Optical Methods for the Detection of Plant Pathogens and Diseases (Review)

Gudkov, Sergey V.; Matveeva, Tatiana A.; Sarimov, Ruslan M.; Simakin, Alexander V.; Stepanova, Evgenia V.; Moskovskiy, Maksim N.; Dorokhov, Alexey S.; Izmailov, Andrey Yu.

doi:10.3390/agriengineering5040110

Cited by 4 publications

(3 citation statements)

References 156 publications

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“…The number of pigments synthesized by fungi is very large [ 46 ]; the mycelium of some species at different states of their growth may contain up to a dozen pigments. Thus, the idea of detecting the plant pathogens using optical methods [ 47 ] looks promising. Naturally, quantum chemistry calculations applying the TD-DFT approach have been used for many carotenoids and other photosynthetic pigments [ 48 , 49 ]; moreover, some studies have recently been published on the simulation of the vibrational spectrum of rubrofusarin [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

A Prototype Method for the Detection and Recognition of Pigments in the Environment Based on Optical Property Simulation

Pishchalnikov,

Chesalin,

Kurkov

et al. 2023

Plants

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The possibility of pigment detection and recognition in different environments such as solvents or proteins is a challenging, and at the same time demanding, task. It may be needed in very different situations: from the nondestructive in situ identification of pigments in paintings to the early detection of fungal infection in major agro-industrial crops and products. So, we propose a prototype method, the key feature of which is a procedure analyzing the lineshape of a spectrum. The shape of the absorption spectrum corresponding to this transition strongly depends on the immediate environment of a pigment and can serve as a marker to detect the presence of a particular pigment molecule in a sample. Considering carotenoids as an object of study, we demonstrate that the combined operation of the differential evolution algorithm and semiclassical quantum modeling of the optical response based on a generalized spectral density (the number of vibronic modes is arbitrary) allows us to distinguish quantum models of the pigment for different solvents. Moreover, it is determined that to predict the optical properties of monomeric pigments in protein, it is necessary to create a database containing, for each pigment, in addition to the absorption spectra measured in a predefined set of solvents, the parameters of the quantum model found using differential evolution.

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Section: Discussionmentioning

confidence: 99%

A Prototype Method for the Detection and Recognition of Pigments in the Environment Based on Optical Property Simulation

Pishchalnikov,

Chesalin,

Kurkov

et al. 2023

Plants

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“…Traditional optical methods such as multispectral, hyperspectral, and fluorescence imaging provide valuable information on plant health [7]. However, they generally rely on the analysis of grayscale images, which do not always capture the complex color variations associated with disease symptoms [8]. By contrast, AI-based solutions excel at processing colored images, exploiting the rich spectrum of hues and tones to detect subtle changes indicative of disease [9].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection

Ait Nasser,

Akhloufi

2024

Computers

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Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates keen observational skills from agronomists and plant pathologists. Precise identification of plant diseases is crucial to enhance crop yields, ultimately guaranteeing the quality and quantity of production. The latest progress in deep learning (DL) models has demonstrated encouraging outcomes in the identification and classification of plant diseases. In the context of this study, we introduce a novel hybrid deep learning architecture named “CTPlantNet”. This architecture employs convolutional neural network (CNN) models and a vision transformer model to efficiently classify plant foliar diseases, contributing to the advancement of disease classification methods in the field of plant pathology research. This study utilizes two open-access datasets. The first one is the Plant Pathology 2020-FGVC-7 dataset, comprising a total of 3526 images depicting apple leaves and divided into four distinct classes: healthy, scab, rust, and multiple. The second dataset is Plant Pathology 2021-FGVC-8, containing 18,632 images classified into six categories: healthy, scab, rust, powdery mildew, frog eye spot, and complex. The proposed architecture demonstrated remarkable performance across both datasets, outperforming state-of-the-art models with an accuracy (ACC) of 98.28% for Plant Pathology 2020-FGVC-7 and 95.96% for Plant Pathology 2021-FGVC-8.

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“…Detecting internal defects in potatoes, such as internal brown spots, hollow heart, heat necrosis, and black heart, poses a significant challenge for food engineering. The most conventional approaches employed to assess the internal quality of potatoes are characterized by their destructive nature and inefficiency [10]. These internal defects are not readily apparent until the tubers are cut or peeled, leading to economic losses in the potato industry.…”

Section: Introductionmentioning

confidence: 99%

Partial Least Square Regression for Nondestructive Determination of Sucrose Content of Healthy and Fusarium spp. Infected Potato (Solanum tuberosum L.) Utilizing Visible and Near-Infrared Spectroscopy

Prasetyo,

Amanah,

Farras

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Conventional methods of quantifying the chemical content of potatoes at different storage temperatures are time-consuming and expensive. This research studied the Visible and Near Infrared (Vis-NIR) spectroscopy for possible rapid and nondestructive methods. In this study, healthy and Fusarium spp. Potato seeds of Granola L varieties were infected artificially through the process of inoculation of fungi, and healthy potatoes were stored in various post-harvest storage conditions, namely temperatures 12°C, 25°C and a combination of temperatures 12°C and 25°C. VIS-NIR spectral data from seeds are observed periodically during the storage period. The study results showed that Vis-NIR predicted sucrose content in potatoes. The best-developed PLSR calibration model for potatoes stored at 25°C and a combination of 12°C and 25°C show R2c of 0.87 and 0.83 and RMSEC of 0.26 and 0.28. The models also successfully predicted the sugar content of potato stored at 25°C and a combination of temperatures 12°C and 25°C with R2p 0.75 and 0.78, RMSEP of 0.36 and 0.32, and RPD of 1.99 and 2.81 for sucrose. The developed model of sucrose content or potato storage temperatures of 12°C is not recommended for monitoring and detection due to the low RPD < 1.9 even though the R2c values are 0.65 – 0.9. the results of this investigation indicate that VIS-NIR spectroscopy could potentially serve as a tool for quantifying the chemical composition of potatoes during post-harvest storage.

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Optical Methods for the Detection of Plant Pathogens and Diseases (Review)

Cited by 4 publications

References 156 publications

A Prototype Method for the Detection and Recognition of Pigments in the Environment Based on Optical Property Simulation

A Prototype Method for the Detection and Recognition of Pigments in the Environment Based on Optical Property Simulation

A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection

Partial Least Square Regression for Nondestructive Determination of Sucrose Content of Healthy and Fusarium spp. Infected Potato (Solanum tuberosum L.) Utilizing Visible and Near-Infrared Spectroscopy

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