Non-Destructive Monitoring of Crop Fresh Weight and Leaf Area with a Simple Formula and a Convolutional Neural Network

Moon, Taewon; Kim, Dongpil; Kwon, Sungmin; Ahn, Tae In; Son, Jung Eek

doi:10.3390/s22207728

Cited by 6 publications

(1 citation statement)

References 46 publications

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“…By using ab initio modelling, Moxon et al recently demonstrated the strong interaction of the surface of PuO 2 with CO 2 forming carbonate species. 172 Toth and Friedman reported that a dried polymer (i.e. PuO 2 nanoparticles) prepared by NaOH addition on a Pu(IV) acidic solution shows a great affinity for CO 2 forming carbonates which was conrmed by FTIR bands at 1500, 1350 and 890 cm À1 .…”

Section: Structural Variations Induced By Puo 2 Nanoparticle Surface ...mentioning

confidence: 99%

Synthesis and multi-scale properties of PuO₂ nanoparticles: recent advances and open questions

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Section: Structural Variations Induced By Puo 2 Nanoparticle Surface ...mentioning

confidence: 99%

Synthesis and multi-scale properties of PuO₂ nanoparticles: recent advances and open questions

et al. 2022

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Sweet Pepper Leaf Area Estimation Using Semantic 3D Point Clouds Based on Semantic Segmentation Neural Network

Giang,

Ryoo

2024

AgriEngineering

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In the field of agriculture, measuring the leaf area is crucial for the management of crops. Various techniques exist for this measurement, ranging from direct to indirect approaches and destructive to non-destructive techniques. The non-destructive approach is favored because it preserves the plant’s integrity. Among these, several methods utilize leaf dimensions, such as width and length, to estimate leaf areas based on specific models that consider the unique shapes of leaves. Although this approach does not damage plants, it is labor-intensive, requiring manual measurements of leaf dimensions. In contrast, some indirect non-destructive techniques leveraging convolutional neural networks can predict leaf areas more swiftly and autonomously. In this paper, we propose a new direct method using 3D point clouds constructed by semantic RGB-D (Red Green Blue and Depth) images generated by a semantic segmentation neural network and RGB-D images. The key idea is that the leaf area is quantified by the count of points depicting the leaves. This method demonstrates high accuracy, with an R2 value of 0.98 and a RMSE (Root Mean Square Error) value of 3.05 cm2. Here, the neural network’s role is to segregate leaves from other plant parts to accurately measure the leaf area represented by the point clouds, rather than predicting the total leaf area of the plant. This method is direct, precise, and non-invasive to sweet pepper plants, offering easy leaf area calculation. It can be implemented on laptops for manual use or integrated into robots for automated periodic leaf area assessments. This innovative method holds promise for advancing our understanding of plant responses to environmental changes. We verified the method’s reliability and superior performance through experiments on individual leaves and whole plants.

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Growth Analysis of Plant Factory-Grown Lettuce by Deep Neural Networks Based on Automated Feature Extraction

et al. 2022

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The mechanisms of lettuce growth in plant factories under artificial light (PFALs) are well known, whereby the crop is generally used as a model in horticultural science. Deep learning has also been tested several times using PFALs. Despite their numerous advantages, the performance of deep learning models is commonly evaluated based only on their accuracy. Therefore, the objective of this study was to train deep neural networks and analyze the deeper abstraction of the trained models. In total, 443 images of three lettuce cultivars were used for model training, and several deep learning algorithms were compared using multivariate linear regression. Except for linear regression, all models showed adequate accuracies for the given task, and the convolutional neural network (ConvNet) model showed the highest accuracy. Based on color mapping and the distribution of the two-dimensional t-distributed stochastic neighbor embedding (t-SNE) results, ConvNet effectively perceived the differences among the lettuce cultivars under analysis. The extension of the target domain knowledge with complex models and sufficient data, similar to ConvNet with multitask learning, is possible. Therefore, deep learning algorithms should be investigated from the perspective of feature extraction.

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Non-Destructive Monitoring of Crop Fresh Weight and Leaf Area with a Simple Formula and a Convolutional Neural Network

Cited by 6 publications

References 46 publications

Synthesis and multi-scale properties of PuO₂ nanoparticles: recent advances and open questions

Synthesis and multi-scale properties of PuO₂ nanoparticles: recent advances and open questions

Sweet Pepper Leaf Area Estimation Using Semantic 3D Point Clouds Based on Semantic Segmentation Neural Network

Growth Analysis of Plant Factory-Grown Lettuce by Deep Neural Networks Based on Automated Feature Extraction

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Non-Destructive Monitoring of Crop Fresh Weight and Leaf Area with a Simple Formula and a Convolutional Neural Network

Cited by 6 publications

References 46 publications

Synthesis and multi-scale properties of PuO2 nanoparticles: recent advances and open questions

Synthesis and multi-scale properties of PuO2 nanoparticles: recent advances and open questions

Sweet Pepper Leaf Area Estimation Using Semantic 3D Point Clouds Based on Semantic Segmentation Neural Network

Growth Analysis of Plant Factory-Grown Lettuce by Deep Neural Networks Based on Automated Feature Extraction

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Product

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Synthesis and multi-scale properties of PuO₂ nanoparticles: recent advances and open questions

Synthesis and multi-scale properties of PuO₂ nanoparticles: recent advances and open questions