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

Islam, Sheikh Md. Rabiul; Reza, Nasim; Chowdhury, Mohammad Ashraful Ferdous; Islam, Mahfuzul; Ali, Md. Yousuf; Kiraga, Shafik; Chung, Sungwook

doi:10.1088/1755-1315/924/1/012013

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…The pennywort plant leaves exhibit a better R 2 value of 0.98, indicating a high constancy in reflecting the true measured leaf area. Our proposed method showed a similar coefficient of determination for estimating leaf area from the images as those reported in [24], and compared to different previous studies [29,30], our proposed method showed a higher coefficient of determination for estimating leaf area from the images. The average accuracy of our proposed method was found to be around 96.6%.…”

Section: Leaf Area Estimation Using Image Processingsupporting

confidence: 77%

“…Figure 5 shows the flowchart of the steps involved in the leaf area (LA) measurement using an image processing method. The steps followed were from the article by [24]. First, the RGB plant leaf images were acquired from the camera.…”

Section: Leaf Area (La) Estimation From Imagesmentioning

confidence: 99%

“…In this case, the leaf area (LA) plays a vital role. Farmers collect leaf samples and measure the LA using a ruler, scanner, or LA meter, which are direct but destructive, laborious, erroneous, and time-consuming processes [24]. On the other hand, image-based LA detection is an indirect, rapid, cost-effective, and non-destructive method [25].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Reza,

Chowdhury,

Islam

et al. 2023

Horticulturae

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The leaf is a primary part of a plant, and examining the leaf area is crucial in understanding growth and plant physiology. Accurately estimating leaf area is key to this understanding. This study proposed a methodology for the non-destructive estimation of leaf area in pennywort plants using image processing and an artificial neural network (ANN) model. The image processing method involved a series of steps, including grayscale conversion, histogram equalization, binary masking, and region filling, achieving an accuracy of around 96.6%. The ANN model, trained with 70% of a dataset, exhibited high correlations of 97.1% in training and 96.6% in testing phases, with leaf length and width significantly impacting the model output. A comparative analysis revealed the superior performance of the ANN model over the image processing method, demonstrating higher R2 values (>0.99) and lower errors. Furthermore, it showed the impact of diverse LED light combinations and nutrient levels (electrical conductivity, EC) on pennywort plant growth, indicating that the R70:B30 LED light ratio with nutrient level 2 (2.0 dS·m−1) fostered the most favorable growth for pennywort plants. The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. This approach offers opportunities for future studies, tracking changes in leaf areas under varied growth conditions without harming the plant, thus enhancing precision in research.

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Section: Leaf Area Estimation Using Image Processingsupporting

confidence: 77%

Section: Leaf Area (La) Estimation From Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Reza,

Chowdhury,

Islam

et al. 2023

Horticulturae

View full text Add to dashboard Cite

show abstract

“…For remote accessibility, a virtual network computing (VNC) viewer was implemented on the microcontroller system. This allowed for remote control through a graphical user interface (GUI) display, ensuring automatic initiation of the viewer upon device boot-up, as detailed by Islam et al [49]. VNC, developed in the mid-1990s, is a remote desktop technology with open-source code licensed under the GNU General Public License.…”

Section: Data Processing and Analytical Proceduresmentioning

confidence: 99%

Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image Processing

Islam,

Reza,

Ahmed

et al. 2024

Horticulturae

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Understanding the diverse environmental influences on seedling growth is critical for maximizing yields. The need for a more comprehensive understanding of how various environmental factors affect seedling growth is required. Integrating sensor data and image processing techniques offers a promising approach to accurately detect stress symptoms and uncover hidden patterns, enhancing the comprehension of seedling responses to environmental factors. The objective of this study was to quantify environmental stress symptoms for six seedling varieties using image-extracted feature characteristics. Three sensors were used: an RGB camera for color, shape, and size information; a thermal camera for measuring canopy temperature; and a depth camera for providing seedling height from the image-extracted features. Six seedling varieties were grown under controlled conditions, with variations in temperature, light intensity, nutrients, and water supply, while daily automated imaging was conducted for two weeks. Key seedling features, including leaf area, leaf color, seedling height, and canopy temperature, were derived through image processing techniques. These features were then employed to quantify stress symptoms for each seedling type. The analysis of stress effects on the six seedling varieties revealed distinct responses to environmental stressors. Integration of color, size, and shape parameters established a visual hierarchy: pepper and pak choi seedlings showed a good response, cucumber seedlings showed a milder response, and lettuce and tomato seedlings displayed an intermediate response. Pepper and tomato seedlings exhibited a wide range of growth stress symptoms, at 13.00% to 83.33% and 2.96% to 70.01%, respectively, indicating considerable variability in their reactions to environmental stressors. The suggested classification approach provides valuable groundwork for advancing stress monitoring and enabling growers to optimize environmental conditions.

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3D Assessment of Vine Training Systems Derived from Ground-Based RGB-D Imagery

et al. 2022

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In the field of computer vision, 3D reconstruction of crops plays a crucially important role in agriculture. On-ground assessment of geometrical features of vineyards is of vital importance to generate valuable information that enables producers to take the optimum actions in terms of agricultural management. A training system of vines (Vitis vinifera L.), which involves pruning and a trellis system, results in a particular vine architecture, which is vital throughout the phenological stages. Pruning is required to maintain the vine's health and to keep its productivity under control. The creation of 3D models of vineshoots is of crucial importance for management planning. Volume and structural information can improve pruning systems, which can increase crop yield and improve crop management. In this experiment, an RGB-D camera system, namely Kinect v2, was used to reconstruct 3D vine models, which were used to determine branch volume on eight differentiated vineyard training systems: Lyre, GDC (Geneva Double Curtain), Y-Trellis, Pergola, Single Curtain, Smart Dyson, VSP (Vertical Shoot Positioned), and the head-trained Gobelet. The results were compared with dry biomass ground truth-values. Dense point clouds had a substantial impact on the connection between the actual biomass measurements in four of the training systems (Pergola, Curtain, Smart Dyson and VSP). For the comparison of actual dry biomass and RGB-D volume and its associated 3D points, strong linear fits were obtained. Significant coefficients of determination (R2 = 0.72 to R2 = 0.88) were observed according to the number of points connected to each training system separately, and the results revealed good correlations with actual biomass and volume values. When comparing RGB-D volume to weight, Pearson’s correlation coefficient increased to 0.92. The results reveal that the RGB-D approach is also suitable for shoot reconstruction. The research proved how an inexpensive optical sensor can be employed for rapid and reproducible 3D reconstruction of vine vegetation that can improve cultural practices such as pruning, canopy management and harvest.

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

Cited by 3 publications

References 10 publications

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image Processing

3D Assessment of Vine Training Systems Derived from Ground-Based RGB-D Imagery

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