Estimation of leaf area index in cereal crops using red–green images

Kirk, Kristian; Andersen, Hans Jørgen; Thomsen, Anne Belinda; Jørgensen, Johannes Ravn; Jørgensen, Rasmus Nyholm

doi:10.1016/j.biosystemseng.2009.07.001

Cited by 57 publications

(34 citation statements)

References 15 publications

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“…This algorithm was later improved using local homogeneity and morphological operations in Gebhardt and Kaühbauch (2007). Kirk et al (2009) applied a combination of greenness and intensity derived from the red and green spectral bands and compute an automatic threshold for a two-class problem assuming two Gaussian probability density functions associated to soil and vegetation respectively; this procedure requires the previous estimation of an angle to rotate the hypothetical greenness axis. Meyer and Camargo-Neto (2008) applied the automatic Otsu's thresholding method for binarizing ExG and the normalized difference index (NDI), where a comparison was established against the segmentation obtained from ExGR determining that in this last case, a value of zero suffices for the threshold, therefore the Otsu's method is not required.…”

Section: Revision Of Methodsmentioning

confidence: 99%

Automatic segmentation of relevant textures in agricultural images

Guijarro

Pajares

Riomoros

et al. 2011

Computers and Electronics in Agriculture

298

122

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Keywords:Machine vision Image segmentation Texture identification in crops Automatic tasks in agriculture One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. Also the identification of textures belonging to the soil could be useful to know some variables, such as humidity, smoothness or any others. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, some times it is convenient to know not only the soil information and the plants growing in the soil but also additional information supplied by global references based on specific areas. This implies that the images to be processed contain textures of three main types to be identified: green plants, soil and sky if any. This paper proposes a new automatic approach for segmenting these main textures and also to refine the identification of sub-textures inside the main ones. Concerning the green identification, we propose a new approach that exploits the performance of existing strategies by combining them. The combination takes into account the relevance of the information provided by each strategy based on the intensity variability. This makes an important contribution. The combination of thresholding approaches, for segmenting the soil and the sky, makes the second contribution; finally the adjusting of the supervised fuzzy clustering approach for identifying sub-textures automatically, makes the third finding. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processing.

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Section: Revision Of Methodsmentioning

confidence: 99%

Automatic segmentation of relevant textures in agricultural images

Guijarro

Pajares

Riomoros

et al. 2011

Computers and Electronics in Agriculture

298

122

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“…Consequently, these platforms are utilized for the assessment of nurseries and breeder plots [9]. The plot-wise characteristics that are usually targeted for capture are canopy vigour [10,11], canopy height [12], biomass [13], leaf area [14] or ground cover [15].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

et al. 2018

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Abstract:This study evaluates an aerial and ground imaging platform for assessment of canopy development in a wheat field. The dependence of two canopy traits, height and vigour, on fertilizer treatment was observed in a field trial comprised of ten varieties of spring wheat. A custom-built mobile ground platform (MGP) and an unmanned aerial vehicle (UAV) were deployed at the experimental site for standard red, green and blue (RGB) image collection on five occasions. Meanwhile, reference field measurements of canopy height and vigour were manually recorded during the growing season. Canopy level estimates of height and vigour for each variety and treatment were computed by image analysis. The agreement between estimates from each platform and reference measurements was statistically analysed. Estimates of canopy height derived from MGP imagery were more accurate (RMSE = 3.95 cm, R 2 = 0.94) than estimates derived from UAV imagery (RMSE = 6.64 cm, R 2 = 0.85). In contrast, vigour was better estimated using the UAV imagery (RMSE = 0.057, R 2 = 0.57), compared to MGP imagery (RMSE = 0.063, R 2 = 0.42), albeit with a significant fixed and proportional bias. The ability of the platforms to capture differential development of traits as a function of fertilizer treatment was also investigated. Both imaging methodologies observed a higher median canopy height of treated plots compared with untreated plots throughout the season, and a greater median vigour of treated plots compared with untreated plots exhibited in the early growth stages. While the UAV imaging provides a high-throughput method for canopy-level trait determination, the MGP imaging captures subtle canopy structures, potentially useful for fine-grained analyses of plants.

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“…Results showed that the LAI-2000 gave a slightly better correlation (0.78-0.90) with the reference compared with the camera (0.68-0.81). Different authors stressed the importance of the zenith angle to measure the gap fraction and the interest of a zenith angle of 57.5° [4][5]. Several commercial instruments for measuring LAI are cited in literature [1], [2], [6].…”

Section: Introductionmentioning

confidence: 99%

“…The inconvenient of those methods is to be influenced by the chlorophyll content or the soil type and to saturate when the soil is completely covered. Kirk et al proposed a LAI evaluation method for cereals using images acquired by a colour camera from above the canopy [4]. The gap fraction was measured and the LAI was computed by adjusting an exponential canopy model.…”

Section: Introductionmentioning

confidence: 99%

Assessment of plant leaf area measurement by using stereo-vision

Leemans

Dumont

Destain

2013

2013 International Conference on 3D Imaging

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The aim of this study is to develop an alternative measurement for the leaf area index (LAI), an important agronomic parameter for plant growth assessment. A 3D stereo-vision technique was developed to measure both leaf area and corresponding ground area. The leaf area was based on pixel related measurements while the ground area was based on the mean distance from the leaves to the camera. Laboratory and field experiments were undertaken to estimate the accuracy and the precision of the technique. Result showed that, though the leaves-camera distance had to be estimated precisely in order to have accurate measurement, the precision of the LAI evaluation, after regression, was equivalent to the reference measurements, that is to say around 10% of the estimated value. This shows the potential of the 3D measurements compared with tedious reference measurements.Index Terms-LAI, 3D, plant leaf area, ALA.

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Estimation of leaf area index in cereal crops using red–green images

Cited by 57 publications

References 15 publications

Automatic segmentation of relevant textures in agricultural images

Automatic segmentation of relevant textures in agricultural images

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

Assessment of plant leaf area measurement by using stereo-vision

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