Early detection of rice blast (Pyricularia) at seedling stage in Nipponbare rice variety using near-infrared hyper-spectral image

<p>Objective: To obtain the characteristics analysis results of aronia melanocarpa leaves under saline alkali stress state and improve yield and quality of aronia melanocarpa. Methods: Using hyper spectral imaging system to obtain hyper spectral images of aronia melanocarpa leaves under saline alkali stress. The clear hyper spectral image is obtained by the conversion of reflectance, spectral envelope removal, and spectral denoising, and final hyper spectral image is obtained by the normalization of a clearer hyper spectral image. Results: Spectral information of aronia melanocarpa leaves under slight saline alkali stress: in the visible band, leaf reflectance is lower than leaf nutrient rich stress condition; in the near infrared band, the nutrient rich leaves was significantly higher than that of stress leaves. Spectral information of leaves under moderate saline alkali stress: spectral reflectance of different lesion spots for a same leaf in 550-680nm band is: severe > moderate > slight > normal, in the near infrared band is on the contrary; spectral reflectance for different lesion grades in 550-680 nm, severe lesion leaves have highest reflectance, and normal leaves have the lowest reflectance. Under severe saline alkali stress, the leaf spectral information is: there is no significant difference in leaf spectral reflectance between the attachment aphid and the damaged leaf, but the difference is obvious for normal leaves in the band of 450-500 nm, 560-680 nm and 750-900 nm. Comparative results analysis for the three of saline alkali stress degree is: the near infrared band of 560-680 nm and the visible band of 780-900 nm is the sensitive band for the diagnosis of three kinds of stress; slight saline alkali stress has the most significant differences at 550 nm, and 780-900 nm; severe saline alkali stress has at 680 nm and 780-900 nm. Conclusion: The proposed method can analyze hyper spectral image characteristics of aronia melanocarpa leaves under different saline alkali stress the condition of is a kind of plant leaves, which is an efficient method for the analysis of characteristics of plant leaves under saline alkali stress.</p>

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Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Sensors ◽

10.3390/s21093052 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3052

Author(s):

Mas Ira Syafila Mohd Hilmi Tan ◽

Mohd Faizal Jamlos ◽

Ahmad Fairuz Omar ◽

Fatimah Dzaharudin ◽

Suramate Chalermwisutkul ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Early Detection ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Predictive Analytics ◽

Detection System ◽

Imaging Techniques ◽

Ganoderma Boninense ◽

Machine Learning Classification ◽

Detection Techniques

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

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Exploring personalized postoperative non-invasive tissue oximetry in DIEP flap breast reconstruction

European Journal of Plastic Surgery ◽

10.1007/s00238-021-01873-7 ◽

2021 ◽

Author(s):

Anouk A. M. A. Lindelauf ◽

Nousjka P. A. Vranken ◽

Rutger M. Schols ◽

Esther A. C. Bouman ◽

Patrick W. Weerwind ◽

...

Keyword(s):

Early Detection ◽

Breast Reconstruction ◽

Near Infrared ◽

Diep Flap ◽

Baseline Measurement ◽

Flap Surgery ◽

Level Of Evidence ◽

Non Invasive ◽

Vascular Compromise ◽

Tissue Oximetry

Abstract Early detection of vascular compromise after autologous breast reconstruction is crucial to enable timely re-exploration for flap salvage. Several studies proposed non-invasive tissue oximetry for early identification of ischemia of deep inferior epigastric perforator (DIEP) flaps. The present study aimed to explore the utility of non-invasive tissue oximetry following DIEP flap surgery using a personalized oxygenation threshold. Methods Patients undergoing immediate/delayed DIEP flap surgery were included in this prospective observational study. DIEP flap tissue oxygenation (StO2) was monitored continuously using near-infrared spectroscopy. A baseline measurement was performed by positioning one sensor at the marked position of the major inferior epigastric perforator on the abdomen. A new sensor was positioned postoperatively on the transplanted tissue. In unilateral procedures, postoperative StO2 values of the native breast were also obtained. Measurements were continued for 24 h. Results Thirty patients (42 flaps) were included. Fourteen patients (46.7%) had an uncomplicated postoperative course. A minor complication was observed in thirteen patients; in five patients, at least one major complication occurred, requiring re-exploration. Median StO2 readings were significantly lower in patients with major complications compared to uncomplicated cases. In fourteen unilateral DIEP flap procedures, StO2 values of the native breast were similar to the preoperative baseline measurement (92%; p = 0.452). Conclusions Non-invasive tissue oximetry following DIEP flap surgery could aid in early detection of vascular compromise. StO2 values of the native breast and abdominal wall preoperatively can be used interchangeably and can serve as personalized reference value. Level of evidence: Level IV, diagnostic / prognostic study.

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Early Detection of SARS-CoV-2 Seroconversion in Humans with Aggregation-Induced Near-Infrared Emission Nanoparticle-Labeled Lateral Flow Immunoassay

ACS Nano ◽

10.1021/acsnano.1c01932 ◽

2021 ◽

Author(s):

Rui Chen ◽

Cuiping Ren ◽

Miao Liu ◽

Xiaopeng Ge ◽

Mingsheng Qu ◽

...

Keyword(s):

Early Detection ◽

Near Infrared ◽

Infrared Emission ◽

Lateral Flow ◽

Lateral Flow Immunoassay ◽

Near Infrared Emission

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Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Sensors ◽

10.3390/s21030742 ◽

2021 ◽

Vol 21 (3) ◽

pp. 742

Author(s):

Canh Nguyen ◽

Vasit Sagan ◽

Matthew Maimaitiyiming ◽

Maitiniyazi Maimaitijiang ◽

Sourav Bhadra ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Early Detection ◽

Convolutional Neural Network ◽

Near Infrared ◽

Hyperspectral Data ◽

Viral Diseases ◽

Support Vector ◽

Spectral Features ◽

Feature Spaces

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

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Spectroscopic detection of forest diseases: a review (1970–2020)

Journal of Forestry Research ◽

10.1007/s11676-021-01378-w ◽

2021 ◽

Author(s):

Lorenzo Cotrozzi

Keyword(s):

Early Detection ◽

Optical Sensors ◽

Near Infrared ◽

Sustainable Forest Management ◽

Visible Spectrum ◽

Cost Effective ◽

Infrared Region ◽

Forest Disturbances ◽

Effective Manner ◽

Forest Diseases

AbstractSustainable forest management is essential to confront the detrimental impacts of diseases on forest ecosystems. This review highlights the potential of vegetation spectroscopy in improving the feasibility of assessing forest disturbances induced by diseases in a timely and cost-effective manner. The basic concepts of vegetation spectroscopy and its application in phytopathology are first outlined then the literature on the topic is discussed. Using several optical sensors from leaf to landscape-level, a number of forest diseases characterized by variable pathogenic processes have been detected, identified and quantified in many country sites worldwide. Overall, these reviewed studies have pointed out the green and red regions of the visible spectrum, the red-edge and the early near-infrared as the spectral regions most sensitive to the disease development as they are mostly related to chlorophyll changes and symptom development. Late disease conditions particularly affect the shortwave-infrared region, mostly related to water content. This review also highlights some major issues to be addressed such as the need to explore other major forest diseases and geographic areas, to further develop hyperspectral sensors for early detection and discrimination of forest disturbances, to improve devices for remote sensing, to implement long-term monitoring, and to advance algorithms for exploitation of spectral data. Achieving of these goals will enhance the capability of vegetation spectroscopy in early detection of forest stress and in managing forest diseases.

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