Use of improved hue, luminance and saturation (IHLS) color space in the estimation of Nitrogen on tomato seedlings (Lycopersicon esculentum)

Mata-Donjuan, Gloria F.; Mercado-Luna, Adán; Rico-García, Enrique; Herrera-Ruíz, Gilberto

doi:10.5897/sre11.966

Cited by 4 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The color (hue) of the plant is captured by the hue channel in the HSV colorspace. The hue of tomato seedlings have previously been found to correlate with nitrogen content ( Mata-Donjuan et al., 2012 ). The change in hue can be visualized using circular histograms of the hue channel.…”

Section: Methodsmentioning

confidence: 99%

“…The mean of the hue distribution has previously been shown to correlate with laboratory measurements of plant nitrogen content (Mata-Donjuan et al, 2012). Direct estimation of nitrogen levels is not possible unless it can be calibrated using a SPAD chlorophyll meter (Turner and Jund, 1994) or in a laboratory.…”

Section: Greenness Measurementsmentioning

confidence: 99%

“…For instance, plant color has previously been used to estimate nitrogen deficiency in legumes ( Wiwart et al., 2009 ). Different approaches using indices based on RGB ( Tewari et al., 2013 ) or estimating euclidean distances using HSL and CIELAB color spaces have been applied ( Wiwart et al., 2009 ), and the mean of the hue component from the HSV/HSL/IHLS color spaces has previously been found to correlate strongly with nitrogen content in tomato seedlings ( Mata-Donjuan et al., 2012 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Greenotyper: Image-Based Plant Phenotyping Using Distributed Computing and Deep Learning

et al. 2020

View full text Add to dashboard Cite

Image-based phenotype data with high temporal resolution offers advantages over end-point measurements in plant quantitative genetics experiments, because growth dynamics can be assessed and analysed for genotype-phenotype association. Recently, network-based camera systems have been deployed as customizable, low-cost phenotyping solutions. Here, we implemented a large, automated image-capture system based on distributed computing using 180 networked Raspberry Pi units that could simultaneously monitor 1,800 white clover ( Trifolium repens ) plants. The camera system proved stable with an average uptime of 96% across all 180 cameras. For analysis of the captured images, we developed the Greenotyper image analysis pipeline. It detected the location of the plants with a bounding box accuracy of 97.98%, and the U-net-based plant segmentation had an intersection over union accuracy of 0.84 and a pixel accuracy of 0.95. We used Greenotyper to analyze a total of 355,027 images, which required 24–36 h. Automated phenotyping using a large number of static cameras and plants thus proved a cost-effective alternative to systems relying on conveyor belts or mobile cameras.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Greenness Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Greenotyper: Image-Based Plant Phenotyping Using Distributed Computing and Deep Learning

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In 2012, Mata-Donjuan [5] proposed that the information of the H component of tomato leaves in the improved HLS (iHLS) has a higher linear relationship with the total nitrogen of the leaves, the coefficient of determination R² is 0.86, and the total nitrogen content of the leaves TN (mg/ g) The relationship with the H component is Equation (1). TN = 0.7922H -32.377 (1) In this paper, the amount of fertilization is determined by the method based on improved HLS (iHLS) color space representation, and the H channel statistics are performed on the image of the segmented area of the nitrogen-deficient leaves and healthy leaves.…”

Section: Fertilization Amount Decisionmentioning

confidence: 99%

Tomato fertilizer deficiency classification and fertilization decision model based on leaf images and deep learning

Jia

Wei

2022

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022)

View full text Add to dashboard Cite

Conventional crop fertilization classification and fertilization decision-making methods are difficult to operate efficiently and accurately. In order to improve the efficiency of greenhouse tomato fertilization and fertilization decision-making, this study is based on more than 1,800 greenhouse tomato leaf pictures, using data enhancement, migration learning, gradient descent, the regularization method optimizes the Convolutional Neural Network (CNN), and builds a deep learning model for tomato leaf fertility classification. The full convolutional neural network (FCN) is optimized using data enhancement and hyperparameter optimization methods, and a deep learning model for tomato leaf region segmentation and region information extraction is constructed. In this paper, the leaf image information collected by the on-site camera is converted into improved color space information, combined with the tomato leaf nitrogen deficiency-color model, to realize the amount of decision of the tomato nitrogen fertilizer at the irrigation site. Research shows that the accuracy of the CNN leaf fertility recognition model training set is close to 100%, the accuracy of the validation set can reach 95%, and the accuracy of the test set is 91%; the average intersection ratio from the test set data of the FCN leaf segmentation model is 0.91 and the average pixel accuracy is 0.94.

show abstract

“…Wiwart et al used the color space HSL and LAB to assess macronutrients (N, P, K, and Mg) in plants using the Euclidean distance [34]. Gloria proved that the improved hue, luminance and saturation (IHLS) color space is a fast and economical method to estimate Nitrogen on tomato seedlings [35]. However, the N content of crops is a typical nonlinear problem.…”

Section: A Digital Imaging For Crop N Estimationmentioning

confidence: 99%

The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery

Zhang¹,

Han²,

Sobeih³

et al. 2021

Preprint

View full text Add to dashboard Cite

Nitrogen (N) fertiliser is routinely applied by farmers to increase crop yields. At present, farmers often over-apply N fertilizer in some locations or timepoints because they do not have high-resolution crop N status data. N-use efficiency can be low, with the remaining N lost to the environment, resulting in high production costs and environmental pollution. Accurate and timely estimation of N status in crops is crucial to improving cropping systems' economic and environmental sustainability. The conventional approaches based on tissue analysis in the laboratory for estimating N status in plants are time consuming and destructive. Recent advances in remote sensing and machine learning have shown promise in addressing the aforementioned challenges in a non-destructive way. We propose a novel deep learning framework: a channel-spatial attention-based vision transformer (CSVT) for estimating crop N status from large images collected from a UAV in a wheat field. Unlike the existing works, the proposed CSVT introduces a Channel Attention Block (CAB) and a Spatial Interaction Block (SIB), which allows capturing nonlinear characteristics of spatial-wise and channel-wise features from UAV digital aerial imagery, for accurate N status prediction in wheat crops. Moreover, since acquiring labeled data is time consuming and costly, local-to-global self-supervised learning is introduced to pre-train the CSVT with extensive unlabelled data. The proposed CSVT has been compared with the state-of-the-art models, tested and validated on both testing and independent datasets. The proposed approach achieved high accuracy (0.96) with good generalizability and reproducibility for wheat N status estimation.

show abstract

Use of improved hue, luminance and saturation (IHLS) color space in the estimation of Nitrogen on tomato seedlings (Lycopersicon esculentum)

Cited by 4 publications

References 6 publications

Greenotyper: Image-Based Plant Phenotyping Using Distributed Computing and Deep Learning

Greenotyper: Image-Based Plant Phenotyping Using Distributed Computing and Deep Learning

Tomato fertilizer deficiency classification and fertilization decision model based on leaf images and deep learning

The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery

Contact Info

Product

Resources

About