Biological effectiveness of polyfunctional biopreparations in soft wheat cultivation and assessment of crop quality based on NDVI index

Novikova, I. I.; Попова, Э. В.; Колесников, Л. Е.; Priyatkin, N. S.; Kolesnikova, Yulia R.

doi:10.1051/bioconf/20201800021

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…The influence of Bacillus subtilis strains and chitosan complexes on the productivity of common wheat and their infection with pathogens was determined after the development of preparative forms of multifunctional biopreparations. Studies have shown the effectiveness of polyfunctional preparations, together with strains of microorganisms as pathogen antagonists, and chitosan as an activator of plant disease resistance in protecting wheat from root rot, increasing yield and improving grain quality, best adapted to agri-ecological environmental factors [42]. In modern agricultural production, the application of innovative technologies has been evaluated as the physicochemical properties of nanoparticles contribute to biocompatibility and biological activity by interaction with wheat and other crops.…”

Section: Multicriteria Evaluation Of the Optimal Bio-impact Effective...mentioning

confidence: 99%

Soil Bio-Impact Effectiveness for the Optimal Multicriterial Environmental Sustainability in Crop Production

2020

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Different bio-impacts affect the various properties and composition of soil, plant residues, harvests, and technological processes, as well as the interactions between different parts of the soil, working machine tools, energy consumption and environmental pollution with harmful gases. To summarize the wide-coverage investigations of various aspects of different bio-impact parameters, a multicriteria evaluation was conducted. Experimental research shows that different bioeffects such as those of agricultural practices can be oriented towards a reduction in fuel consumption, followed by reductions in CO2 emissions from machinery and changes in soil properties, dynamics of composition, yield and other parameters. A multicriteria assessment of the essential parameters would give farmers new opportunities to choose one optimal decision for reducing fuel consumption and increasing agricultural production, thereby reducing the negative environmental impact of soil cultivation processes, increasing yields and improving soil. Of all the properties investigated, from a practical point of view, the selection of the most important of all the essential links, such as reducing energy and expenditure, reducing environmental pollution, improving soil, and increasing yields and productivity, is reasonable. The evaluation of the bio-impact effects in agriculture by accounting for many criteria from several aspects was the main objective of the multicriteria assessment using the analytic hierarchy process. Based on the results of a multivariable research of fuel consumption—C1, C2, yield—C3, CO2 from soil—C4, density—C5, total porosity—C6, humus—C7, soil stability—C8, and soil moisture content—C9, the evaluation used experimental research data and the Simple Additive Weighting (SAW) mathematical method to find the best-case scenario. Multicriteria effectiveness was most pronounced after the first and third soil bio-impacts by using a solution of essential oils of plants, 40 species of various herbs extracts, marine algae extracts, mineral oils, Azospirillum sp. (N), Frateuria aurentia (K), Bacillus megaterium (P), seaweed extract. The most important goal was to achieve the best soil bio-impact effectiveness—minimized energy consumption from ploughing and disc harrowing operations, parallelly minimized harmful emissions from agricultural machinery, minimized CO2 from soil, soil density, maximized soil total porosity, soil humus, soil stability, yield and optimized soil moisture.

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Section: Multicriteria Evaluation Of the Optimal Bio-impact Effective...mentioning

confidence: 99%

Soil Bio-Impact Effectiveness for the Optimal Multicriterial Environmental Sustainability in Crop Production

2020

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“…One of the main trends in modern crop production is the use of plant protection agents and technologies facilitating the effective control of harmful organisms and contributing to the reduction in chemicals. The optical characteristics of crop phytosanitary states can be determined by NIR-related VIs such as the NDVI (Normalized Difference Vegetation Index), which serves as a relative indicator of the amount of photosynthetically active biomass and can accurately determine changes in crop states [ 7 ]. The NDVI is probably the most utilized and well-known VI, and it consists of a linear algebraic relation between red and NIR radiation which is measured by costly and specialized multi-sensor cameras.…”

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confidence: 99%

Comparative Analysis of the NDVI and NGBVI as Indicators of the Protective Effect of Beneficial Bacteria in Conditions of Biotic Stress

Solano-Alvarez

Valencia-Hernández

Vergara-Pineda

et al. 2022

Plants

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Precision agriculture has the objective of improving agricultural yields and minimizing costs by assisting management with the use of sensors, remote sensing, and information technologies. There are several approaches to improving crop yields where remote sensing has proven to be an important methodology to determine agricultural maps to show surface differences which may be associated with many phenomena. Remote sensing utilizes a wide variety of image sensors that range from common RGB cameras to sophisticated, hyper-spectral image cameras which acquire images from outside the visible electromagnetic spectrum. The NDVI and NGBVI are computer vision vegetation index algorithms that perform operations from color masks such as red, green, and blue from RGB cameras and hyper-spectral masks such as near-infrared (NIR) to highlight surface differences in the image to detect crop anomalies. The aim of the present study was to determine the relationship of NDVI and NGBVI as plant health indicators in tomato plants (Solanum lycopersicum) treated with the beneficial bacteria Bacillus cereus-Amazcala (B. c-A) as a protective agent to cope with Clavibacter michiganensis subsp. michiganensis (Cmm) infections. The results showed that in the presence of B. c-A after infection with Cmm, NDVI and NGBVI can be used as markers of plant weight and the activation of the enzymatic activities related to plant defense induction.

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Combining biophysical parameters with thermal and RGB indices using machine learning models for predicting yield in yellow rust affected wheat crop

Singh,

Krishnan,

Singh

et al. 2023

Sci Rep

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Evaluating crop health and forecasting yields in the early stages are crucial for effective crop and market management during periods of biotic stress for both farmers and policymakers. Field experiments were conducted during 2017–18 and 2018–19 with objective to evaluate the effect of yellow rust on various biophysical parameters of 24 wheat cultivars, with varying levels of resistance to yellow rust and to develop machine learning (ML) models with improved accuracy for predicting yield by integrating thermal and RGB indices with crucial plant biophysical parameters. Results revealed that as the level of rust increased, so did the canopy temperature and there was a significant decrease in crop photosynthesis, transpiration, stomatal conductance, leaf area index, membrane stability index, relative leaf water content, and normalized difference vegetation index due to rust, and the reductions were directly correlated with levels of rust severity. The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9–16.9%, 28.6–34.4% and 59–61.1%, respectively. The ML models were able to provide relatively accurate early yield estimates, with the accuracy increasing as the harvest approached. The yield prediction performance of the different ML models varied with the stage of the crop growth. Based on the validation output of different ML models, Cubist, PLS, and SpikeSlab models were found to be effective in predicting the wheat yield at an early stage (55-60 days after sowing) of crop growth. The KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR and PLS models were proved to be more useful in predicting the crop yield at the middle stage (70 days after sowing) of the crop, while RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB and MARS models were found good to predict the crop yield at late stage (80 days after sowing). The study quantified the impact of different levels of rust severity on crop biophysical parameters and demonstrated the usefulness of remote sensing and biophysical parameters data integration using machine-learning models for early yield prediction under biotically stressed conditions.

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Biological effectiveness of polyfunctional biopreparations in soft wheat cultivation and assessment of crop quality based on NDVI index

Cited by 6 publications

References 2 publications

Soil Bio-Impact Effectiveness for the Optimal Multicriterial Environmental Sustainability in Crop Production

Soil Bio-Impact Effectiveness for the Optimal Multicriterial Environmental Sustainability in Crop Production

Comparative Analysis of the NDVI and NGBVI as Indicators of the Protective Effect of Beneficial Bacteria in Conditions of Biotic Stress

Combining biophysical parameters with thermal and RGB indices using machine learning models for predicting yield in yellow rust affected wheat crop

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