Method for estimating leaf coverage in strawberry plants using digital image processing

Sandino, Juan; Ramos-Sandoval, Olga L.; Amaya, Darío

doi:10.1590/1807-1929/agriambi.v20n8p716-721

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…In this image type, the hue channel was used to decide the colour type. The saturation channel represented shades of that colour, and the value channel presented the brightness of the colour [16]. Using HSV images, the green pixels representing the green canopy were classified from each image, as shown in Figure 4.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Using HSV images, the green pixels representing the green canopy were classified from each image, as shown in Figure 4. The colours of an image were differentiated based on Equation ( 1), which returns a vector with the data that defines the resulting image [16]. where dst (I) corresponds to the vector or returned image, 255 is the size of each pixel of the src (I)0 vector or input image, and lowerb (I)0 and upperb (I)0 refer to the lower and upper limits of the HSV colour space, respectively.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Total and canopy area of leaves are calculated by the following methods: image acquisition, image pre-processing, segmentation, region filling, and area calculation. In addition, other researchers apply threshold-based segmentation or contour extraction approaches for leaf region segmentation [16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Measurement of Overlapping Leaf Area of Ice Plants Using Digital Image Processing Technique

et al. 2022

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Non-destructive and destructive leaf area estimation are critical in plant physiological and ecological experiments. In modern agriculture, ubiquitous digital cameras and scanners are primarily replacing traditional leaf area measurements. Thus, measuring the leaflet’s dimension is integral in analysing plant photosynthesis and growth. Leaf dimension assessment with image processing is widely used nowadays. In this investigation, we employed an image segmentation algorithm to classify ice plant (Mesembryanthemum crystallinum L.) canopy images with a threshold segmentation technique by a grey colour model and by calculating the degree of green colour in the HSV (hue, saturation, value) model. Notably, the segmentation technique is used to separate suitable surfaces from a defective noisy background. In this work, the canopy area was measured by pixel number statistics relevant to the known reference area. Furthermore, we proposed total leaf area estimation by a destructive method with a computer coordinating area curvimeter, and lastly, we evaluated the overlapping percentage using the total leaf area and canopy area measurements. To assess the overlapping percentage using the proposed algorithm, the curvimeter method experiment was performed on 24 images of ice plants. The obtained results reveal that the overlapping percentage is less than 10%, as evidenced by a difference in the curvimeter and the proposed algorithm’s results with the canopy leaf area approach. Furthermore, the results show a strong correlation between the canopy and total leaf area (R2: 0.99) calculated by our proposed method. This overlapping leaf area finding offers a significant contribution to crop evolution by using computational techniques to make monitoring easier.

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Section: Image Acquisitionmentioning

confidence: 99%

Section: Image Acquisitionmentioning

confidence: 99%

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Measurement of Overlapping Leaf Area of Ice Plants Using Digital Image Processing Technique

et al. 2022

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“…At the canopy level, green reflectance (550 nm), red reflectance (680 nm), VI, and NDVI were highly correlated with N content, with an R 2 of 0.5, 0.6, 0.56, and 0.56, respectively. Sandino et al [127] adopted a basic computer vision method to estimate strawberry leaf coverage from RGB images with an accuracy of 90%. Procedures such as smoothing, dilatation, contour detection, threshold segmentation, and edge detection operations were used.…”

Section: Leaf and Canopy Traitsmentioning

confidence: 99%

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

2021

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Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

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“…The canopy area plays a significant impact in plant photosynthesis, transpiration, and crop growth. In addition to that, it is a key index in plant breeding practices and plant growth rate measurement ( Sandino et al., 2016 ). Therefore, modern agriculture operations focusing on nondestructive accuracy methods for canopy area measurement.…”

Section: Introductionmentioning

confidence: 99%

Influence of different growing media on the growth and development of strawberry plants

Madhavi

Khan

Bhujel

et al. 2021

Heliyon

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The optimal production of strawberries requires the essential nutrients and favourable media for vegetative and reproductive growth. The present study sought to determine the effectiveness of growth parameters and fruit yield of strawberries in different media growing under a greenhouse. To analyze the significant effect for the growth and fruit yield among the growing media, four treatments such as control soil (CS), bio plus compost (T 1 ), the combination of bio plus compost, and synthetic nutrient applied media/integrated media (T 2 ) and synthetic nutrient applied soil media (T 3 ) were assayed. Morphology parameters like plant height, canopy area, fresh weight, dry weight of roots were measured in each stage after eight weeks and sixteen weeks and yield attributing parameter as the number of fruits set per plant and number of fruits per plant were measured at the beginning and end of the reproductive stage eight and sixteen weeks respectively. The effects of growing media for the strawberry plant growth and productivity were analyzed using completely randomized block designs through analyzing the variance with a significance level of p < 0.05. The canopy area of the strawberry plants was calculated using the image processing technique applied in HSV colour space. Correspondingly, the vegetative stage and reproductive stage of T 2 plants attained the maximum plant height of 16.93 AE 0.31 cm and 19.34 AE 0.21 cm, canopy area with 23.02 AE 1.94 cm 2 and 28.78 AE 0.93 cm 2 , fresh weight of 18.00 AE 3.06 g, and 20.15 AE 3.49 g, dry weight of 5.15 AE 1.26 g and 6.66 AE 2.34 g and the number of fruits set per plant 18.83 AE 2.64 and number of fruits per plant 24.17 AE 2.14 followed by T 1 , T 3, and CS respectively. A comparison of the relative growth and fruit yield at the vegetative and reproductive phases of plants T 2 implied better performance. This study demonstrated that bio plus compost with synthetic nutrients act as a better source for the growth and production of strawberries under the greenhouse.

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Method for estimating leaf coverage in strawberry plants using digital image processing

Cited by 8 publications

References 15 publications

Measurement of Overlapping Leaf Area of Ice Plants Using Digital Image Processing Technique

Measurement of Overlapping Leaf Area of Ice Plants Using Digital Image Processing Technique

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

Influence of different growing media on the growth and development of strawberry plants

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