.…”

Section: Experimental Evaluationsmentioning

confidence: 99%

Canopy leaf area and leaf mass in the upper stratum of Urochloa hybrid ‘Mavuno’ grass subjected to nitrogen fertilisation

de Mattos,

Matuo,

Herling

et al. 2023

Grass and Forage Science

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Nitrogen (N) inputs are recognised to maximise herbage mass (HM) in tropical perennial grasses, whereas less is clear on their impact on HM distribution and the effects on leaf mass (LM) and leaf area index (LAI) in the upper stratum. This 2 year study, carried out in Pirassununga, Brazil, assessed the HM distribution in the upper (>20 cm) and lower (<20 cm) strata in Urochloa hybrid ‘Mavuno’ grass maintained under similar pre‐ and post‐cutting canopy heights with contrasting N fertilisation rates applied after each cutting (no‐nitrogen, 15, 30, and 45 kg N ha−1). The relevance of specific leaf area (SLA), leaf N concentration (NLeaf), tiller weight (TW) and population density to the LM and LAI of the upper stratum were also examined. Mavuno grass expressed a stable HM < 20 cm (59%–71% during Year I and 66%–80% for Year II), and apparent N fertilisation impacts on HM > 20 cm were verified at specific regrowth cycles during Year II. Mavuno grass pastures expressed plasticity for adjustments on leaf, tiller and population attributes, which were modulated by both climatic conditions and N fertilisation. Under favourable growth conditions during Year I, fertilised pastures were able to sustain higher NLeaf and SLA but associated with lower TW, resulting in maximisation of LAI but not in LM in the upper stratum. During Year II, fertilised pastures expressed higher NLeaf, SLA, number of basal tillers, despite the lowest TW, which resulted in higher LAI and LM in the upper stratum compared with non‐fertilised pastures. Our results highlighted that adjustments on leaf and population attributes within the canopy were driven to maximise the upper stratum LAI, being positively affected by N fertilisation.

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“…25, No. 1, January 2022: 317-328 318 some leaf area meters (planimeter) [24], [25]. However, most of these methods are destructive (they require the excision of leaves, which may damage the growth of plants), and provide off-line performance analysis.…”

Section: Introductionmentioning

confidence: 99%

Digital image processing methods for estimating leaf area of cucumber plants

Ngo

Nguyen

et al. 2022

IJEECS

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Increasingly <span>emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring </span>systems.

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Methods of image acquisition and software development for leaf area measurements in pastures

Cited by 17 publications

References 13 publications

A Plant Leaf Geometric Parameter Measurement System Based on the Android Platform

A Plant Leaf Geometric Parameter Measurement System Based on the Android Platform

Canopy leaf area and leaf mass in the upper stratum of Urochloa hybrid ‘Mavuno’ grass subjected to nitrogen fertilisation

Digital image processing methods for estimating leaf area of cucumber plants

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