Non-destructive determination of leaf area in tomato plants using image processing

1997 ◽  
Vol 72 (2) ◽  
pp. 255-262 ◽  
Author(s):  
E. Nyakwende ◽  
C. J. Paull ◽  
J. G. Atherton
Keyword(s):  
Image Processing ◽  
Leaf Area ◽  
Tomato Plants ◽  
Non Destructive

scholarly journals Digital processing of leaf area of soybeans, subjected to different treatments of seeds

2020 ◽  
Vol 13 (3) ◽  
pp. 24
Author(s):  
M. L. V. Passos ◽  
J. B. C. Souza ◽  
E. A. Silva ◽  
C. A. A. C. Silva ◽  
W. S. Sousa ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Program ◽  
Leaf Area ◽  
Seed Treatment ◽  
Digital Camera ◽  
Digital Processing ◽  
Thiophanate Methyl ◽  
Beneficial Effects ◽  
Non Destructive

Digital image processing, when applied to the study of leaf area, allows the integration of the direct measurement and non-destructive, and thus preserves the integrity of the plant. The objective was the quantification of the leaf area of soybean, cv. FTS Paragominas RR, submitted to different treatments of seed with the use of the computer program ImageJ, and basic presuppositions of image processing. The experiment was conducted at the Center of Agrarian Sciences and Environmental, Federal University of Maranhão, in Chapadinha (MA), in the period from February to June 2018. The seeds of soybean 'Paragominas RR' were submitted to the technique of seed treatment, consisting of three fungicides of the active ingredients, thiophanate methyl + fluazinam, fludioxonil and carbendazim + tiram, an insecticide active ingredient fipronil and the control. The leaf area was analyzed in the growth phase, through the use of digital camera and ImageJ®. The use of the routines in the computer program ImageJ® were effective for the determination of leaf area of the soybean submitted to different treatments of the seed. The thiophanate methyl + fluazinam in the dose 200 mL per 100 kg of seeds showed beneficial effects on growth of the cv. FTS Paragominas RR, as estimated by the leaf area.

scholarly journals A NON-DESTRUCTIVE METHOD FOR ESTIMATING LEAF AREA OF Ceiba glaziovii (Kuntze) K. Schum.

FLORESTA ◽  
2019 ◽  
Vol 50 (1) ◽  
pp. 1063
Author(s):  
João Everthon da Silva Ribeiro ◽  
Francisco Romário Andrade Figueiredo ◽  
Ester Dos Santos Coêlho ◽  
Walter Esfrain Pereira ◽  
Manoel Bandeira de Albuquerque
Keyword(s):  
Leaf Area ◽  
Regression Models ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Linear Measurements ◽  
Maximum Width ◽  
Bias Index ◽  
Different Shapes ◽  
Non Destructive

The determination of leaf area is of fundamental importance in studies involving ecological and ecophysiological aspects of forest species. The objective of this research was to adjust an equation to determine the leaf area of Ceiba glaziovii as a function of linear measurements of leaves. Six hundred healthy leaf limbs were collected in different matrices, with different shapes and sizes, in the Mata do Pau-Ferro State Park, Areia, Paraíba state, Northeast Brazil. The maximum length (L), maximum width (W), product between length and width (L.W), and leaf area of the leaf limbs were calculated. The regression models used to construct equations were: linear, linear without intercept, quadratic, cubic, power and exponential. The criteria for choosing the best equation were based on the coefficient of determination (R²), Akaike information criterion (AIC), root mean square error (RMSE), Willmott concordance index (d) and BIAS index. All the proposed equations satisfactorily estimate the leaf area of C. glaziovii, due to their high determination coefficients (R² ≥ 0.851). The linear model without intercept, using the product between length and width (L.W), presented the best criteria to estimate the leaf area of the species, using the equation 0.4549*LW.

scholarly journals Determination of alfalfa leaf area by non-destructive method

2011 ◽  
Vol 1 (1-4) ◽  
Author(s):  
Silvano Bianco ◽  
Leonardo Bianco de Carvalho ◽  
Matheus Saraiva Bianco
Keyword(s):  
Leaf Area ◽  
Alfalfa Leaf ◽  
Non Destructive

scholarly journals Non-destructive Estimation of Spinach Leaf Area: Image Processing and Artificial Neural Network Based Approach

2020 ◽  
pp. 146-153
Author(s):  
Naveen Kumar Mahanti ◽  
Upendar Konga ◽  
Subir Kumar Chakraborty ◽  
V. Bhushana Babu
Keyword(s):  
Image Processing ◽  
Leaf Area ◽  
Linear Models ◽  
Measurement Techniques ◽  
Area Measurement ◽  
Image Processing Techniques ◽  
Spinach Leaf ◽  
Processing Techniques ◽  
Non Destructive ◽  
Better Than

Leaf area (LA) measurement provides valuable key information in understanding the growth and physiology of a plant. Simple, accurate and non-destructive methods are inevitable for leaf area estimation. These methods are important for physiological and agronomic studies. However, the major limitations of existing leaf area measurement techniques are destructive in nature and time consuming. Therefore, the objective of the present work is to develop ANN and linear regression models along with image processing techniques to estimate spinach leaf area making use of leaf width (LW) and length (LL) and comparison of developed models performance based on the statistical parameters. The spinach leaves were grown under different nitrogen fertilizer doses (0, 50, 100, 150, 200, 250, 300, 350 and 400 kg N/ha). The morphological parameters length (LL), width (LW) and area (LA) of leaves were measured using an image-processing software. The performance LA= -0.66+0.64 (LL × LW) (R2 = 0.98, RMSE = 3.25 cm2) equation was better than the other linear models. The performance of the ANN model (R2 = 0.99, RMSE = 3.10 cm2) was better than all other linear models. Therefore, developed models along with image processing techniques can be used as a non-destructive technique for estimation of spinach leaf area.

scholarly journals Non-destructive measurement for estimating leaf area of Bellis perennis

2017 ◽  
Vol 10 (1) ◽  
pp. 58-64
Author(s):  
Indera Sakti Nasution
Keyword(s):  
Image Processing ◽  
Plant Growth ◽  
Leaf Area ◽  
Color Space ◽  
Morphological Operations ◽  
Connected Component ◽  
Kinect Camera ◽  
Growth Estimation ◽  
Destructive Measurement ◽  
Non Destructive

Non-destructive measurement of approaches of modeling can be very convenient and useful for plant growth estimation. This study, digital image processing was evaluated as a non-destructive technique to estimate leaf area of Bellis perennis. The plant samples were growing in the greenhouse and the images were taken every day using Kinect camera. The proposed method used combination of L*a*b* color space, Otsu’s thresholding, morphological operations and connected component analysis to estimate leaf area of Bellis perennis. L* channel was used to distinguish the leaves and background. Calibration area uses a pot of known area in each image as a scale to calibrate the leaves area. The results show that the algorithm is able to separate leaf pixels from soil or pot backgrounds, and also allow it to be implemented in greenhouse automatically. This algorithm can be used for other plants in assumption that there is not too much leaf overlapped during measurement.

scholarly journals Non-destructive evaluation of the leaf area of garlic crop using mathematical models

2020 ◽  
Vol 36 (5) ◽  
Author(s):  
Felipe Augusto Reis Gonçalves ◽  
Marcelo de Paula Senoski ◽  
Thiago Picinatti Raposo ◽  
Leonardo Angelo de Aquino ◽  
Maria Elisa de Sena Fernandes
Keyword(s):  
Linear Regression ◽  
Mathematical Models ◽  
Leaf Area ◽  
Dry Matter ◽  
Linear Models ◽  
Coefficient Of Determination ◽  
Length Width ◽  
Number Of Leaves ◽  
Non Destructive

Growth measurements such as leaf area (LA) and dry matter (DM) are important in experiments about plants population, fertilization, irrigation and others parameters of cultivation, in garlic crop. The LA and DM are commonly defined as destructive, lengthy and cause loss of plants in the experimental units. The objective of this study was to fit mathematical models using linear models that estimate the leaf area and dry matter of garlic plants - variety Ito. For that, garlic plants were collected at 30, 45, 60, 75, 90, 115 and 120 days after planting. The measurements of width (W), length (L) of leaves, LA, DM, pseudostem diameter (PD), number of leaves per plant (NL) and height (H) were determined in each time. The models were fitted to estimate the LA or DM as function of the variables W, L, L*W, PD and LA. The statistical analysis of the linear regression, coefficient of determination of the linear regression (R2), root mean square error (RMSE), modified concordance index (d1) and the BIAS index were verified to determine the most representative models. It`s possible to estimate the LA and the leaf DM of garlic plants using the variables: length, width, pseudostem diameter and height of plants.

scholarly journals Development of Estimating Method of Leaf Area for Non-destructive Diagnosis of Growth and Development of Tomato Plants

2015 ◽  
Vol 51 ◽  
pp. 11-14
Author(s):  
Beom Seok Seo ◽  
Yang Ho Park ◽  
Du Nam Jeong ◽  
Su Won Seo ◽  
Chan Woo Kim ◽  
...  
Keyword(s):  
Leaf Area ◽  
Growth And Development ◽  
Tomato Plants ◽  
Non Destructive
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