Non-destructive estimation of leaf area of durian ( Durio zibethinus ) – An artificial neural network approach

M, Kishor Kumar; R, Senthil Kumar; Sankar,; Sakthivel, T.; Karunakaran, G.; Pc, Tripathi

doi:10.1016/j.scienta.2017.03.028

Cited by 27 publications

(10 citation statements)

References 32 publications

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“…Our results are in agreement with previous studies (Ahmadian -Moghadam, 2012;Dos Santos et al, 2018;Kucukonder et al, 2016;Kumar et al, 2017;Lee et al, 2018;Odabas, Erugun, & Oner, 2013;Sinha, Chowdhury, Saha, & Datta, 2013;Siswantoro & Artadana, 2019;Vazquez-Cruz et al, 2012;Were, Tien, Dick, & Singh, 2015).…”

Section: Resultssupporting

confidence: 94%

“…The results demonstrated that the MLP neural network had higher accuracy than the regression models in leaf area prediction. Kumar et al (2017) estimated the durian (Durio zibethinus Murray) leaf area using the MLP neural network. The geometric parameters of the leaf, including the length and width, were selected as the ANN input, and the leaf area was selected as the MLP network output.…”

Section: Crop Sciencementioning

confidence: 99%

“…In addition, artificial intelligence (AI)-based methods have also been used to estimate geometric properties, color, and leaf texture. These methods include the use of artificial neural network (ANN; Guo, Sun, Wang, & Lin, 2013;Kumar et al, 2017;Shabani, Ghaffary, Sepaskhah, & Kamgar-Haghighi, 2017), neuro-fuzzy network (Amiri & Shabani, 2017), and support vector machine (Liang et al, 2014;Yang, Huang, Wang, & Wang, 2008;Yuan et al, 2017).…”

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confidence: 99%

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Image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro‐fuzzy inference system, and regression methods

et al. 2021

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In this study, the leaf area was estimated by different regression models and new methods of image processing by using artificial intelligence (AI) in bread (Triticum aestivum L.) and durum wheat (Triticum durum L.) and triticale (×Tritosecale Wittm. ex A. Camus) at seedling, booting, and milk development stages. Data on leaf traits in 1,000 plants of breed wheat, triticale, and durum wheat were studied. Among regression models using general data, LA = + √ , LA = + (), and LA = + (∕) models had a R 2 > 90% for all three cultivars (L and W represented length and width of leaves). For wheat bread, models + () and √ × () exhibited a good estimate of the leaf area at all stages of growth, whereas in triticale, the linear models that used the product of √ × and √ were more suitable to estimate leaf areas in booting and milk development. The multilayer perceptron (MLP) neural network modeling indicated that the 'trainlm,' 'trainlm,' and 'traincgb' algorithms with an optimal structure of 2-10-1, 2-3-1, and 2-10-1 had the least amount of the mean square error for bread wheat, triticale, and durum wheat, respectively, for each of the growth stages. Furthermore, based on the adaptive neuro-fuzzy inference system (ANFIS) method, a very accurate estimate of the leaf area was obtained for each of the growth stages. The comparison of different models in the leaf area estimation showed that unlike estimating leaf area by regression, AI-based methods did not depend on plant growth stages. In this study, we suggested the utilization of image processing and artificial intelligence in leaf area estimation. It will accelerate leaf area measurement in the field, by extending these methods and transferring it into smartphone applications.

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Section: Resultssupporting

confidence: 94%

Section: Crop Sciencementioning

confidence: 99%

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confidence: 99%

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Image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro‐fuzzy inference system, and regression methods

et al. 2021

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“…On the other hand, indirect methods are low-cost, simple, fast, and accurate for the in situ estimation of the leaf area, being indicated for studies with multiple evaluations in the same individual without destroying the sample [ 12 ]. One of the indirect (non-destructive) methods most commonly used today is the estimation of leaf area using regression models and allometric equations, with leaf area being the dependent variable and dimensional parameters of leaf blade (e.g., length and width) the independent variables for these analyses [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method

Ribeiro

Coêlho

Oliveira

et al. 2023

Heliyon

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“…For leaf area deduction, the projected outer surface or projected tree row surface (PTRS) is linearly related to the leaf area [43]. In addition, the non-destructive estimation of leaf area based on an artificial neural network approach has also been studied [44,45]. Furthermore, a number of methods have been developed to estimate the leaf angle distributions.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data

Cao

Liu

et al. 2018

Remote Sensing

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Leaf attribute estimation is crucial for understanding photosynthesis, respiration, transpiration, and carbon and nutrient cycling in vegetation and evaluating the biological parameters of plants or forests. Terrestrial laser scanning (TLS) has the capability to provide detailed characterisations of individual trees at both the branch and leaf scales and to extract accurate structural parameters of stems and crowns. In this paper, we developed a computer graphic-based 3D point cloud segmentation approach for accurately and efficiently detecting tree leaves and their morphological features (i.e., leaf area and leaf angle distributions (leaf azimuthal angle and leaf inclination angle)) from single leaves. To this end, we adopted a sphere neighbourhood model with an adaptive radius to extract the central area points of individual leaves with different morphological structures and complex spatial distributions; meanwhile, four auxiliary criteria were defined to ensure the accuracy of the extracted central area points of individual leaf surfaces. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to cluster the central area points of leaves and to obtain the centre point corresponding to each leaf surface. We also achieved segmentation of individual leaf blades using an advanced 3D watershed algorithm based on the extracted centre point of each leaf surface and two morphology-related parameters. Finally, the leaf attributes (leaf area and leaf angle distributions) were calculated and assessed by analysing the segmented single-leaf point cloud. To validate the final results, the actual leaf area, leaf inclination and azimuthal angle data of designated leaves on the experimental trees were manually measured during field activities. In addition, a sensitivity analysis investigated the effect of the parameters in our segmentation algorithm. The results demonstrated that the segmentation accuracy of Ehretia macrophylla (94.0%) was higher than that of crape myrtle (90.6%) and Fatsia japonica (88.8%). The segmentation accuracy of Fatsia japonica was the lowest of the three experimental trees. In addition, the single-leaf area estimation accuracy for Ehretia macrophylla (95.39%) was still the highest among the three experimental trees, and the single-leaf area estimation accuracy for crape myrtle (91.92%) was lower than that for Ehretia macrophylla (95.39%) and Fatsia japonica (92.48%). Third, the method proposed in this paper provided accurate leaf inclination and azimuthal angles for the three experimental trees (Ehretia macrophylla: leaf inclination angle: R 2 = 0.908, RMSE = 6.806° and leaf azimuth angle: R 2 = 0.981, RMSE = 7.680°; crape myrtle: leaf inclination angle: R 2 = 0.901, RMSE = 8.365° and leaf azimuth angle: R 2 = 0.938, RMSE = 7.573°; Fatsia japonica: leaf inclination angle: R 2 = 0.849, RMSE = 6.158° and leaf azimuth angle: R 2 = 0.947, RMSE = 3.946°). The results indicate that the proposed method is effective and operational for providing accurate, detailed information on single leaves and vegetation structure from scanned data. This capability facilitates improvements in applications such as the estimation of leaf area, leaf angle distribution and biomass.

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Non-destructive estimation of leaf area of durian ( Durio zibethinus ) – An artificial neural network approach

Cited by 27 publications

References 32 publications

Image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro‐fuzzy inference system, and regression methods

Image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro‐fuzzy inference system, and regression methods

Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method

Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data

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