scholarly journals Biotic and Abiotic Sources of Stress on Wild Rocket Classified by Leaf-image Hyperspectral Data Mining With an Artificial Intelligence Model

2021 ◽  
Author(s):  
Alejandra Navarro ◽  
Nicola Nicastro ◽  
Corrado Costa ◽  
Alfonso Pentangelo ◽  
Mariateresa Cardarelli ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Deficit ◽  
Water Status ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Physiological Parameters ◽  
Control Treatment ◽  
Carotenoid Content ◽  
Biotic Stresses ◽  
Wild Rocket

Abstract BackgroundWild rocket (Diplotaxis tenuifolia) is prone to soil-borne stresses under intensive cultivation systems devoted to ready-to-eat salad chain, increasing needs for external inputs. Early detection of the abiotic and biotic stresses by using digital reflectance-based probes may allow optimization and enhance performances of the mitigation strategies.Methodshyperspectral image analysis was applied to D. tenuifolia potted plants subjected, in a greenhouse experiment, to five treatments for one week: a control treatment watered to 100% water holding capacity, two biotic stresses: Fusarium wilting and Rhizoctonia rotting, and two abiotic stresses: water deficit and salinity. Leaf hyperspectral fingerprints were submitted to an artificial intelligence pipeline for training and validating image-based classification models able to work in the stress range. Spectral investigation was corroborated by pertaining physiological parameters. ResultsWater status was mainly affected by water deficit treatment, followed by fungal diseases, while salinity did not change water relations of wild rocket plants compared to control treatment. Biotic stresses triggered discoloration in plants just in a week after application of the treatments, as evidenced by the colour space coordinates and pigment contents values. Some vegetation indices, calculated on the bases of the reflectance data, targeted on plant vitality and chlorophyll content, healthiness, and carotenoid content, agreed with the patterns of variations observed for the physiological parameters. Artificial neural network helped selection of VIS (492-504, 540-568 and 712-720 nm) and NIR (855, 900-908 and 970 nm) bands, whose read reflectance contributed to discriminate stresses by imaging. ConclusionsThis study provided significative spectral information linked to the assessed stresses, allowing the identification of narrowed spectral regions and single wavelengths due to changes in photosynthetically active pigments and in water status revealing the etiological cause.

scholarly journals Physiological parameters to select upland rice genotypes for tolerance to water deficit

2015 ◽  
Vol 50 (7) ◽  
pp. 534-540 ◽  
Author(s):  
Cleber Morais Guimarães ◽  
Luís Fernando Stone ◽  
Adriano Pereira de Castro ◽  
Odilon Peixoto de Morais Júnior
Keyword(s):  
Water Stress ◽  
Water Deficit ◽  
Upland Rice ◽  
Water Status ◽  
Leaf Temperature ◽  
Physiological Parameters ◽  
Stress Conditions ◽  
Diffusive Resistance ◽  
Susceptible Control ◽  
Rice Genotypes

Abstract: The objective of this work was to evaluate the feasibility of using physiological parameters for water deficit tolerance, as an auxiliary method for selection of upland rice genotypes. Two experiments - with or without water deficit - were carried out in Porangatu, in the state of Goiás, Brazil; the water deficit experiment received about half of irrigation that was applied to the well-watered experiment. Four genotypes with different tolerance levels to water stress were evaluated. The UPLRI 7, B6144F-MR-6-0-0, and IR80312-6-B-3-2-B genotypes, under water stress conditions, during the day, showed lower stomatal diffusive resistance, higher leaf water potential, and lower leaf temperature than the control. These genotypes showed the highest grain yields under water stress conditions, which were 534, 601, and 636 kg ha-1, respectively, and did not differ significantly among them. They also showed lower drought susceptibility index than the other genotypes. 'BRS Soberana' (susceptible control) was totally unproductive under drought conditions. Leaf temperature is a easy-read parameter correlated to plant-water status, viable for selecting rice genotypes for water deficit tolerance.

scholarly journals Categorization of the water status of rice inoculated with arbuscular mycorrhizae and with water deficit

2021 ◽  
pp. 339-355
Author(s):  
Michel Ruiz Sánchez ◽  
Juan Adriano Cabrera Rodríguez ◽  
José M. Del'Anico Rodríguez ◽  
Yaumara Muñoz Hernández ◽  
Ricardo Aroca Álvarez ◽  
...  
Keyword(s):  
Water Stress ◽  
Water Deficit ◽  
Mycorrhizal Fungi ◽  
Water Status ◽  
Light Stress ◽  
Arbuscular Mycorrhizal ◽  
Arbuscular Mycorrhizal Symbiosis ◽  
Control Treatment ◽  
Mycorrhizal Symbiosis ◽  
Rice Plants

Introduction. The water deficit negatively affects rice plants and limits their productivity. Arbuscular mycorrhizal symbiosis has been shown to improve rice productivity in drought conditions. Objective. To propose a new categorization for the state of water stress of rice plants inoculated (AM) or not with arbuscular mycorrhizal fungi (nonAM) and exposed to water deficit (D) during the vegetative phase. Materials and methods. The experiment was carried out under controlled greenhouse conditions during the years 2009 and 2010 at the Zaidín Experimental Station, Granada, Spain. The rice transplantation was carried out fourteen days after germination to pots with a 5 cm water sheet and at 30, 40, or 50 days after transplantation (DAT) they were subjected to water deficit during a period of 15 days, at which time the water sheet was restored. The control treatment was maintained throughout the cycle under flood conditions (ww). Evaluations were performed at 45, 55, 65 DAT and after recovery at 122 DAT. The harvest was carried out at 147 DAT. Results. The reduction in water supply demonstrated water stress in the plants, manifested by the decrease in the water potential of the leaves. Arbuscular mycorrhizal symbiosis always favored the water status of the plant. Four categories of water status of plants were proposed taking into account water potentials and agricultural yield: no stress (≥-0.67 MPa); light stress (<-0.67 to -1.20 MPa); moderate stress (<-1.20 to -1.60 MPa), and severe stress (<-1.60 MPa). Conclusion. The categorization of stress due to the water deficit is a tool of high scientific value for the specific case of rice, since this plant has the capacity to adapt to tolerate the presence of a sheet of water throughout its biological cycle and is highly susceptible to water deficit.

scholarly journals Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning

2021 ◽  
Vol 14 (1) ◽  
pp. 84
Author(s):  
Catello Pane ◽  
Gelsomina Manganiello ◽  
Nicola Nicastro ◽  
Francesco Carotenuto
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Hyperspectral Image ◽  
Learning Algorithm ◽  
Vegetation Indices ◽  
Symptom Assessment ◽  
Hyperspectral Data ◽  
Cross Prediction ◽  
Average Accuracy ◽  
Wild Rocket

Fusarium oxysporum f. sp. raphani is responsible for wilting wild rocket (Diplotaxis tenuifolia L. [D.C.]). A machine learning model based on hyperspectral data was constructed to monitor disease progression. Thus, pathogenesis after artificial inoculation was monitored over a 15-day period by symptom assessment, qPCR pathogen quantification, and hyperspectral imaging. The host colonization by a pathogen evolved accordingly with symptoms as confirmed by qPCR. Spectral data showed differences as early as 5-day post infection and 12 hypespectral vegetation indices were selected to follow disease development. The hyperspectral dataset was used to feed the XGBoost machine learning algorithm with the aim of developing a model that discriminates between healthy and infected plants during the time. The multiple cross-prediction strategy of the pixel-level models was able to detect hyperspectral disease profiles with an average accuracy of 0.8. For healthy pixel detection, the mean Precision value was 0.78, the Recall was 0.88, and the F1 Score was 0.82. For infected pixel detection, the average evaluation metrics were Precision: 0.73, Recall: 0.57, and F1 Score: 0.63. Machine learning paves the way for automatic early detection of infected plants, even a few days after infection.

Different Carotenoid Enrichment in Two Climacteric Fruits after Post- Harvest UV-B Treatment

2020 ◽  
Vol 16 (2) ◽  
pp. 102-108
Author(s):  
Carolina F. Assumpção ◽  
Médelin M. da Silva ◽  
Vanessa S. Hermes ◽  
Annamaria Ranieri ◽  
Ester A. Ferreira ◽  
...  
Keyword(s):  
Bioactive Compound ◽  
Ultraviolet B ◽  
Low Flow ◽  
Control Treatment ◽  
Carotenoid Content ◽  
Post Harvest ◽  
Beneficial Effects ◽  
Fruit Skin ◽  
Compound Synthesis ◽  
Viable Method

Background: Ultraviolet B (UV-B) radiation is a promising and environmentally friendly technique, which in a low flow rate, can induce bioactive compound synthesis. This work aimed at evaluating the effectiveness of post-harvest UV-B treatment in order to improve carotenoid content in climacteric fruits like persimmon and guava fruits. Methods: The fruits were harvested at commercial maturity and placed into climatic chambers equipped with UV-B lamps. For control treatment, the UV-B lamps were covered by a benzophenone film, known to block the radiation. This radiation was applied during 48 hours and fruits were sampled at 25, 30 and 48 hours of each treatment. HPLC analysis was performed to separate and identify carotenoid compounds from fruit skin after a saponification process. Results: Fruit from 30 hours treatment began to present a carotenoid accumulation since the majority of analyzed compounds exhibited its synthesis stimulated from this time on. In persimmon skin, it was observed that the maximum content was reached after 48 hours of UV-B treatment. Conclusion: These results suggest that this post-harvest UV-B treatment can be an innovative and a viable method to induce beneficial effects on guava and mainly on persimmon fruit.

scholarly journals Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

2021 ◽  
Vol 13 (3) ◽  
pp. 536
Author(s):  
Eve Laroche-Pinel ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Véronique Chéret ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Status ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Short Wave ◽  
Spatial And Temporal Scales ◽  
Spectral Bands ◽  
Main Challenge ◽  
Spectral Scale ◽  
Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

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.

scholarly journals Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images

2021 ◽  
Vol 13 (9) ◽  
pp. 1837
Author(s):  
Eve Laroche-Pinel ◽  
Sylvie Duthoit ◽  
Mohanad Albughdadi ◽  
Anne D. Costard ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Potential ◽  
Large Scale ◽  
Water Status ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Severe Drought ◽  
Stem Water Potential ◽  
Stem Water ◽  
Sentinel 2

Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (R2=0.40, RMSE=0.26).

scholarly journals Potential of UAS-Based Remote Sensing for Estimating Tree Water Status and Yield in Sweet Cherry Trees

2020 ◽  
Vol 12 (15) ◽  
pp. 2359
Author(s):  
Víctor Blanco ◽  
Pedro José Blaya-Ros ◽  
Cristina Castillo ◽  
Fulgencio Soto-Vallés ◽  
Roque Torres-Sánchez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deficit Irrigation ◽  
Vegetation Index ◽  
Sweet Cherry ◽  
Prunus Avium ◽  
Water Status ◽  
Control Treatment ◽  
Stem Water Potential ◽  
Irrigation Treatments ◽  
Cherry Trees

The present work aims to assess the usefulness of five vegetation indices (VI) derived from multispectral UAS imagery to capture the effects of deficit irrigation on the canopy structure of sweet cherry trees (Prunus avium L.) in southeastern Spain. Three irrigation treatments were assayed, a control treatment and two regulated deficit irrigation treatments. Four airborne flights were carried out during two consecutive seasons; to compare the results of the remote sensing VI, the conventional and continuous water status indicators commonly used to manage sweet cherry tree irrigation were measured, including midday stem water potential (Ψs) and maximum daily shrinkage (MDS). Simple regression between individual VIs and Ψs or MDS found stronger relationships in postharvest than in preharvest. Thus, the normalized difference vegetation index (NDVI), resulted in the strongest relationship with Ψs (r2 = 0.67) and MDS (r2 = 0.45), followed by the normalized difference red edge (NDRE). The sensitivity analysis identified the optimal soil adjusted vegetation index (OSAVI) as the VI with the highest coefficient of variation in postharvest and the difference vegetation index (DVI) in preharvest. A new index is proposed, the transformed red range vegetation index (TRRVI), which was the only VI able to statistically identify a slight water deficit applied in preharvest. The combination of the VIs studied was used in two machine learning models, decision tree and artificial neural networks, to estimate the extra labor needed for harvesting and the sweet cherry yield.

Water deficit in plant communities

1970 ◽  
Vol 48 (6) ◽  
pp. 1199-1201 ◽  
Author(s):  
M. Gračanin ◽  
Lj. Ilijanić ◽  
V. Gaži ◽  
N. Hulina
Keyword(s):  
Water Deficit ◽  
Plant Communities ◽  
Water Regime ◽  
Water Status

Comparative investigations within the two different plant communities of Croatia (Fagetum silvaticae croaticum abietetosum Ht and Querco-Carpinetum croaticum erythronietosum Ht) indicate that (1) the two communities have their own range of water deficit values, (2) the Dw values are dependent on the capability of the species to regulate their water regime, (3) the same species behave differently within the two communities, (4) water deficit of leaves can be used as an indication of the water status of the site and plants, and consequently may have a significant place in the synecology and synchorology of plant communities.

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