Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

Terentev, Anton; Долженко, В. И.; Fedotov, Alexander; Eremenko, Danila

doi:10.3390/s22030757

Cited by 129 publications

(81 citation statements)

References 208 publications

(421 reference statements)

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“…The role of sterols in leaf senescence is also supported in other plants such as Arabidopsis and others. 26 A degradation product of chlorophyll such as phytol accumulates and initiates the synthesis of α-tocopherol in plant senescence 27 and under disease conditions, 28 where a significant plant metabolic process occurs in bacterial infection in Arabidopsis plants. 26 This supports our results that show an increased level of phytol, stigmasterol, α-tocopherol, and βamyrin in the aged leaves.…”

Section: Resultsmentioning

confidence: 99%

Next-Generation Metabolomic Profiling of Basil Leaves in Senescence by Mass Spectrometry with a Combined Approach by Fourier Transform Infrared Spectroscopy and Hyperspectral Reflectance

Sarangapani

2022

ACS Agric. Sci. Technol.

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Leaf senescence is a key developmental process in plants that involves remobilization of nutrients from senescing leaves to other parts of the plant. It is essential therefore to understand the metabolic events in leaf senescence for plant performance that enhances agricultural yield. Metabolite analysis by spectrometry methods revealed a significant difference in phytocompounds between the healthy and aged leaves contributing to the fitness and survival of the plant. In addition, we demonstrate that hyperspectral reflectance signatures covering the spectral range of 400−1000 nm for healthy and aged leaves were highly predictive in differentiating the metabolome changes indicating a statistical significance and explaining the spectral variability of the leaf traits. Correlation of data sets from both spectral signatures and invasive methods facilitated the detection of candidate metabolites along with chemometric analysis. The results demonstrated that metabolomics along with spectroscopy and reflectance are valuable tools for real time metabolic monitoring to understand the metabolic changes in leaf senescence for nutrient remobilization and signaling responses to environmental cues and crop monitoring.

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Section: Resultsmentioning

confidence: 99%

Next-Generation Metabolomic Profiling of Basil Leaves in Senescence by Mass Spectrometry with a Combined Approach by Fourier Transform Infrared Spectroscopy and Hyperspectral Reflectance

Sarangapani

2022

ACS Agric. Sci. Technol.

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“…On the other hand, collecting more data has increased the need to develop new data analysis techniques. In this sense, machine learning and deep learning techniques have been increasingly used to describe patterns of plant health data, such as pathogen analysis [23,30,65,66], nutritional and water deficiency [67][68][69], pest infestations [45,66,70], and the presence of weeds [71].…”

Section: Discussionmentioning

confidence: 99%

“…The use of hyperspectral images to detect insect pests is a recent approach, showing high potential to identify and classify insect infestation levels [23]. Hyperspectral images contain various spatial and spectral information from the plant tissue, allowing precise and efficient detection of spectral alterations caused by insect damage.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging

et al. 2022

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Arthropod pests are among the major problems in soybean production and regular field sampling is required as a basis for decision-making for control. However, traditional sampling methods are laborious and time-consuming. Therefore, our goal is to evaluate hyperspectral remote sensing as a tool to establish reflectance patterns from soybean plants infested by various densities of two species of stinkbugs (Euschistus heros and Diceraeus melacanthus (Hemiptera: Pentatomidae)) and two species of caterpillars (Spodoptera eridania and Chrysodeixis includens (Lepidoptera: Noctuidae)). Bioassays were carried out in greenhouses with potted plants placed in cages with 5 plants infested with 0, 2, 5, and 10 insects. Plants were classified according to their reflectance, based on the acquisition of spectral data before and after infestation, using a hyperspectral push-broom spectral camera. Infestation by stinkbugs did not cause significative differences in the reflectance patterns of infested or non-infested plants. In contrast, caterpillars caused changes in the reflectance patterns, which were classified using a deep-learning approach based on a multilayer perceptron artificial neural network. High accuracies were achieved when the models classified low (0 + 2) or high (5 + 10) infestation and presence or absence of insects. This study provides an initial assessment to apply a non-invasive detection method to monitor caterpillars in soybean before causing economic damage.

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“…Zhang et al ( 2019 ) carried out a similar study using high-resolution hyperspectral images to detect the presence of yellow rust in winter wheat. Refer to Terentev et al ( 2022 ) for a recent overview of hyperspectral approaches.…”

Section: Related Work On Disease Detectionmentioning

confidence: 99%

Detection and Localization of Tip-Burn on Large Lettuce Canopies

Franchetti¹,

Pirri

2022

Front. Plant Sci.

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Recent years have seen an increased effort in the detection of plant stresses and diseases using non-invasive sensors and deep learning methods. Nonetheless, no studies have been made on dense plant canopies, due to the difficulty in automatically zooming into each plant, especially in outdoor conditions. Zooming in and zooming out is necessary to focus on the plant stress and to precisely localize the stress within the canopy, for further analysis and intervention. This work concentrates on tip-burn, which is a plant stress affecting lettuce grown in controlled environmental conditions, such as in plant factories. We present a new method for tip-burn stress detection and localization, combining both classification and self-supervised segmentation to detect, localize, and closely segment the stressed regions. Starting with images of a dense canopy collecting about 1,000 plants, the proposed method is able to zoom into the tip-burn region of a single plant, covering less than 1/10th of the plant itself. The method is crucial for solving the manual phenotyping that is required in plant factories. The precise localization of the stress within the plant, of the plant within the tray, and of the tray within the table canopy allows to automatically deliver statistics and causal annotations. We have tested our method on different data sets, which do not provide any ground truth segmentation mask, neither for the leaves nor for the stresses; therefore, the results on the self-supervised segmentation is even more impressive. Results show that the accuracy for both classification and self supervised segmentation is new and efficacious. Finally, the data set used for training test and validation is currently available on demand.

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Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

Cited by 129 publications

References 208 publications

Next-Generation Metabolomic Profiling of Basil Leaves in Senescence by Mass Spectrometry with a Combined Approach by Fourier Transform Infrared Spectroscopy and Hyperspectral Reflectance

Next-Generation Metabolomic Profiling of Basil Leaves in Senescence by Mass Spectrometry with a Combined Approach by Fourier Transform Infrared Spectroscopy and Hyperspectral Reflectance

Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging

Detection and Localization of Tip-Burn on Large Lettuce Canopies

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