Algorithm for appearance simulation of plant diseases based on symptom classification

Yang, Meng; Ding, Shu

doi:10.3389/fpls.2022.935157

Cited by 5 publications

(6 citation statements)

References 39 publications

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“…Note that absorbance and absorptance are not the same quantity. 25 This approximation can lead to deviations in the calculated color. We avoid this problem using the linear relationship between absorbance and pigment concentration in color conversion.…”

Section: Biological-based Methodsmentioning

confidence: 99%

“…However, according to the Beer‐Lambert law, 25 the decadic absorbance

{A}_{10}=\varepsilon cl=-{\log}_{10}\left(1-{A}_p\right),

where

\varepsilon

is the molecular extinction coefficient,

l

indicates the path length of the measuring beam in the sample, and

{A}_p

is the internal absorptance. When

\varepsilon

and

l

are constant,

{A}_{10}

is linearly related to concentration

c

.…”

Section: Simulation Methodsmentioning

confidence: 99%

“…According to the Beer–Lambert law, absorbance is linearly proportional to the pigment concentration. Note that absorbance and absorptance are not the same quantity 25 . This approximation can lead to deviations in the calculated color.…”

Section: Related Workmentioning

confidence: 99%

“…where i indicates the pigment index. However, according to the Beer-Lambert law, 25 the decadic absorbance…”

Section: Conversion Of Pigment Concentrations To Colormentioning

confidence: 99%

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Botanical‐based simulation of color change in fruit ripening: Taking tomato as an example

Xu,

Liu

2023

Computer Animation & Virtual

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The color change of plant fruit in ripening is a typical time‐varying phenomenon involving various factors. Due to its complexity and biodiversity, it is challenging to model this phenomenon. To address this issue, we take the tomato as an example and propose a botanical‐based framework considering variety, environment, phytohormone, and genes to simulate fruit color change during the ripening process. Specifically, we propose a first‐order kinetic model that integrates varietal, environmental, and phytohormonal factors to represent the variation of pigment concentrations in the pericarp. Moreover, we introduce a logistic model to describe the change in pigment concentration in the epidermis. Based on the gene expression pathway of tomato color in botany, we propose a genotype‐to‐phenotype simulation method to represent its biodiversity. An improved method is proposed to convert pigment concentrations into color accurately. Furthermore, we propose a gradient descent‐based method to assist the user in quickly setting pigment concentration parameters. Experiments verified that the proposed framework can simulate a wide range of tomato colors. Both qualitative and quantitative experiments validated the proposed method. Furthermore, our framework can be applied to more fruits.

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Section: Biological-based Methodsmentioning

confidence: 99%

“…However, according to the Beer‐Lambert law, 25 the decadic absorbance

{A}_{10}=\varepsilon cl=-{\log}_{10}\left(1-{A}_p\right),

where

\varepsilon

is the molecular extinction coefficient,

l

indicates the path length of the measuring beam in the sample, and

{A}_p

is the internal absorptance. When

\varepsilon

and

l

are constant,

{A}_{10}

is linearly related to concentration

c

.…”

Section: Simulation Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…where i indicates the pigment index. However, according to the Beer-Lambert law, 25 the decadic absorbance…”

Section: Conversion Of Pigment Concentrations To Colormentioning

confidence: 99%

See 2 more Smart Citations

Botanical‐based simulation of color change in fruit ripening: Taking tomato as an example

Xu,

Liu

2023

Computer Animation & Virtual

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“…Through the application of these augmentation techniques, the diversity of the training dataset was increased, leading to the improved performance of the classification model. The augmented dataset was then utilized to train the model, resulting in high accuracy and robustness in recognizing CAU species [54][55][56].…”

Section: Image Augmentationmentioning

confidence: 99%

Automated Classification of Agricultural Species through Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning

Sriprateep,

Khonjun,

Golinska-Dawson

et al. 2024

Mathematics

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The classification of certain agricultural species poses a formidable challenge due to their inherent resemblance and the absence of dependable visual discriminators. The accurate identification of these plants holds substantial importance in industries such as cosmetics, pharmaceuticals, and herbal medicine, where the optimization of essential compound yields and product quality is paramount. In response to this challenge, we have devised an automated classification system based on deep learning principles, designed to achieve precision and efficiency in species classification. Our approach leverages a diverse dataset encompassing various cultivars and employs the Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning model (P-AMIS-E). This model integrates ensemble image segmentation techniques, including U-Net and Mask-R-CNN, alongside image augmentation and convolutional neural network (CNN) architectures such as SqueezeNet, ShuffleNetv2 1.0x, MobileNetV3, and InceptionV1. The culmination of these elements results in the P-AMIS-E model, enhanced by an Artificial Multiple Intelligence System (AMIS) for decision fusion, ultimately achieving an impressive accuracy rate of 98.41%. This accuracy notably surpasses the performance of existing methods, such as ResNet-101 and Xception, which attain 93.74% accuracy on the testing dataset. Moreover, when applied to an unseen dataset, the P-AMIS-E model demonstrates a substantial advantage, yielding accuracy rates ranging from 4.45% to 31.16% higher than those of the compared methods. It is worth highlighting that our heterogeneous ensemble approach consistently outperforms both single large models and homogeneous ensemble methods, achieving an average improvement of 13.45%. This paper provides a case study focused on the Centella Asiatica Urban (CAU) cultivar to exemplify the practical application of our approach. By integrating image segmentation, augmentation, and decision fusion, we have significantly enhanced accuracy and efficiency. This research holds theoretical implications for the advancement of deep learning techniques in image classification tasks while also offering practical benefits for industries reliant on precise species identification.

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Spectral sensor‐based device for real‐time detection and severity estimation of groundnut bud necrosis virus in tomato

Kumari,

Parray,

Basavaraj

et al. 2024

Journal of Field Robotics

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A machine learning‐based approach was utilized to develop a device for groundnut bud necrosis virus (GBNV) disease severity detection and estimation in tomato plants (Solanum lycopersicum L.). The study involved inoculating tomato plants with GBNV, monitoring changes in morphological and spectral characteristics, evaluating machine learning algorithms (decision tree [DT] classifier) for analysis and classification of disease severity, and developing and validating a device for disease detection and severity estimation. Spectral data analysis revealed distinct patterns in reflectance, with notable peaks observed in the 680 and 760 nm bands, while reflectance remained low and constant beyond 900 nm. Machine learning techniques, specifically a DT model, were employed to classify disease severity based on spectral data with high accuracy (95.01% training accuracy and 93.65% testing accuracy). The model identified the near‐infrared band as highly correlated (correlation coefficient of 0.82) with disease severity. Furthermore, a compact handheld device integrating a spectral sensor, organic light‐emitting diode display, and Raspberry Pi 3B was developed for real‐time disease severity estimation. The device demonstrated robust performance, accurately predicting disease severity at different growth stages, even in the absence of visible symptoms. Additionally, disease severity percentages obtained via reverse transcription polymerase chain reaction were used to validate the accuracy of the device's estimations. Its responsive nature, with estimated response times ranging from milliseconds to seconds, facilitates timely interventions in agricultural settings. Overall, this interdisciplinary approach, combining spectral analysis, machine learning, and device development, presents a promising solution for efficient disease monitoring and management in agriculture, contributing to enhanced crop health and food security.

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Algorithm for appearance simulation of plant diseases based on symptom classification

Cited by 5 publications

References 39 publications

Botanical‐based simulation of color change in fruit ripening: Taking tomato as an example

Botanical‐based simulation of color change in fruit ripening: Taking tomato as an example

Automated Classification of Agricultural Species through Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning

Spectral sensor‐based device for real‐time detection and severity estimation of groundnut bud necrosis virus in tomato

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