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Journal articles on the topic 'Leaf area estimation'

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Author: Grafiati

Published: 25 May 2024

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1

Fanourakis, Dimitrios, Filippos Kazakos, and Panayiotis A. Nektarios. "Allometric Individual Leaf Area Estimation in Chrysanthemum." Agronomy 11, no. 4 (April 18, 2021): 795. http://dx.doi.org/10.3390/agronomy11040795.

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A model for estimating the area of individual leaves (LA) by employing their dimensions was developed for chrysanthemum. Further hypotheses were tested: (a) LA estimation is improved by considering blade length (Lb) rather than leaf length (L), and (b) a reasonable LA estimation can be attainable by considering L in conjunction to a shape trait, which is cultivar dependent. For the model development, six cultivars were employed (1500 leaves in total), while for model validation, an independent set of nine cultivars was utilized (1125 leaves in total). Several characteristics were digitally assessed in fully expanded leaves which included petiole length, leaf L, width (W), perimeter, shape traits (aspect ratio, circularity, roundness, solidity), together with LA. LA estimation was more accurate by considering both L and W, as compared to a single dimension. A linear model, employing the product of L by W as independent variable, provided the most accurate LA estimation (R2 = 0.84). The model validation indicated a highly significant correlation between computed and measured LA (R2 = 0.88). Replacing L by Lb reasonably predicted LA (R2 = 0.832) but at some expense of accuracy. Contrary to expectation, considering L (or W) and a cultivar-specific shape trait generally led to poor LA estimations.

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2

S, THIMMEGOWDA. "ESTIMATION OF LEAF AREA IN WHEAT GENOTYPES." Madras Agricultural Journal 73, May (1986): 278–80. http://dx.doi.org/10.29321/maj.10.a02268.

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Leaf product cons ants for night wheat genotypes, ware investigated. The test product constants varied significantly 10.729 to 0.709 for flag-leat, 0.737 10 0.864. for other than flag leaf and 0 740 to 0.831 for all leaves) among the genotypes. This indicated that a singla constant for whost cras as such cannot be accepted." However, a lea! product constant of flag leaf only could be used for estimating leaf area in wheat genotypes as the high releationship existed between the actual leal area and the estimated leaf area in all the genotypes as compared to other than flag leaf and all leaves

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3

Cargnelutti Filho, Alberto, Rafael Vieira Pezzini, Ismael Mario Márcio Neu, and Gabriel Elias Dumke. "Estimation of buckwheat leaf area by leaf dimensions." Semina: Ciências Agrárias 42, no. 3Supl1 (April 22, 2021): 1529–48. http://dx.doi.org/10.5433/1679-0359.2021v42n3supl1p1529.

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The objective of this work was to model and identify the best models for estimating the leaf area, determined by digital photos, of buckwheat (Fagopyrum esculentum Moench) of the cultivars IPR91-Baili and IPR92-Altar, as a function of length (L), width (W) or length x width product (LW) of the leaf blade. Ten uniformity trials (blank experiments) were carried out, five with IPR91-Baili cultivar and five with IPR92-Altar cultivar. The trials were performed on five sowing dates. In each trial and cultivar, expanded leaves were collected at random from the lower, middle and upper segments of the plants, totaling 1,815 leaves. In these 1,815 leaves, L and W were measured and the LW of the leaf blade was calculated, which were used as independent variables in the model. The leaf area of each leaf was determined using the digital photo method (Y), which was used as a dependent variable of the model. For each sowing date, cultivar and thirds of the plant, 80% of the leaves (1,452 leaves) were randomly separated for the generation of the models and 20% of the leaves (363 leaves) for the validation of the models of leaf area estimation as a function of linear dimensions. For buckwheat, IPR91-Baili and IPR92-Altar cultivars, the quadratic model (Ŷ = 0.5217 + 0.6581LW + 0.0004LW2, R2 = 0.9590), power model (Ŷ = 0.6809LW1.0037, R2 = 0.9587), linear model (Ŷ = 0.0653 + 0.6892LW, R2 = 0.9587) and linear model without intercept (Ŷ = 0.6907LW, R2 = 0.9587) are indicated for the estimation of leaf area determined by digital photos (Y) based on the LW of the leaf blade (x), and, preferably, the linear model without intercept can be used, due to its greater simplicity.

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4

Silva, Jocélia Rosa da, Arno Bernardo Heldwein, Andressa Janaína Puhl, Adriana Almeida do Amarante, Daniella Moreira Salvadé, Cadmo João Onofre Gregory dos Santos, and Mateus Leonardi. "Leaf Area Estimation in Chamomile." Journal of Agricultural Science 11, no. 2 (January 15, 2019): 429. http://dx.doi.org/10.5539/jas.v11n2p429.

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The knowledge of the variables specific leaf area and leaf area index is important for direct or indirect quantification of plant growth, development and yield. However, there is a lack of these information due to the difficulty in measuring the leaf area of chamomile. Measuring leaf area by direct methods, such as the use of leaf area integrator is a very laborious and time consuming activity because the plant has many leaves and with small size. The use of leaf dry matter is a promising variable for the leaf area estimation. As an important measure to evaluate plant growth, the present study aimed to obtain a model for chamomile leaf area estimation through leaf dry matter. The experiment was conducted in two sowing dates (March 18 and June 30, 2017) at different plant densities (66, 33, 22, 16, 13, 11 and 8 plants m-2). The leaves of chamomile plants were collected in the plant vegetative and reproductive phases. The leaf area determination was performed using the electronic integration method of leaf area. The specific leaf area was 133 cm2 g-1, with no differences between sowing dates, plant densities and phenological phases of plant collection. The leaf area measured with the electronic leaf area integrator exhibited high correlation with chamomile leaf dry matter and the resulting model of leaf area data by the integrator presented optimum performance. This model is indicated for leaf area determination of chamomile when there is availability of leaf dry matter data.

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5

Deng, Yangbo, Kunyong Yu, Xiong Yao, Qiaoya Xie, Yita Hsieh, and Jian Liu. "Estimation of Pinus massoniana Leaf Area USING Terrestrial Laser Scanning." Forests 10, no. 8 (August 6, 2019): 660. http://dx.doi.org/10.3390/f10080660.

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The accurate estimation of leaf area is of great importance for the acquisition of information on the forest canopy structure. Currently, direct harvesting is used to obtain leaf area; however, it is difficult to quickly and effectively extract the leaf area of a forest. Although remote sensing technology can obtain leaf area by using a wide range of leaf area estimates, such technology cannot accurately estimate leaf area at small spatial scales. The purpose of this study is to examine the use of terrestrial laser scanning data to achieve a fast, accurate, and non-destructive estimation of individual tree leaf area. We use terrestrial laser scanning data to obtain 3D point cloud data for individual tree canopies of Pinus massoniana. Using voxel conversion, we develop a model for the number of voxels and canopy leaf area and then apply it to the 3D data. The results show significant positive correlations between reference leaf area and mass (R2 = 0.8603; p < 0.01). Our findings demonstrate that using terrestrial laser point cloud data with a layer thickness of 0.1 m and voxel size of 0.05 m can effectively improve leaf area estimations. We verify the suitability of the voxel-based method for estimating the leaf area of P. massoniana and confirmed the effectiveness of this non-destructive method.

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6

Toebe, M., P. J. Melo, R. R. Souza, A. C. Mello, and F. L. Tartaglia. "Leaf area estimation in triticale by leaf dimensions." Revista Brasileira de Ciências Agrárias - Brazilian Journal of Agricultural Sciences 14, no. 2 (June 30, 2019): 1–9. http://dx.doi.org/10.5039/agraria.v14i2a5656.

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7

K, BALAKRISHNAN, NATARAJARATNAM N, and SUNDARUM K.M. "A RAPID METHOD FOR THE ESTIMATION OF LEAF AREA IN FIELD BEAN." Madras Agricultural Journal 72, November (1985): 633–35. http://dx.doi.org/10.29321/maj.10.a02415.

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The present investigation aimed to establish a relationship between leaf length x leal breadth and leat area in field bean CV. Co. 1. The regression equation fitted against leaf area and the product between terminal leaf length and breadth was Y = 3 09+1.63 (X) (r = 0.9647**), where Y = leaf area (trifoliate leaf) per leaf. X = Length X Breadth of the terminal leaf let of the trifoliate leaf (L x B). The leaf area was also predicted by using the formula A = 1,685 (L x B). A significant correlation (r=0.9630) was also obtained with actual and predicted leaf area by using the above constants. It was found that the predicted leaf area by regression equation was more accurate than by using A= 1685 (L x B) method. This study will be helpful to estimate the leat area in situ without destroying canopy.

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8

Mack, Laura, Filippo Capezzone, Sebastian Munz, Hans-Peter Piepho, Wilhelm Claupein, Tim Phillips, and Simone Graeff-Hönninger. "Nondestructive Leaf Area Estimation for Chia." Agronomy Journal 109, no. 5 (September 2017): 1960–69. http://dx.doi.org/10.2134/agronj2017.03.0149.

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9

Çi̇rak, C., M. Odabaş, A. Ayan, B. Sağlam, and K. Kevseroğlu. "Estimation of leaf area in selectedHypericumspecies." Acta Botanica Hungarica 50, no. 1-2 (March 2008): 81–91. http://dx.doi.org/10.1556/abot.50.2008.1-2.5.

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10

Jiang, Ni, Wanneng Yang, Lingfeng Duan, Guoxing Chen, Wei Fang, Lizhong Xiong, and Qian Liu. "A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images." Journal of Innovative Optical Health Sciences 08, no. 02 (March 2015): 1550002. http://dx.doi.org/10.1142/s1793545815500029.

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Total green leaf area (GLA) is an important trait for agronomic studies. However, existing methods for estimating the GLA of individual rice plants are destructive and labor-intensive. A nondestructive method for estimating the total GLA of individual rice plants based on multi-angle color images is presented. Using projected areas of the plant in images, linear, quadratic, exponential and power regression models for estimating total GLA were evaluated. Tests demonstrated that the side-view projected area had a stronger relationship with the actual total leaf area than the top-projected area. And power models fit better than other models. In addition, the use of multiple side-view images was an efficient method for reducing the estimation error. The inclusion of the top-view projected area as a second predictor provided only a slight improvement of the total leaf area estimation. When the projected areas from multi-angle images were used, the estimated leaf area (ELA) using the power model and the actual leaf area had a high correlation coefficient (R2 > 0.98), and the mean absolute percentage error (MAPE) was about 6%. The method was capable of estimating the total leaf area in a nondestructive, accurate and efficient manner, and it may be used for monitoring rice plant growth.

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11

Mhanna, M. A. "Evaluation of new mathematical models for estimation of single olive leaves area." Agricultural Science and Technology 12, no. 2 (June 2020): 144–47. http://dx.doi.org/10.15547/ast.2020.02.024.

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Abstract. The study was conducted on “Khoderi” olive cultivar planted in Jableh Region-Latakia province, Syria in 2017 in order to evaluate some mathematical models adapted for olive single leaf area estimation. Leaf samples were taken from the middle of one-year branches. Actual areas of the leaves were measured using Adobe Photoshop CS5. Leaf dimensions (length and width) were measured accurately. Coefficients of determination were estimated for the relation between leaf dimensions and the actual area. The best coefficient of determination was between the natural logarithm of the product (leaf length × leaf width) and the natural logarithm of leaf area (R2= 0.962). Linear regression equation of the mentioned relation was fitted and evaluated. The accuracy of the new model (A=e0.9509ln LW – 0.2867) was compared to other models commonly used for olive single leaf area estimation. The comparison showed no significant differences between leaf area obtained by the new model and the actual leaf area values (p=0.01), whereas significant differences were found for the other models. The new model showed the lowest Root Mean Square Error (RMSE) and high efficiency in estimating olive leaf area of “Khoderi” cultivar in two different environments; the same results were obtained for olive cultivar “Picholine” the French. We recommend the new model for olive single leaf area estimation.

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12

Koubouris, Georgios, Dimitris Bouranis, Efraim Vogiatzis, Abdolhossein Rezaei Nejad, Habtamu Giday, Georgios Tsaniklidis, Eleftherios K. Ligoxigakis, Konstantinos Blazakis, Panagiotis Kalaitzis, and Dimitrios Fanourakis. "Leaf area estimation by considering leaf dimensions in olive tree." Scientia Horticulturae 240 (October 2018): 440–45. http://dx.doi.org/10.1016/j.scienta.2018.06.034.

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13

FARGO, W. S., E. L. BONJOUR, and T. L. WAGNER. "AN ESTIMATION EQUATION FOR SQUASH LEAF AREA USING LEAF MEASUREMENTS." Canadian Journal of Plant Science 66, no. 3 (July 1, 1986): 677–82. http://dx.doi.org/10.4141/cjps86-089.

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An equation was developed which may be used to estimate the area of all sizes of developing squash (Cucurbita pepo L.) leaves. The equation uses two leaf measurements (midrib length (ML) and the distance between tertiary lobes (TD)) which may be taken quickly in the laboratory or field without disturbing the host plant. The equation is:[Formula: see text]The equation is applicable in monitoring individual leaf expansion as well as total plant leaf area increase and in examining the dynamics of the plant under various environmental conditions.Key words: Cucurbita pepo L., leaf area, growth, development, leaf expansion

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14

NeSmith, D. Scott. "Nondestructive Leaf Area Estimation of Rabbiteye Blueberries." HortScience 26, no. 10 (October 1991): 1332. http://dx.doi.org/10.21273/hortsci.26.10.1332.

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15

Gugliuzza, G., G. Fascella, M. M. Mammano, and M. Militello. "Non-destructive leaf area estimation inMyrtus communisplants." Acta Horticulturae, no. 1104 (October 2015): 89–94. http://dx.doi.org/10.17660/actahortic.2015.1104.14.

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16

ASADA, Takenori. "Estimation of Leaf Area in Apple Orchards." Journal of the Japanese Society for Horticultural Science 58, no. 1 (1989): 25–29. http://dx.doi.org/10.2503/jjshs.58.25.

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17

Fascella, G., Y. Rouphael, C. Cirillo, M. M. Mammano, A. Pannico, and S. De Pascale. "Allometric model for leaf area estimation inBougainvilleagenotypes." Acta Horticulturae, no. 1215 (October 2018): 449–52. http://dx.doi.org/10.17660/actahortic.2018.1215.81.

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18

Serdar, Ümit, and Hüsnü Demirsoy. "Non-destructive leaf area estimation in chestnut." Scientia Horticulturae 108, no. 2 (April 2006): 227–30. http://dx.doi.org/10.1016/j.scienta.2006.01.025.

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19

Misle, E., B. Kahlaoui, M. Hachicha, and P. Alvarado. "Leaf area estimation in muskmelon by allometry." Photosynthetica 51, no. 4 (December 1, 2013): 613–20. http://dx.doi.org/10.1007/s11099-013-0062-x.

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20

Elings, Anne. "Estimation of Leaf Area in Tropical Maize." Agronomy Journal 92, no. 3 (May 2000): 436–44. http://dx.doi.org/10.2134/agronj2000.923436x.

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21

Potdar, M. V., and K. R. Pawar. "Non-destructive leaf area estimation in banana." Scientia Horticulturae 45, no. 3-4 (January 1991): 251–54. http://dx.doi.org/10.1016/0304-4238(91)90070-f.

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22

Andarini, Yusi Nurmalita, Higa Afza, and Sutoro Sutoro. "Pendugaan Luas Daun Tanaman Talas (Colocasia esculenta)." Jurnal Ilmu Pertanian Indonesia 25, no. 4 (October 27, 2020): 610–17. http://dx.doi.org/10.18343/jipi.25.4.610.

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Estimation of leaf area by using leaf length and leaf width variables could be done without destruction of the leaves from plants and more practical than using the leaf areameter. Surface area is a function of the variable length and width, so the leaf area can be measured based on leaf length and leaf width variables. The purpose of this research is to get the leaf area estimator model with nondestructive method. Taro plants were observed by using 12 accessions/varieties taken from the germplasm collection in Gene Bank Collection of ICABIOGRAD, IAARD. Observations of the length, width, and area of leaf were carried out on 10-12 leaf samples for each accession/variety from taro cultivation which was about 4 months old. The length (P), width (L), and area (Y) of each taro leaf were measured. The estimation of taro leaves area by regression equation was analyzed by using one (P or L) and two (P and L) independent variables. Estimation using two variables, leaf length and width, is better than only use one variable. Taro leaf area (Y) of each leaf can be determined by the equation Y = 0.9462 P x L for ratio of P/L less than 1.10, Y = 0.9109 P x L for ratio of P/L between 1.10-1.19, and Y = 0.8860 P x L for ratio of P/L equal or greater than 1.20. Keywords: model estimation, leaf area, taro

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23

Ribeiro, João E. da S., Ester dos S. Coêlho, Ângela M. dos S. Pessoa, Anna K. S. de Oliveira, Agda M. F. de Oliveira, Aurélio P. Barros Júnior, Vander Mendonça, and Glauber H. de S. Nunes. "Nondestructive method for estimating the leaf area of sapodilla from linear leaf dimensions." Revista Brasileira de Engenharia Agrícola e Ambiental 27, no. 3 (March 2023): 209–15. http://dx.doi.org/10.1590/1807-1929/agriambi.v27n3p209-215.

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ABSTRACT The leaf area is a parameter of fundamental importance in studies on plant growth and physiology. The objective of this study was to build allometric equations for the accurate and fast estimation of sapodilla leaf areas. In total, 250 leaves of different shapes and sizes were collected from sapodilla matrices trees growing at the Universidade Federal Rural do Semi-Árido, Mossoró-RN, Brazil. For each leaf, the length, width, product of length and width (LW), product of length and length, product of width and width, and leaf area were measured. Linear and nonlinear models were used to construct the allometric equations. The best equations were chosen on the basis of the following criteria: the highest coefficient of determination, Pearson’s linear correlation coefficient, and Willmott’s index of agreement; and the lowest Akaike information criterion and root mean square error. It was verified that the models that used the LW value presented the best criteria for estimating the leaf area. Specifically, the equations ŷ = 0.664 × LW1.018 and ŷ = 0.713 × LW, which use LW values, are the most suitable for estimating the leaf area of sapodilla quickly and accurately.

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24

Yamaguchi, Nobuhiko, Hiroshi Okumura, Osamu Fukuda, Wen Liang Yeoh, and Munehiro Tanaka. "Estimating Tomato Plant Leaf Area Using Multiple Images from Different Viewing Angles." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 2 (March 20, 2024): 352–60. http://dx.doi.org/10.20965/jaciii.2024.p0352.

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The estimation of leaf area is an important measure for understanding the growth, development, and productivity of tomato plants. In this study, we focused on the leaf area of a potted tomato plant and proposed methods, namely, NP, D2, and D3, for estimating its leaf area. In the NP method, we used multiple tomato plant images from different viewing angles to reduce the estimation error of the leaf area, whereas in the D2 and D3 methods, we further compensated for the perspective effects. The performances of the proposed methods were experimentally assessed using 40 “Momotaro Peace” tomato plants. The experimental results confirmed that the NP method had a smaller mean absolute percentage error (MAPE) on the test set than the conventional estimation method that uses a single tomato plant image. Likewise, the D2 and D3 methods had a smaller MAPE on the test set than the conventional method that did not compensate for perspective effects.

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25

Hardin, Perry J., and Ryan R. Jensen. "Neural Network Estimation of Urban Leaf Area Index." GIScience & Remote Sensing 42, no. 3 (September 2005): 251–74. http://dx.doi.org/10.2747/1548-1603.42.3.251.

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26

ZANETTI, SAMARA, LAÍS F. M. PEREIRA, MARIA MÁRCIA P. SARTORI, and MARCELO A. SILVA. "Leaf area estimation of cassava from linear dimensions." Anais da Academia Brasileira de Ciências 89, no. 3 (September 2017): 1729–36. http://dx.doi.org/10.1590/0001-376520172016-0475.

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27

Kuusk, Andres, Mait Lang, Ave Kodar, and Allan Sims. "Estimation of Leaf Area Index of Hemiboreal Forests." Open Remote Sensing Journal 6, no. 1 (April 10, 2015): 1–10. http://dx.doi.org/10.2174/1875413901506010001.

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28

Sanchez-de-Miguel, P., P. Junquera, M. De la Fuente, L. Jimenez, R. Linares, P. Baeza, and J. R. Lissarrague. "Estimation of vineyard leaf area by linear regression." Spanish Journal of Agricultural Research 9, no. 1 (March 1, 2011): 202. http://dx.doi.org/10.5424/sjar/20110901-354-10.

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29

Giaccone, M., A. Pannico, P. Scognamiglio, C. M. Rivera, C. Cirillo, Y. Rouphael, S. De Pascale, and B. Basile. "Regression model for leaf area estimation inFicus caricaL." Acta Horticulturae, no. 1173 (October 2017): 163–68. http://dx.doi.org/10.17660/actahortic.2017.1173.27.

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30

Trachta, Mariano Abel, Alencar Zanon Junior, Alexandre Ferigolo Alves, Charles Patrick de Oliveira de Freitas, Nereu Augusto Streck, Paula de Souza Cardoso, Amanda Thirza Lima Santos, et al. "Leaf area estimation with nondestructive method in cassava." Bragantia 79, no. 4 (December 2020): 347–59. http://dx.doi.org/10.1590/1678-4499.20200018.

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31

Serrano, L., J. A. Gamon, and J. Berry. "Estimation of leaf area with an integrating sphere." Tree Physiology 17, no. 8-9 (August 1, 1997): 571–76. http://dx.doi.org/10.1093/treephys/17.8-9.571.

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32

Astashev, Mikhail, Olga Beloshapkina, Andrey Kvitko, Alexey Matasov, Roman Zakharyan, and Inna Bogun. "Development of software for plant leaf area estimation." IOP Conference Series: Earth and Environmental Science 390 (November 24, 2019): 012029. http://dx.doi.org/10.1088/1755-1315/390/1/012029.

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33

Kishore, D. K., K. K. Pramanick, J. K. Verma, and R. Singh. "Non-destructive estimation of apple (MalusdomesticaBorkh.) leaf area." Journal of Horticultural Science and Biotechnology 87, no. 4 (January 2012): 388–90. http://dx.doi.org/10.1080/14620316.2012.11512881.

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34

Ray, Ramesch C., and R. P. Singh. "Leaf area estimation in capsicum (Capsicum annuum L.)." Scientia Horticulturae 39, no. 3 (June 1989): 181–88. http://dx.doi.org/10.1016/0304-4238(89)90131-3.

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35

Silva, Toshik I. da, João E. da S. Ribeiro, Marlon G. Dias, Renata R. P. Cruz, Larissa F. Macêdo, Jackson S. Nóbrega, Giuliana N. B. Sales, Erli P. dos Santos, Franciscleudo B. da Costa, and José A. S. Grossi. "Non-destructive method for estimating chrysanthemum leaf area." Revista Brasileira de Engenharia Agrícola e Ambiental 27, no. 12 (December 2023): 934–40. http://dx.doi.org/10.1590/1807-1929/agriambi.v27n12p934-940.

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ABSTRACT Chrysanthemum (Dendranthema grandiflora) is the second most produced and commercialized ornamental plant in the world. Measuring leaf area through non-destructive methods is fundamental for studies on its growth and production. The estimation of leaf area by linear dimensions of the leaves can be a strategy for this purpose. The objective of this study was to find allometric equations to estimate the leaf area of chrysanthemum. The linear, linear without intercept, quadratic, cubic, power, and exponential regression models were used for the analysis. The choice of equations was based on the highest coefficients of determination. The non-destructive method using allometric models has accuracy for estimating the leaf area (LA) of chrysanthemum from the product between leaf length (L) and leaf width (W). The LA of chrysanthemum can be estimated using the equation ŷ = 0.6611*LW0.9490 (L - leaf length; W - leaf width). This equation will allow researchers and producers to determine leaf area non-destructively.

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36

Lee, Jae Myun, Jae Yeon Jeong, and Hyo Gil Choi. "Estimation of Leaf Area Using Leaf Length, Leaf width, and Lamina Length in Tomato." Journal of Bio-Environment Control 31, no. 4 (October 31, 2022): 325–31. http://dx.doi.org/10.12791/ksbec.2022.31.4.325.

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37

Liu, Shenzhou, Wenzhi Zeng, Lifeng Wu, Guoqing Lei, Haorui Chen, Thomas Gaiser, and Amit Kumar Srivastava. "Simulating the Leaf Area Index of Rice from Multispectral Images." Remote Sensing 13, no. 18 (September 14, 2021): 3663. http://dx.doi.org/10.3390/rs13183663.

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Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

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Pandey, S. K., and Hema Singh. "A Simple, Cost-Effective Method for Leaf Area Estimation." Journal of Botany 2011 (November 3, 2011): 1–6. http://dx.doi.org/10.1155/2011/658240.

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Easy, accurate, inexpensive, and nondestructive methods to determine individual leaf area of plants are a useful tool in physiological and agronomic studies. This paper introduces a cost-effective alternative (called here millimeter graph paper method) for standard electronic leaf area meter, using a millimeter graph paper. Investigations were carried out during August–October, 2009-2010, on 33 species, in the Botanical garden of the Banaras Hindu University at Varanasi, India. Estimates of leaf area were obtained by the equation, leaf area (cm2) = x/y, where x is the weight (g) of the area covered by the leaf outline on a millimeter graph paper, and y is the weight of one cm2 of the same graph paper. These estimates were then compared with destructive measurements obtained through a leaf area meter; the two sets of estimates were significantly and linearly related with each other, and hence the millimeter graph paper method can be used for estimating leaf area in lieu of leaf area meter. The important characteristics of this cost-efficient technique are its easiness and suitability for precise, non-destructive estimates. This model can estimate accurately the leaf area of plants in many experiments without the use of any expensive instruments.

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Gower, Stith T., and John M. Norman. "Rapid Estimation of Leaf Area Index in Conifer and Broad-Leaf Plantations." Ecology 72, no. 5 (October 1991): 1896–900. http://dx.doi.org/10.2307/1940988.

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Pezzini, Rafael Vieira, Alberto Cargnelutti Filho, Bruna Mendonça Alves, Diego Nicolau Follmann, Jéssica Andiara Kleinpaul, Cleiton Antonio Wartha, and Daniela Lixinski Silveira. "Models for leaf area estimation in dwarf pigeon pea by leaf dimensions." Bragantia 77, no. 2 (June 2018): 221–29. http://dx.doi.org/10.1590/1678-4499.2017106.

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Ma, L., F. P. Gardner, and A. Selamat. "Estimation of Leaf Area from Leaf and Total Mass Measurements in Peanut." Crop Science 32, no. 2 (March 1992): 467–71. http://dx.doi.org/10.2135/cropsci1992.0011183x003200020036x.

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YAMAMOTO, Haruhiko, Kazushige SOGAWA, Tomonari WATANABE, and Hiroya HIGUCHI. "An Estimation of Leaf Area Damaged by Leaf Eater Using Spectral Reflectivity." Journal of Agricultural Meteorology 47, no. 1 (1991): 15–20. http://dx.doi.org/10.2480/agrmet.47.15.

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Ghadami Firouzabadi, Ali, Mahmoud Raeini-Sarjaz, Ali Shahnazari, and Hamid Zareabyaneh. "Non-destructive estimation of sunflower leaf area and leaf area index under different water regime managements." Archives of Agronomy and Soil Science 61, no. 10 (February 23, 2015): 1357–67. http://dx.doi.org/10.1080/03650340.2014.1002776.

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Trégoat, Olivier, Nathalie Ollat, Gilbert Grenier, and Cornelis Van Leeuwen. "Survey of the accuracy and rapidity of several methods for vine leaf area assessment." OENO One 35, no. 1 (March 31, 2001): 31. http://dx.doi.org/10.20870/oeno-one.2001.35.1.993.

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<p style="text-align: justify;">Vine leaf area is an important viticultural parameter. Because its assessment is difficult, leaf area is not frequently taken into account. In this survey, several techniques of vine leaf area estimation are compared for their accuracy and rapidity. Leaf area is highly correlated with leaf blade fresh or dry weight. This method is destructive, because all the leaves have to be removed, and thus it can only be applied after harvest. Leaf area can also be estimated by the measurement of the lenght of two upper lateral veins. This method is precise, but very time consuming. Sample size reduction to one leaf out of four measured does not affect the quality of the estimation, though sample reduction to one leaf out of ten does. For accurate assessment, samples must be taken from multiple shoots. The third method utilizes the correlation between leaf area and shoot length. This technique combines precision and rapidity, though a standard curve should be established for each cultivar and stage of vine development. The percentage of light extinction through the vegetation can be measured by means of an L.A.I. 2000 device. Values are closely correlated to leaf area index and vine leaf area can be deduced when vine density is known. This method is very rapid (only one to two minutes per vine) but it does not distinguish between the primary and secondary leaf area. Moreover, the L.A.I. 2000 device is very expensive. Digital photographs were taken of the vines studied. Assessment of the percentage of leaf surface area, after binarisation of the image, does not lead to an accurate estimation of vine leaf area. The choice of a technique for vine leaf area estimation among the ones tested, will depend on: a) required precision, b) time availability, c) the need to dispose separately of primary and secondary leaf area, d) the possibility to invest in equipment and e) the possibility to remove leaves.</p>

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Sá, Ludimila Geiciane de, Carlos Juliano Brant Albuquerque, Nermy Ribeiro Valadares, Orlando Gonçalves Brito, Amara Nunes Mota, Ana Clara Gonçalves Fernandes, and Alcinei Mistico Azevedo. "Area estimation of soybean leaves of different shapes with artificial neural networks." Acta Scientiarum. Agronomy 44 (May 24, 2022): e54787. http://dx.doi.org/10.4025/actasciagron.v44i1.54787.

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Leaf area is one of the most commonly used physiological parameters in plant growth analysis because it facilitates the interpretation of factors associated with yield. The different leaf formats related to soybean genotypes can influence the quality of the model fit for the estimation of leaf area. Direct leaf area measurement is difficult and inaccurate, requires expensive equipment, and is labor intensive. This study developed methodologies to estimate soybean leaf area using neural networks and considering different leaf shapes. A field experiment was carried out from February to July 2017. Data were collected from thirty-six cultivars separated into three groups according to the leaf shape. Multilayer perceptrons were developed using 300 leaves per group, of which 70% were used for training and 30% for validation. The most important morphological measures were also tested with Garson’s method. The artificial neural networks were efficient in estimating the soybean leaf area, with coefficients of determination close to 0.90. The left leaflet width and right leaflet length are sufficient to estimate the leaf area. Network 4, trained with leaves from all groups, was the most general and suitable for the prediction of soybean leaf area.

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Hamid, A., and W. Agata. "Estimating leaf area in mungbean (Vigna radiata)." Journal of Agricultural Science 113, no. 2 (October 1989): 165–67. http://dx.doi.org/10.1017/s0021859600086718.

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SUMMARYLinear measurements of the length and maximum width of terminal leaflets of mungbean (Vigna radiata (L.) Wilczek) were made to estimate the whole trifoliate leaf area. The linear measurements were compared with the areas of leaflets and whole trifoliate leaves, determined using a leaf area meter. Five varieties were used in the study. Varieties differed in leaf shape and size, and equations were generated for each of the varieties to estimate the leaf area as a function of the product of the length and maximum width of the terminal leaflets, thus providing a means of nondestructive estimation of leaf area.

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Cregg, Bert M. "Leaf Area Estimation of Mature Foliage of Juniperus." Forest Science 38, no. 1 (February 1, 1992): 61–67. http://dx.doi.org/10.1093/forestscience/38.1.61.

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Abstract The ratio of total surface area to projected leaf area was determined for mature foliage samples collected at three canopy heights from Juniperus virginiana and J. scopulorum from four seed sources grown in southeastern Nebraska. The relation of projected leaf area to leaf dry weight and volume was also determined. Total surface area was estimated to be 3.2 times the projected surface area. This relationship was independent of seed source or crown position. Projected leaf area can be satisfactorily estimated from weight or volume. However, these relationships differed by crown position or seed source. These results indicate that leaf area of mature juniper foliage may be rapidly estimated through measurement of projected surface area. Further, the leaf area of large samples may be estimated by determining the appropriate specific leaf area or surface-to-volume ratios. For. Sci. 38(1):61-67.

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Mela, Débora, Marlon Gomes Dias, Toshik Iarley Da Silva, João Everthon da Silva Ribeiro, Andressa Carmo Pena Martinez, and Affonso Henrique Lima Zuin. "Estimation of Thunbergia grandiflora leaf area from allometric models." Comunicata Scientiae 13 (March 29, 2022): e3722. http://dx.doi.org/10.14295/cs.v13.3722.

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Sky vine (Thunbergia grandiflora Roxb) is a vine with important structural components for forest environments. Studies on growth and development are necessary, because of the environmental and economic importance. The leaf area determination is essential for ecophysiological studies to understand the relationship of the plant with the environment. The objective of this work was to estimate an allometric equation to estimate the leaf area of T. grandiflora from linear dimensions. 200 leaves of different shapes and sizes were collected from adult plants and the length (L), width (W), the product between length and width (LW), and real leaf area (LA) were measured. The linear regression, linear without intercept, quadratic, cubic, power, and exponential models were used to estimate the equations. The criteria for determining the best model were higher determination coefficient (R2), Willmott's agreement index (d), lower Akaike information criterion (AIC), the root of the mean error square (RMSE), and BIAS index closer to zero. The leaf area of T. grandiflora can be estimated satisfactorily by the equation ŷ = 0.58*LW.

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Suárez, Juan Carlos, Fernando Casanoves, and Julio Di Rienzo. "Non-Destructive Estimation of the Leaf Weight and Leaf Area in Common Bean." Agronomy 12, no. 3 (March 16, 2022): 711. http://dx.doi.org/10.3390/agronomy12030711.

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Regression models to predict leaf area and leaf weight in common bean (Phaseolus vulgaris) were fitted using the three leaflets of the leaves. A total of 1504 leaves from 40 genotypes were collected, covering a large range of leaf sizes. Width, length, area, and weight were measured for each leaflet. The total leaf area and weight was obtained by the sum of left, central, and right leaflets. The dataset was randomly divided into training and validation sets. The training set was used for model fitting and selection, and the validation dataset was used to obtain statistics for model prediction ability. The leaf area and leaf weight were modeled using different linear regression models based on the length and width of the leaflet. Polynomial regressions involving both length and width of the leaflet provided very good models to estimate the expected area (R2 = 0.978) and weight (R2 = 0.820) of leaves.

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Dias, Marlon G., Débora Mela, Toshik I. da Silva, João E. da S. Ribeiro, José A. S. Grossi, Affonso H. L. Zuin, Andressa C. P. Martinez, and José G. Barbosa. "Leaf area estimation of Congea tomentosa using a non-destructive method." Revista Brasileira de Engenharia Agrícola e Ambiental 26, no. 10 (October 2022): 729–34. http://dx.doi.org/10.1590/1807-1929/agriambi.v26n10p729-734.

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ABSTRACT Congea tomentosa is a climbing plant suitable for covering arbors, railings, and fences. Leaf area determination is useful in understanding the plant-environment relationship and facilitating agronomic studies on transpiration, water requirement, light interception, and photosynthetic activity. The objective of this study was to obtain an allometric equation to estimate the leaf area of C. tomentosa by measuring the leaf dimensions. Analyses were performed on 200 leaves of different shapes and sizes from 10 randomly chosen adult plants grown under field conditions. The leaf length, leaf width, product length and width, and leaf area were determined. Linear, linear without intercept, quadratic, cubic, power, and exponential regression models were used to estimate the leaf area. The coefficient of determination, Willmott’s concordance index, Akaike information criterion, root mean square error and BIAS index were used to determine the best model. The leaf area of C. tomentosa can be satisfactorily estimated using a non-destructive method that uses measurements of leaf dimensions. The equation ŷ = 0.63 × LW (Leaf: L = length, W = width) estimates the leaf area of C. tomentosa in a practical and fast way, with 99.15% of precision. Estimation of the leaf area of C. tomentosa using statistical models is less expensive and easily accessible to researchers and producers of this plant.

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