Information Technology for Remote Evaluation of After Effects of Residues of Herbicides on Winter Crop Rape

Dolia, Mykola; Lysenko, Vitaliy; Pasichnyk, Natalia; Opryshko, Oleksiy; Komarchuk, Dmytro; Miroshnyk, Volodymyr; Lendiel, Taras; Martsyfei, Artem

doi:10.1109/aiact.2019.8847850

Cited by 17 publications

(4 citation statements)

References 29 publications

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“…Following a study by V. Lysenko et al (2018), the state of nitrogen nutrition can be assessed in the optical range. No characteristic colouration was found for technological stresses (Dolia, et al, 2019). However, plants that were damaged by the phytotoxic effect of herbicides or the aftereffects of their application showed typical signs of damage, such as curvature of the leaf blade (Fig.…”

Section: Resultsmentioning

confidence: 99%

Remote field monitoring results feasibility assessment for energy crops yield management

Pasichnik,

Opryshko,

Shvorov

et al. 2023

Tehnìka ta energetika

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Most vegetation indices for UAV data analysis are developed for low-resolution satellite platforms, which requires the use of other monitoring methods and agrochemical measures to accurately determine the state of plantations, considering different stages of vegetation and spectral characteristics. The research aims to develop a methodology for assessing the suitability of remote sensing spectral data for energy crop nutrition management. The study was conducted using winter crops, including wheat and rapeseed. The results for winter wheat for the period from 2017 to 2020 were analysed. Stresses associated with nutrient deficiencies were studied in the fields of long-term stationary experiments at the National University of Life and Environmental Sciences of Ukraine. The results obtained from the Slantrange sensor and Slantview software were used. The studies confirmed that the pixel distribution in images of plantations (wheat and winter rape) can be described by a Gaussian distribution. The coefficient of determination for wheat was higher than for rape due to the peculiarities of the plant leaf structure. For rapeseed, a higher coefficient of determination was found for the lognormal distribution, which is not convenient for automating fertilisation processes in precision farming technologies. The analysis of the distribution by spectral channels, in particular the presence of several maxima, may indicate the presence of foreign inclusions or transitional stages of vegetation, which makes such data unsuitable for crop management. It has been established that if, after soil filtration, the maximum amplitude of the distribution exceeds the nearest one by more than 3 times, the growing season can be considered stable for a particular area, and the results of spectral monitoring are reliable for further analysis It has been confirmed that the vegetation indices GNDVI and RNDVI are not effective for assessing the reliability of data based on the standard deviation of the distribution. Reference values of the standard deviation of the distribution can be established at research stations with controlled stress factors, which will help in crop management

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Section: Resultsmentioning

confidence: 99%

Remote field monitoring results feasibility assessment for energy crops yield management

Pasichnik,

Opryshko,

Shvorov

et al. 2023

Tehnìka ta energetika

Self Cite

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“…The Use of UAVs with a Slantrange Sensor System for Estimation of Crop Safety equipped with Parrot SEQUOIA spectral systems described by Deng and co-authors, 16 and Slantrange is represented by Enciso et al 17 and Dolia et al 18 In this case, the Slantrange system, unlike analogues, does not require the use of calibration panels at all, and is able to process data during the flight, which ensures the speed of obtaining finished cards of stress indexes without the involvement of cloud services. Since the effective use of gauging panels is complicated for characteristic areas of 80-100 hectares for Ukraine, the Slantrange system will have advantages.…”

Section: Selection Of Spectral Sensory Equipment For Uavmentioning

confidence: 99%

On the Use of UAVs with a Slantrange Sensor System for Estimation of Crop Safety

Pasichnyk¹,

Lienkov²,

Шворов³

et al. 2020

ISIJ

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The article addresses applied aspects of using UAVs for monitoring winter wheat crops to assess the aftereffects of herbicides remaining on the culture of the predecessor. The issue has a local specificity related to inconsistencies of plant cultivation technologies and the inadequate study of the impact of modern plant protection products in domestic soil conditions. Restoring the crop yields is possible by timely identification of the causes of stress, but the time for decision-making is limited. This time can be reduced by state-of-the-art monitoring technologies applied at industrial scale. Laboratory studies using phyto cameras and spectral and spectral-spatial monitoring methods unambiguously testified to the stress caused by the aftereffect of herbicides, but did not allow to establish clear criteria. Therefore, we conducted field studies using UAV-mounted Slantrange complex and analyzing the DJI Matrice 200 to define the distribution of stress areas on the field. It was found that the reliability of monitoring data can increased computer data processing and computer training in the search for correlation links between the distribution of stress plants in the field and the implementation of technological operations, terrain topography, etc.

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“…In an article by Belgian scientists [1], a multispectral vision system was developed that includes four wavelength ranges in the visible / near-IR range. Multispectral images of healthy and defective fruits were obtained, allowing to cover the entire color variability of this two-color apple variety.In the work of Ukrainian researchers [6], the technology of image recognition using standard and stress indices is described, applied to determine the diseases of rapeseed leaves when shooting with a UAV in the optical range using the RGB camera and the Slantrange complex. The affected leaves have an abnormal color, in the optical range the most informative are the red and green channels.…”

Section: Introductionmentioning

confidence: 99%

Neural network for identifying apple fruits on the crown of a tree

2021

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To identify the fruits on the crowns of trees and count their number, a software and hardware complex (PAC) based on a technical vision system and a recurrent neural network of deep learning has been developed. The created neural network and class allocation algorithms allow the software and hardware complex to function stably in the conditions of industrial horticultural plantings, regardless of the size and interference of foliage, to determine the color of the fruit surface, to identify the presence of diseases and defects of fruits. The developed package provides digital monitoring of both photographic materials and video streams in online mode.

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Information Technology for Remote Evaluation of After Effects of Residues of Herbicides on Winter Crop Rape

Cited by 17 publications

References 29 publications

Remote field monitoring results feasibility assessment for energy crops yield management

Remote field monitoring results feasibility assessment for energy crops yield management

On the Use of UAVs with a Slantrange Sensor System for Estimation of Crop Safety

Neural network for identifying apple fruits on the crown of a tree

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