scholarly journals Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

2021 ◽  
Vol 13 (13) ◽  
pp. 2436
Author(s):  
Federico Calamita ◽  
Hafiz Ali Imran ◽  
Loris Vescovo ◽  
Mohamed Lamine Mekhalfi ◽  
Nicola La Porta
Keyword(s):  
Root Rot ◽  
Woody Plants ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Disease Diagnosis ◽  
Hyperspectral Data ◽  
Detection Methods ◽  
Root Rot Disease ◽  
Rot Disease

Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. Prompt recognition of diseased plants is crucial to control the pathogen. However, the current disease detection methods are limited at a field scale. Therefore, an alternative approach is needed. In this study, we investigated the potential of hyperspectral techniques to identify fungi-infected vs. healthy plants of Vitis vinifera. We used the hyperspectral imaging sensor Specim-IQ to acquire leaves’ reflectance data of the Teroldego Rotaliano grapevine cultivar. We analyzed three different groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the near-infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to the leaf mesophyll changes. Asymptomatic plants emerged from the other groups due to a lower reflectance in the red edge spectrum (around 705 nm), ascribable to an accumulation of secondary metabolites involved in plant defense strategies. Further significant differences were observed in the wavelengths close to 550 nm in diseased vs. asymptomatic plants. We evaluated several machine learning paradigms to differentiate the plant groups. The Naïve Bayes (NB) algorithm, combined with the most discriminant variables among vegetation indices and spectral narrow bands, provided the best results with an overall accuracy of 90% and 75% in healthy vs. diseased and healthy vs. asymptomatic plants, respectively. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis in woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAVs), could be a promising tool for a cost-effective, non-invasive method of Armillaria disease diagnosis and mapping in-field, contributing to a significant step forward in precision viticulture.

scholarly journals Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

2021 ◽  
Author(s):  
Federico Calamita ◽  
Hafiz Ali Imran ◽  
Loris Vescovo ◽  
Mohamed Lamine Mekhalfi ◽  
Nicola La Porta
Keyword(s):  
Root Rot ◽  
Woody Plants ◽  
Near Infrared ◽  
Plant Defence ◽  
Disease Diagnosis ◽  
Hyperspectral Data ◽  
Learning Context ◽  
Linear Discriminant ◽  
Root Rot Disease ◽  
Rot Disease

The Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. The disease damage prompt assessment is crucial for pest management. However, the disease detection current methods are limited at the field scale. Therefore, an alternative approach that can enhance or supplement traditional techniques is needed. In this study, we investigated the potential of hyperspectral methods to identify the changes between fungi-infected and uninfected plants of Vitis vinifera in early detecting the Armillaria disease. The hyperspectral imaging sensor Specim-IQ was used to acquire images of leaves of the Teroldego Rotaliano grapevine cultivar. We analysed three groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the Near infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to internal leaf structure changes. Asymptomatic plants emerged from the other groups due to a smaller reflectance in the red-edge spectrum (around 705nm). Hypothetically associated with the presence of secondary metabolites involved in plant defence strategy. Furthermore, significant differences were observed in the wavelengths close to 550 nm in diseased plants versus asymptomatic. We used linear discriminant analysis from a machine learning context to classify the leaves based on the most significant variables (vegetation indices and single bands), with resulting overall accuracies of 85% and 84% respectively in healthy vs. diseased and healthy vs. asymptomatic. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis on woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAV), could be a promising tool for a cost-effective, non-destructive method of Armillaria disease early diagnosis and mapping in the field, contributing to a significant step forward in precision viticulture.

scholarly journals APhytophthoraconserved transposon-like DNA element as a potential target for soyabean root rot disease diagnosis

2012 ◽  
Vol 62 (3) ◽  
pp. 719-726 ◽  
Author(s):  
T.-T. Dai ◽  
J. Meng ◽  
S. Dong ◽  
C.-C. Lu ◽  
W. Ye ◽  
...  
Keyword(s):  
Root Rot ◽  
Disease Diagnosis ◽  
Root Rot Disease ◽  
Potential Target ◽  
Rot Disease

scholarly journals Biological control of Phaseolus vulgaris and Pisum sativum root rot disease using Trichoderma species

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Hammad Abdelwanees Ketta ◽  
Omar Abd El-Raouf Hewedy
Keyword(s):  
Phaseolus Vulgaris ◽  
Pisum Sativum ◽  
Common Bean ◽  
Root Rot ◽  
Fungal Pathogens ◽  
Plant Diseases ◽  
Root Rot Disease ◽  
Pea Root ◽  
Rot Disease ◽  
Pea Root Rot

Abstract Background Root rot pathogens reported to cause considerable losses in both the quality and productivity of common bean (Phaseolus vulgaris L.) and pea (Pisum sativum L.). It is an aggressive crop disease with detriment economic influence caused by Fusarium solani and Rhizoctonia solani among other soil-borne fungal pathogens. Destructive plant diseases such as root rot have been managed in the last decades using synthetic pesticides. Main body Seeking of economical and eco-friendly alternatives to combat aggressive soil-borne fungal pathogens that cause significant yield losses is urgently needed. Trichoderma emerged as promising antagonist that inhibits pathogens including those inducing root rot disease. Detailed studies for managing common bean and pea root rot disease using different Trichoderma species (T. harzianum, T. hamatum, T. viride, T. koningii, T. asperellum, T. atroviridae, T. lignorum, T. virens, T. longibrachiatum, T. cerinum, and T. album) were reported both in vitro and in vivo with promotion of plant growth and induction of systemic defense. The wide scale application of selected metabolites produced by Trichoderma spp. to induce host resistance and/or to promote crop yield, may represent a powerful tool for the implementation of integrated pest management strategies. Conclusions Biological management of common bean and pea root rot-inducing pathogens using various species of the Trichoderma fungus might have taken place during the recent years. Trichoderma species and their secondary metabolites are useful in the development of protection against root rot to bestow high-yielding common bean and pea crops.

scholarly journals Bacillus megaterium-Mediated Synthesis of Selenium Nanoparticles and Their Antifungal Activity against Rhizoctonia solani in Faba Bean Plants

2021 ◽  
Vol 7 (3) ◽  
pp. 195
Author(s):  
Amr H. Hashem ◽  
Amer M. Abdelaziz ◽  
Ahmed A. Askar ◽  
Hossam M. Fouda ◽  
Ahmed M. A. Khalil ◽  
...  
Keyword(s):  
Antifungal Activity ◽  
Vicia Faba ◽  
Root Rot ◽  
Antifungal Agents ◽  
Damping Off ◽  
Growth Promoters ◽  
Root Rot Disease ◽  
Rot Disease

Rhizoctonia root-rot disease causes severe economic losses in a wide range of crops, including Vicia faba worldwide. Currently, biosynthesized nanoparticles have become super-growth promoters as well as antifungal agents. In this study, biosynthesized selenium nanoparticles (Se-NPs) have been examined as growth promoters as well as antifungal agents against Rhizoctonia solani RCMB 031001 in vitro and in vivo. Se-NPs were synthesized biologically by Bacillus megaterium ATCC 55000 and characterized by using UV-Vis spectroscopy, XRD, dynamic light scattering (DLS), and transmission electron microscopy (TEM) imaging. TEM and DLS images showed that Se-NPs are mono-dispersed spheres with a mean diameter of 41.2 nm. Se-NPs improved healthy Vicia faba cv. Giza 716 seed germination, morphological, metabolic indicators, and yield. Furthermore, Se-NPs exhibited influential antifungal activity against R. solani in vitro as well as in vivo. Results revealed that minimum inhibition and minimum fungicidal concentrations of Se-NPs were 0.0625 and 1 mM, respectively. Moreover, Se-NPs were able to decrease the pre-and post-emergence of R. solani damping-off and minimize the severity of root rot disease. The most effective treatment method is found when soaking and spraying were used with each other followed by spraying and then soaking individually. Likewise, Se-NPs improve morphological and metabolic indicators and yield significantly compared with infected control. In conclusion, biosynthesized Se-NPs by B. megaterium ATCC 55000 are a promising and effective agent against R. solani damping-off and root rot diseases in Vicia faba as well as plant growth inducer.

scholarly journals Application of Bacillus pumilus isolates for management of black rot disease in strawberry

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Farid Abd-El-Kareem ◽  
Ibrahim E. Elshahawy ◽  
Mahfouz M. M. Abd-Elgawad
Keyword(s):  
Root Rot ◽  
Bacillus Pumilus ◽  
Field Conditions ◽  
Black Rot ◽  
Bacterial Isolates ◽  
Black Root Rot ◽  
Root Rot Disease ◽  
Growth Area ◽  
Rot Disease ◽  
The Impact

Abstract Background Black root rot of strawberry plants caused by Rhizoctonia solani, Fusarium solani, and Pythium sp. is a serious disease in Egypt. Biocontrol agents have frequently proved to possess paramount and safe tools against many diseases. The impact of soil treatments with 3 Bacillus pumilus isolates on black root rot disease of strawberry plants caused by R. solani, F., and Pythium sp. under laboratory and field conditions was examined herein on the commonly used ‘Festival’ strawberry cultivar. To increase the bacterial adhesion and distribution on the roots, each seedling was dipped in bacterial cell suspension at 1 × 108 colony-forming units/ml of each separate bacterial isolate for 30 min then mixed with 5% Arabic gum. Results The tested B. pumilus isolates significantly reduced the growth area of these 3 fungi. The two bacterial isolates Nos. 2 and 3 reduced the growth area by more than 85.2, 83.6, and 89.0% for R. solani, F. solani, and Pythium sp., respectively. Likewise, the 3 bacterial isolates significantly (P ≤ 0.05) inhibited the disease under field conditions. Isolates Nos. 2 and 3 suppressed the disease incidence by 64.4 and 68.9% and disease severity by 65.3 and 67.3%, respectively. The fungicide Actamyl had effect similar to that of the 2 isolates. B. pumilus isolates significantly enhanced growth parameters and yields of strawberry plants; isolates Nos. 2 and 3 raised the yield by 66.7 and 73.3%, respectively. Conclusions Bacillus pumilus isolates could effectively manage the black rot disease in strawberry herein. Due to the significant impact of the root rot disease on strawberry yield, B. pumilus should be further tested to manage the disease on strawberry on large scale in Egypt.

scholarly journals Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 341
Author(s):  
Pauliina Salmi ◽  
Matti A. Eskelinen ◽  
Matti T. Leppänen ◽  
Ilkka Pölönen
Keyword(s):  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Strong Correlations ◽  
Algae Biomass ◽  
Chlorophyll A Concentration ◽  
Cost Efficient ◽  
Hyperspectral Camera

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near-infrared and red turned out to be a powerful index because it was simple to calculate and interpret, yet it yielded strong correlations to abundances strain-specifically (0.85 < r < 0.96, p < 0.001). When all the indices formulated as A/B, A/(A + B) or (A − B)/(A + B), where A and B were wavebands of the spectral camera, were scrutinized, good correlations were found amongst them for biomass of each strain (0.66 < r < 0.98, p < 0.001). Comparison of near-infrared/red index to chlorophyll a concentration demonstrated that small-celled strains had higher chlorophyll absorbance compared to strains with larger cells. The comparison of spectral imaging to chlorophyll fluorescence was done for one strain of green algae and yielded strong correlations (near-infrared/red, r = 0.97, p < 0.001). Consequently, we described a simple imaging setup and information extraction based on vegetation indices that could be used to monitor algae cultures.

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