Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Berger, Katja; Machwitz, Miriam; Kycko, Marlena; Kefauver, Shawn C.; Wittenberghe, Shari Van; Gerhards, Max; Verrelst, Jochem; Atzberger, Clement; Tol, Christiaan van der; Damm, Alexander; Rascher, Uwe; Herrmann, Ittai; Sobejano-Paz, Verónica; Fahrner, Sven; Pieruschka, Roland; Prikaziuk, Egor; Buchaillot, Ma. Luisa; Halabuk, Andrej; Celesti, Marco; Koren, Gerbrand; Görmüş, Esra Tunç; Rossini, Micol; Foerster, Michael; Siegmann, Bastian; Abdelbaki, Asmaa; Tagliabue, Giulia; Hank, Tobias; Darvishzadeh, Roshanak; Aasen, Helge; García, Mónica; Pôças, Isabel; Bandopadhyay, Subhajit; Sulis, Mauro; Tomelleri, Enrico; Rozenstein, Offer; Filchev, Lachezar; Stancile, Gheorghe; Schlerf, Martin

doi:10.1016/j.rse.2022.113198

Cited by 103 publications

(56 citation statements)

References 230 publications

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“…Recently, progressive researchers have used HSI for early plant disease detection. This technology exhibits high potential and accuracy for plant disease detection [ 69 ]. In this study, we observed that HSI is suitable for the early detection of bacterial disease in soybean leaves.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging

Lay

Lee

Tayade

et al. 2023

Plants

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Plant diseases that affect crop production and productivity harm both crop quality and quantity. To minimize loss due to disease, early detection is a prerequisite. Recently, different technologies have been developed for plant disease detection. Hyperspectral imaging (HSI) is a nondestructive method for the early detection of crop disease and is based on the spatial and spectral information of images. Regarding plant disease detection, HSI can predict disease-induced biochemical and physical changes in plants. Bacterial infections, such as Pseudomonas syringae pv. tabaci, are among the most common plant diseases in areas of soybean cultivation, and have been implicated in considerably reducing soybean yield. Thus, in this study, we used a new method based on HSI analysis for the early detection of this disease. We performed the leaf spectral reflectance of soybean with the effect of infected bacterial wildfire during the early growth stage. This study aimed to classify the accuracy of the early detection of bacterial wildfire in soybean leaves. Two varieties of soybean were used for the experiment, Cheongja 3-ho and Daechan, as control (noninoculated) and treatment (bacterial wildfire), respectively. Bacterial inoculation was performed 18 days after planting, and the imagery data were collected 24 h following bacterial inoculation. The leaf reflectance signature revealed a significant difference between the diseased and healthy leaves in the green and near-infrared regions. The two-way analysis of variance analysis results obtained using the Python package algorithm revealed that the disease incidence of the two soybean varieties, Daechan and Cheongja 3-ho, could be classified on the second and third day following inoculation, with accuracy values of 97.19% and 95.69%, respectively, thus proving his to be a useful technique for the early detection of the disease. Therefore, creating a wide range of research platforms for the early detection of various diseases using a nondestructive method such HSI is feasible.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging

Lay

Lee

Tayade

et al. 2023

Plants

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“…Sensors can be deployed on various platforms that include handheld instruments, ground-based vehicles, towers, unoccupied aerial vehicles, piloted aircraft, and satellites, all with different spatial and temporal trade-offs [ 4 ]. Hyperspectral data offer the most flexibility for assessing an array of physiological variables and structural plant traits, whereas thermal and lidar data are more narrowly suited for assessing evapotranspiration and canopy structure, respectively [ 17 – 19 ]. The advantage of hyperspectral reflectance data is their sensitivity to variation in pigments, water content, and leaf and canopy structure [ 20 – 22 ].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean

et al. 2023

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Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits ( R 2 = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.

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“…Thermal imaging paired with multispectral imaging provides a dimensional modality to study the physiological response of plants to stress [ 1 ]. For instance, thermal images can detect subtle temperature changes, while multispectral images provide complementary information on the presence of any biotic stress (observed from colour change), and crop biomass [ 28 , 29 , 30 ]. Further, specific technical constraints of unimodal datasets can be resolved through multimodal data fusion.…”

Section: Introductionmentioning

confidence: 99%

An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse

Sharma

Banerjee

Hayden

et al. 2023

Plants

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Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping.

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Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Cited by 103 publications

References 230 publications

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging

Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean

An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse

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