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.

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 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.

Determination of the Delamination Percentage of Compacted Bamboo Guadua Using Extended Field Digital Images Processing

2014 ◽  
Vol 600 ◽  
pp. 15-20 ◽  
Author(s):  
Edgar Torres ◽  
Patricia Luna ◽  
Caori Takeuchi
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Images ◽  
Digital Processing ◽  
Extended Field ◽  
Images Processing ◽  
Digital Images Processing ◽  
Delamination Area

The percentages of delamination of Compacted Bamboo Guadua were calculated using digital images processing. Three processes were done in the development of this project: tests of delamination, digital image pre-processing and digital image processing of the images acquired. The test of delamination followed the ASTM 5824. The digital image pre-processing was supported on the acquisition of sequences of images, doing a sweeping of the samples, and finally the digital processing worked in the generation of panoramas with sequences of images acquired from the sample. Additionally, the total area from the sample was measured digitally, the segmentation and the measurement of delamination area were done, finding the ratio between the delamination area and the total area of the sample, and obtaining the value of percentage of delamination per section. Digitally, the obtained values for samples made with fibers obtained from Stick (Varillón), Top (Sobrebasa), Middle (Basa) parts and mixture of them were 16.97%, 9.96%, 5.96% and 8.64% respectively.

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.

Method for Non-Destructive Measurement of Leaf Area Based on Binocular Vision

2014 ◽  
Vol 577 ◽  
pp. 664-667 ◽  
Author(s):  
Li Zhao ◽  
Li Li Yang ◽  
Shi Gang Cui ◽  
Xing Li Wu ◽  
Fan Liang ◽  
...  
Keyword(s):  
Leaf Area ◽  
Binocular Vision ◽  
Digital Camera ◽  
Area Measurement ◽  
Geometric Distortion ◽  
Digital Cameras ◽  
Two Dimensional Image ◽  
Modern Agriculture ◽  
Destructive Measurement ◽  
Non Destructive

Modern agriculture is developing towards the direction of intelligent. We can realize the nondestructive measurement of plant leaf area by using the digital camera. In this paper, we make the same ratio between the camera’s screen and the background plate to overcome the problem of geometric distortion. Then we use two perpendicular digital cameras from the front and side to collect images respectively for curved leaf. Because there are characteristics of the image grey value mutation on the rage of the vane, we can extract the leaf by image segmentation. The leaf area can be calculated by the statistic of the pixels number according to the projection principle. Experiments show that, the error of leaf area measurement reduces from 13.51% to 5.93% by binocular vision. So this method not only can get the measurement of leaf area data, but also can effectively avoid the two-dimensional image distortion and improve the accuracy of leaf area calculation.

scholarly journals Physiological potential and initial development of soybean plants as a function of seed treatment

2019 ◽  
Vol 49 ◽  
Author(s):  
Luciano Del Bem Junior ◽  
Jonas Leandro Ferrari ◽  
Gustavo Dario ◽  
Yago de Barros Triboni ◽  
Carlos Gilberto Raetano
Keyword(s):  
Leaf Area ◽  
Seed Treatment ◽  
Root Nodules ◽  
Accelerated Aging ◽  
Dry Mass ◽  
Soybean Plant ◽  
Initial Development ◽  
Shoot Height ◽  
Thiophanate Methyl ◽  
Randomized Design

ABSTRACT Seed treatment is a process that helps to control insects and soil-borne pathogens, besides allowing a given crop to reach its maximum production potential and protect its seedlings. This study aimed to evaluate the physiological performance of seeds and the initial development of soybean, as a function of seed treatment. The study was based on a completely randomized design, with five treatments [imidacloprid + thiodicarb (75 + 225 g a.i. 100 kg-1 of seeds); chlorantraniliprole (62.5 g a.i. 100 kg-1 of seeds); cyantraniliprole (72 g a.i. 100 kg-1 of seeds), fipronil + thiophanate-methyl + pyraclostrobin (5 + 45 + 50 g a.i. 100 kg-1 of seeds); and control (seeds without treatment)]. Initially, the physiological quality of the seeds was evaluated by determining the first germination count, final germination and accelerated aging, with four replications. After that, the effect of the seed treatment on the soybean plant development was evaluated by analyzing the leaf area, number of leaves, shoot height, root and shoot dry mass and fresh mass of root nodules, with ten replications. The seed treatment with fipronil + pyraclostrobin + thiophanate-methyl allows an increased germination when the seeds are subjected to the accelerated aging test, besides an increment in the shoot height and leaf area of the plants.

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