Measurement and analysis of plant leaf area based on image processing

2012 ◽  
Author(s):  
Wang Jingwen ◽  
Liu Hong
Keyword(s):  
Image Processing ◽  
Leaf Area ◽  
Plant Leaf ◽  
Measurement And Analysis

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.

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 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 Development of software for plant leaf area estimation

2019 ◽  
Vol 390 ◽  
pp. 012029 ◽  
Author(s):  
Mikhail Astashev ◽  
Olga Beloshapkina ◽  
Andrey Kvitko ◽  
Alexey Matasov ◽  
Roman Zakharyan ◽  
...  
Keyword(s):  
Leaf Area ◽  
Plant Leaf ◽  
Area Estimation

scholarly journals PHOSPHATE AND POTASSIUM FERTILIZATION FOR RADISH GROWN IN A LATOSOL WITH A HIGH CONTENT OF THESE NUTRIENTS

2017 ◽  
Vol 30 (2) ◽  
pp. 412-419 ◽  
Author(s):  
ARTHUR BERNARDES CECÍLIO FILHO ◽  
ALEXSON FILGUEIRAS DUTRA ◽  
GILSON SILVERIO DA SILVA
Keyword(s):  
Ground Water ◽  
Factorial Design ◽  
Leaf Area ◽  
Crop Yields ◽  
Dry Mass ◽  
Plant Leaf ◽  
Potassium Fertilization ◽  
Chemical Fertilization ◽  
Number Of Leaves ◽  
Better Than

ABSTRACT The intensive cultivation of vegetables with frequent chemical fertilization may cause accumulation of nutrients in the soil. This, in turn, may reduce crop yields and damage the environment due to contamination of ground water and rivers. Thus, to increase the effects of P (0, 100, 200, 300 and 400 kg ha -1 of P2O5) and K (0, 60, 120, 180 and 240 kg ha-1 of K2O) doses on the growth and productivity of radish cultivars (Sakata 19 and Sakata 25) in a soil with high levels of these nutrients, two experiments were conducted in randomized blocks with the factors cultivars and doses arranged in a 2 x 5 factorial design with three replications. Number of leaves per plant, leaf area, shoot and root dry mass, total and commercial productivity, percentage of cracked roots and P and K contents in the plant and in the soil were evaluated. The Sakata 19 cultivar performed better than the Sakata 25 in both experiments. The fertilization with P or K did not influence the growth and the productivity of both radish cultivars. Therefore, both cultivars of radish evaluated do not need to be fertilized with P and K when planted in a Latosol with high levels of these nutrients.

scholarly journals Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies

2016 ◽  
Vol 123 ◽  
pp. 195-202 ◽  
Author(s):  
Marco Mora ◽  
Felipe Avila ◽  
Marcos Carrasco-Benavides ◽  
Gonzalo Maldonado ◽  
Jeissy Olguín-Cáceres ◽  
...  
Keyword(s):  
Image Processing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Cover ◽  
Fruit Trees ◽  
Area Index ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Detection of plant leaf disease using digital image processing

2020 ◽  
Author(s):  
Ramesh Kumar Mojjada ◽  
K. Kiran Kumar ◽  
Arvind Yadav ◽  
B.V.V. Satya Vara Prasad
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Plant Leaf ◽  
Leaf Disease

scholarly journals Yield, Antioxidant Components, Oil Content, and Composition of Onion Seeds Are Influenced by Planting Time and Density

Plants ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 293 ◽  
Author(s):  
Carmine Amalfitano ◽  
Nadezhda A. Golubkina ◽  
Laura Del Vacchio ◽  
Giuseppe Russo ◽  
Mario Cannoniero ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Leaf Area ◽  
Seed Yield ◽  
Oil Content ◽  
Seed Oil ◽  
Density Increase ◽  
Area Index ◽  
Planting Time ◽  
Plant Leaf ◽  
Crop Cycle

Research was carried out on onion landrace (Ramata di Montoro) for seed production in southern Italy, with the aim to evaluate the effects on yield and quality of four bulb planting times in factorial combination with four densities, using a split plot design with three replicates. The number of flower stalks per plant, their height and diameter, and the inflorescence diameter decreased with the bulb planting delay and density increase. The highest plant leaf area and LAI (leaf area index), seed yield, number, and mean weight were recorded with the earliest planting time, with the lowest bulb density eliciting the highest plant leaf area but the lowest LAI and seed yield per hectare. The ratio between seeds and inflorescence weight, and seed germinability, decreased with the planting delay and density increase. Seed oil, protein, and antioxidant content (polyphenols and selenium) were highest with the last crop cycle. The polyunsaturated fatty acids, predominant in oil, increased with planting time delay, whereas the monounsaturated fatty acids decreased. Linoleic, oleic, and palmitic acid prevailed among polyunsaturated, monounsaturated, and saturated fatty acids, respectively. Planting from 20 December to 10 January with 3.3 cold-stored bulbs per m2 was the most effective combination in terms of seed yield per hectare, whereas seed oil content and quality were the best, with the last crop cycle starting on 21 February, independent of bulb density.

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