Application of multiple spectral systems for the tree disease detection: A review

Fang, Shiyan; Cui, Ruiyan; Wang, Yan; Zhao, Yanru; Yu, Ke; Jiang, Ao

doi:10.1080/05704928.2021.1930552

Cited by 20 publications

(9 citation statements)

References 108 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results confirmed the capacity of remote sensing methods based on hyperspectral reflectance in the VIS/NIR region for detection plant diseases and discriminating between damage levels [9][10][11]25], which in Hevea represents a promising tool for the early detection and discrimination of SALB in relation to other diseases, such as anthracnose (Colletotrichum spp.) and black crust (Phyllachora huberi), which are closely associated with SALB symptoms at the leaf level [7,65].…”

Section: Changes In Leaf Spectral Reflectancesupporting

confidence: 80%

“…Several studies have confirmed the potential of the combined use of spectral reflectance and machine learning (ML) algorithms for detecting various diseases in plants [9,11,14,25], and others have used reflectance to predict some morphophysiological traits [13,23,29,47]. However, there are few studies that have integrated reflectance, disease, physiology, and ML algorithms [11,12], and no study has used this approach for SALB in rubber trees.…”

Section: Classification Of Salb Levelsmentioning

confidence: 99%

“…In the VIS-NIR sensors, the most important regions of electromagnetic spectrum correspond to the visible portion of the spectrum (VIS, wavelength: 400-750 nm), mainly related to changes in chlorophyll and carotenoid contents, and the near-infrared (NIR), more precisely the short-wavelength region (SW-NIR, wavelength: 750-1300 nm), which is associated with structural discontinuities found in the leaf mesophyll [9,11]. However, the level at which different diseases cause various spectral changes in plants and the degree to which the spectral reflectance changes for a particular stress agent vary among species, plants, organs, and tissues [9,10,25]. Various spectral aspects related to biotic stress are still unresolved in many pathosystems because of the specificity of the host-pathogen interaction [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance

Sterling

Rienzo

2022

Plants

View full text Add to dashboard Cite

The efficiency of visible and near-infrared (VIS/NIR) sensors and predictive modeling for detecting and classifying South American Leaf Blight (SALB) (Pseudocercospora ulei) in rubber trees (Hevea brasiliensis) has been poorly explored. Furthermore, the performance of VIS/NIR analysis combined with machine learning (ML) algorithms for predicting photosynthetic alterations caused by SALB is unknown. Therefore, this study aimed to detect and classify the SALB levels, as well as to predict, for the first time, disease-induced photosynthetic changes in rubber trees. Leaf hyperspectral reflectance combined with five ML techniques (random forest (RF), boosted regression tree (BRT), bagged classification and regression trees (BCART), artificial neural network (ANN), and support vector machine (SVM)) were used. The RF, ANN, and BCART models achieved the best performance for classifying the SALB levels on the training dataset (accuracies of 98.0 to 99.8%), with 10-fold cross-validation repeated five times, and test dataset (accuracies of 97.1 to 100%). The ANN and RF models were better at predicting leaf gas exchange-related traits such as net CO2 assimilation rate (A) and extrinsic water use efficiency (WUEe) in the training (R2 ranged from 0.97 to 0.99) and testing (R2 ranged from 0.96 to 0.99) phases. In comparison, lower performances (R2 ranged from 0.24 to 0.52) were evidenced for the photochemical traits. This research provides a basis for future designs of a remote monitoring system based on early detection and accurate diagnosis of biotic stress caused by SALB, which is fundamental for more effective rubber crop protection.

show abstract

Section: Changes In Leaf Spectral Reflectancesupporting

confidence: 80%

Section: Classification Of Salb Levelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance

Sterling

Rienzo

2022

Plants

View full text Add to dashboard Cite

show abstract

“…Optical spectroscopy has been used to establish the microstructure and composition of plant tissues; to evaluate crops quality and yield; to assess plants responses to biotic and abiotic stress; to screen various disorders and pathologies; to determine the impact of plant species onto the ecosystems' biodiversity and stability; and to assess, remotely the physiological status of photosynthetic biofilms. These many applications have been compiled in various recent reviews on the research topic [1][2][3][4][5]. The emergence of high-throughput plant phenotyping technologies and the concomitant need for rapid and contactless technologies for the assessment of plant performance renewed the interest in the application of optical spectroscopy techniques in plant sciences [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

et al. 2022

View full text Add to dashboard Cite

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.

show abstract

“…Therefore, each sample's the Raman "fingerprint" of each sample is unique (Fang et al, 2021). Significantly, RS could provide essential information related to the biochemical composition of the tree tissue cell, such as protein, polysaccharide, and lipid.…”

Section: Introductionmentioning

confidence: 99%

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage

et al. 2022

Self Cite

View full text Add to dashboard Cite

Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.

show abstract

Application of multiple spectral systems for the tree disease detection: A review

Cited by 20 publications

References 108 publications

Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance

Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage

Contact Info

Product

Resources

About