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.

Study of Constructed Wetland Plant Growth by Computer Image Processing (CIP)

2013 ◽  
Vol 444-445 ◽  
pp. 1815-1819
Author(s):  
Xiao Liu ◽  
Ting Fang Zi Tang
Keyword(s):  
Image Processing ◽  
Plant Growth ◽  
Constructed Wetland ◽  
Computer Image ◽  
Individual Plant ◽  
Wetland Plant ◽  
Interactive Operation ◽  
Computer Image Processing ◽  
Destructive Measurement ◽  
Non Destructive

This paper developed a kind of non-destructive measurement for the growth process of constructed wetland plant: doing image acquisitions of constructed wetland plant regularly through digital camera imaging; introducing image files to computer through interface, displaying them on the screen of computer; developed constructed wetland plant image and growth information extraction software under Visual C++ compiling integrated environment, users could acquire plant contour (realized by image edge recognition) and plant size (characterized by top projected leaf area) through human-machine interactive operation; designed constructed wetland plant (Phragmites communis) experiment to analyze the biomass change during the non-destructive measurement process. The biomass had good correlation with manual measured individual plant height, which proved that computer image processing based non-destructive measurement can provide reliable predictions for plant growth parameters.

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 Digital image processing methods for estimating leaf area of cucumber plants

2022 ◽  
Vol 25 (1) ◽  
pp. 317
Author(s):  
Uoc Quang Ngo ◽  
Duong Tri Ngo ◽  
Hoc Thai Nguyen ◽  
Thanh Dang Bui
Keyword(s):  
Plant Growth ◽  
Leaf Area ◽  
Negative Impact ◽  
High Accuracy ◽  
Growth Monitoring ◽  
Accuracy Rate ◽  
Outdoor Scenes ◽  
Artificial Intelligence Technology ◽  
Rgb Images ◽  
Non Destructive

Increasingly <span>emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring </span>systems.

scholarly journals Non-Destructive Measurement of Three-Dimensional Plants Based on Point Cloud

Plants ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 571 ◽  
Author(s):  
Yawei Wang ◽  
Yifei Chen
Keyword(s):  
Plant Growth ◽  
Point Cloud ◽  
Growth Parameters ◽  
Leaf Shape ◽  
Three Dimensional ◽  
3D Point Cloud ◽  
Surface Integral ◽  
Plant Model ◽  
Destructive Measurement ◽  
Non Destructive

In agriculture, information about the spatial distribution of plant growth is valuable for applications. Quantitative study of the characteristics of plants plays an important role in the plants’ growth and development research, and non-destructive measurement of the height of plants based on machine vision technology is one of the difficulties. We propose a methodology for three-dimensional reconstruction under growing plants by Kinect v2.0 and explored the measure growth parameters based on three-dimensional (3D) point cloud in this paper. The strategy includes three steps—firstly, preprocessing 3D point cloud data, completing the 3D plant registration through point cloud outlier filtering and surface smooth method; secondly, using the locally convex connected patches method to segment the leaves and stem from the plant model; extracting the feature boundary points from the leaf point cloud, and using the contour extraction algorithm to get the feature boundary lines; finally, calculating the length, width of the leaf by Euclidean distance, and the area of the leaf by surface integral method, measuring the height of plant using the vertical distance technology. The results show that the automatic extraction scheme of plant information is effective and the measurement accuracy meets the need of measurement standard. The established 3D plant model is the key to study the whole plant information, which reduces the inaccuracy of occlusion to the description of leaf shape and conducive to the study of the real plant growth status.

Practical methods for non-destructive measurement of tree leaf area

2007 ◽  
Vol 71 (2) ◽  
pp. 99-108 ◽  
Author(s):  
Céline Leroy ◽  
Laurent Saint-André ◽  
Daniel Auclair
Keyword(s):  
Leaf Area ◽  
Tree Leaf ◽  
Destructive Measurement ◽  
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.

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 Identifikasi Nilai Konstanta Bentuk Daun untuk Pengukuran Luas Daun Metode Panjang Kali Lebar pada Tanaman Hortikultura di Tanah Gambut

2015 ◽  
Vol 14 (2) ◽  
pp. 139-146 ◽  
Author(s):  
Djoko Eko Hadi Susilo
Keyword(s):  
Plant Growth ◽  
Leaf Area ◽  
Low Cost ◽  
Peat Soil ◽  
Leaf Shape ◽  
Area Measurement ◽  
Leaf Lamina ◽  
Graph Paper ◽  
Non Destructive ◽  
Destructive Methods

The purpose of this research is to know and identification constanta value of leaf shape for leaf area measurement using length cross width of leaf of horticulture plant in peat soil. This research was conducted from February to May 2015 in Palangka Raya City, Central Kalimantan. This research implemented by observed of leaf from 32 species of horticulture plant in peat soil. In every species, was observed 30 leaf (lamina). Measuring leaf area absolutelly using on grid paper or millimeter graph paper, and than measuring ratio if leaf area is can finding using length cross width of leaf. Result of this research, showed that 32 species of horticulture plant in peat soil have regularity of leaf shape and can identified of constanta value for leaf area measurement using length cross width of leaf.Leaf area measurement using length cross width of leaf is alternative technique because easier (simple), quick (fast), low cost, and accurate to plant growth analysis for non-destructive methods. Leaf area measurement not explain plant growth only, but many purposes was can resulted from it. This research suggested to identification of constanta value of leaf shape for another species horticulture plant in peat soil cultivation.

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