Comparison of HSV and LAB Color Spaces for Hydroponic Monitoring System

Setyawan, Thomas Agung; Riwinanto, Setyo Aji; Suwarto, Suhendro; Helmy,; Nursyahid, Arif; Nugroho, Ari Sriyanto

doi:10.1109/icitacee.2018.8576956

Cited by 11 publications

(9 citation statements)

References 4 publications

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“…In the ITS, the images were first converted to the HSV (hue, saturation, value) color space using the OpenCV library of Python [42]. After that, the leaf threshold was manually segmented (Figure 2a-c), and the leaf areas (LA, cm 2 ) were calculated from the number of leaf pixels (NLP) (Equation ( 6 During the crop growth period, rice plants in 14 fields were sampled 8 times on the same day of the UAV data collection (June 12, June 21, July 1, July 13, July 20, August 2, August 14, and August 25 in 2020).…”

Section: Gndv Imentioning

confidence: 99%

“…In the ITS, the images were first converted to the HSV (hue, saturation, value) color space using the OpenCV library of Python [42]. After that, the leaf threshold was manually segmented (Figure 2a-c), and the leaf areas (LA, cm 2 ) were calculated from the number of leaf pixels (NLP) (Equation ( 6 After scanning, the leaves were put at 105 • C for 30 min, then dried at 75 • C to a constant weight, and the dry matter of the leaves (DML, g) was weighed.…”

Section: Gndv Imentioning

confidence: 99%

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Simulating the Leaf Area Index of Rice from Multispectral Images

Liu

Zeng

et al. 2021

Remote Sensing

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Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

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Section: Gndv Imentioning

confidence: 99%

“…In the ITS, the images were first converted to the HSV (hue, saturation, value) color space using the OpenCV library of Python [42]. After that, the leaf threshold was manually segmented (Figure 2a-c), and the leaf areas (LA, cm 2 ) were calculated from the number of leaf pixels (NLP) (Equation ( 6 After scanning, the leaves were put at 105 • C for 30 min, then dried at 75 • C to a constant weight, and the dry matter of the leaves (DML, g) was weighed.…”

Section: Gndv Imentioning

confidence: 99%

Simulating the Leaf Area Index of Rice from Multispectral Images

Liu

Zeng

et al. 2021

Remote Sensing

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“…The system can provide and retain the EC value given according to the age of the plant with an error rate of 8.9%. In another research, scientists investigate the most suitable colour space for vegetation image processing in smart hydroponics [97]. The authors [97] show that the HSV method has an average accuracy of 75.33% while the CIELAB method has a greater average accuracy of 78.39%.…”

Section: Artificial Intelligence (Ai) Based Hydroponic Systemmentioning

confidence: 99%

A Survey of Smart Hydroponic Systems

Modu¹,

Adam²,

Aliyu³

et al. 2020

Adv. sci. technol. eng. syst. j.

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The explosion in human population has left researchers scrambling for solutions on how to feed the world. Furthermore, rural-urban immigration has on the one hand left the farms in the rural areas devoid of farmers and on the other hand has left the urban areas over-populated. Hydroponics is a form of agriculture where crops are grown without soil. This technique allows the farms to follow the farmers to the urban area. In addition, the fact that no soil is needed, allows hydroponic system to be stacked vertically (also known as vertical farming) to save space. The final frontier in hydroponics is automation. It will allow one farmer to work more than one job and cultivate more than one farm simultaneously. This paper provides a comprehensive survey on smart hydroponic system developed to date.

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“…The colour detection model is required to calculate the leaf area in the digital image processing technique with the support of a computer. HSV (Hue, Saturation, Value) is an accurate colour model even in non-uniform illumination conditions to determine the colours of an image object which is similar to colours seen by the human eye as reported by Setyawan et al (2018). The objective of this present study was to determine the growth and fruit yield of the strawberry plant in the different growing media such as control soil, bio plus compost, integrated media, and synthetic nutrient applied soil media.…”

Section: Introductionmentioning

confidence: 99%

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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Comparison of HSV and LAB Color Spaces for Hydroponic Monitoring System

Cited by 11 publications

References 4 publications

Simulating the Leaf Area Index of Rice from Multispectral Images

Simulating the Leaf Area Index of Rice from Multispectral Images

A Survey of Smart Hydroponic Systems

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

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