An Improved Leaf Area Measurement Based on Image Processing

2012 ◽  
Vol 182-183 ◽  
pp. 624-628
Author(s):  
Dian Yuan Han
Keyword(s):  
Image Processing ◽  
Leaf Area ◽  
Good Accuracy ◽  
Area Measurement ◽  
Scanning Method ◽  
Plant Leaf ◽  
Unknown Area ◽  
Leaf Image ◽  
Background Area

This paper concerns the plant leaf area measurement based on improved image processing. Firstly, the referenced rectangle was detected with 2-side scanning method. Then the leaf region was segmented according to 2G-R-B of every pixel with two different thresholds, and by using of dilatation operation, the trimap of leaf image was got. Next the pixels in unknown area were classified to the foreground or background area with improved knockout method and the exact leaf was segmented. Lastly, the leaf area was calculated according to the pixels proportion between leaf region and the referenced rectangle. Experiment results show this method has good accuracy and rapid speed.

scholarly journals 2D In Situ Method for Measuring Plant Leaf Area with Camera Correction and Background Color Calibration

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Li-fen Tu ◽  
Qi Peng ◽  
Chun-sheng Li ◽  
Aiqun Zhang
Keyword(s):  
Leaf Area ◽  
Measurement Accuracy ◽  
Area Measurement ◽  
Background Color ◽  
Correction Algorithm ◽  
Plant Leaf ◽  
Color Calibration ◽  
In Situ Method ◽  
Environmental Adaptability

In order to measure the plant leaf area conveniently and quickly in an indoor laboratory and outdoor field, a set of scaffold leaf area measurement systems was designed and manufactured. A 2D in situ method for measuring plant leaf area with camera correction and background color calibration was proposed. The method integrates three subalgorithms: fast calibration and distortion correction algorithm, background color calibration algorithm, and edge error correction algorithm. At the same time, the Visual Studio 2015 and OpenCV 3.4.0 were used to develop and test the algorithm. In order to verify the measurement speed and environmental adaptability of the system, the test was carried out in the complex light disturbance outdoors, and the results were consistent with those in the room. In order to verify the measurement accuracy of the system, this method was used to measure the standard rectangular gauge block of known area and the real leaf area, respectively, and its data were compared with the data measured by Wanshen LA-S plant image analyzer. The results show that both methods have a good stability, and the algorithm proposed in this paper performs better in measurement accuracy and environmental adaptability.

Soft-Computing-Based Approaches for Plant Leaf Disease Detection

2019 ◽  
pp. 100-113
Author(s):  
Vivek K. Verma ◽  
Tarun Jain
Keyword(s):  
Image Processing ◽  
Leaf Area ◽  
Soft Computing ◽  
Environmental Conditions ◽  
The Other ◽  
Disease Detection ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Disease Occurrence ◽  
Plant Image

The disease occurrence phenomena in plants are season-based which is dependent on the presence of the pathogen, crops, environmental conditions, and varieties grown. Some plant varieties are particularly subject to outbreaks of diseases; on the other hand, some are opposite to them. Huge numbers of diseases are seen on the plant leaves and stems. Diseases management is a challenging task. Generally, diseases are seen on the leaves or stems of the plant. Image processing is the best way for the detection of plant leaf diseases. Different kinds of diseases occur because of the attack of bacteria, fungi, and viruses. The monitoring of leaf area is an important tool in studying physiological capabilities associated with plant boom. Plant disorder is usually an unusual growth or dysfunction of a plant. Sometimes diseases damage the leaves of plants.

scholarly journals Image processing algorithm to estimate ice-plant leaf area from RGB images under different light conditions

2021 ◽  
Vol 924 (1) ◽  
pp. 012013
Author(s):  
S Islam ◽  
M N Reza ◽  
M Chowdhury ◽  
M N Islam ◽  
M Ali ◽  
...  
Keyword(s):  
Image Processing ◽  
Leaf Area ◽  
Light Condition ◽  
Processing Algorithm ◽  
Artificial Light ◽  
Light Conditions ◽  
Image Processing Algorithm ◽  
Ice Plant ◽  
Plant Leaf ◽  
Rgb Images

Abstract The productivity of horticultural crops in an artificial light condition are highly influenced by the structure of plant and the area coverage. Accurate measurement of leaf area is very important for predicting plant water demand and optimal growth. In this paper, we proposed an image processing algorithm to estimate the ice-plant leaf area from the RGB images under the artificial light condition. The images were taken using a digital camera and the RGB images were transformed to grayscale images. A binary masking was applied from a grayscale image by classifying each pixel, belonging to the region of interest from the background. Then the masked images were segmented and the leaf region was filled using region filling technique. Finally, the leaf area was calculated from the number of pixel and using known object area. The experiment was carried out in three different light conditions with same plant variety (Ice-plant, Mesembryanthemum crystallinum). The results showed that the correlation between the actual and measured leaf area was found over 0.97 (R2:0.973) by our proposed method. Different light condition also showed significant impact on plant growth. Our results inspired further research and development of algorithms for the specific applications.

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 DEVELOPMENT OF PLANT LEAF AREA MEASUREMENT APPARATUS

2017 ◽  
Vol 95 (2) ◽  
pp. 851-860
Author(s):  
ABED EL WAHB R. OBAIA ◽  
ABD EL FATAH M. DREES
Keyword(s):  
Leaf Area ◽  
Area Measurement ◽  
Plant Leaf ◽  
Measurement Apparatus
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