scholarly journals The septoria leaf blotch of wheat in Central Kazakhstan: prognosis, evaluation and monitoring with remotely sensed data

2021 ◽  
Vol 2 (1) ◽  
pp. 28-44
Author(s):  
Dmitry Malakhov
Keyword(s):  
Plant Species ◽  
Vegetation Indices ◽  
Condition Index ◽  
Fungal Diseases ◽  
Optical Data ◽  
Natural Phenomenon ◽  
Growth Stages ◽  
Remotely Sensed Data ◽  
Septoria Leaf Blotch ◽  
Leaf Blotch

Fungal diseases represent a widely spread natural phenomenon affecting many of wild and domesticated plants. In nature, all plant species forms plant communities of a mixed character, and the spatial pattern of dominant species is usually irregular and spotted. Some species are impregnable to a certain infection, which provides a kind of natural barrier to the infection spread within the natural community. Under the agricultural environment, when the single plant species may occupy a huge area, the species-specific parasite takes a great advantage to develop focal outbreaks and fast spreading of the infection within the area. The concentration of vulnerable plants and the absence of natural barriers within the agricultural areas provokes outbreaks of fungal diseases, that may have highly harmful consequences and result in significant yield losses. One of the purposes of the satellite optical data is an operative, cost-effective diagnostic and, in combination with climatic datasets and crop rotation information, a prognosis of fungal disease appearance and severity. In this paper, we describe the system of prognostic and monitoring measures to control the fungal diseases of wheat in Central Kazakhstan with special attention to septoria leaf blotch. The prognostic procedure provides a map of the probability of septoria leaf blotch appearance. The prognosis takes into consideration the combination of three main variables: the model of ecological niche for Septoria, the presence of wheat residue, and Vegetation Condition Index counted for the late spring (May) of the current year. The new spectral index, introduced in this paper, is the core component of monitoring activity. The index is sensitive to septoria leaf blotch severity at middle to late (stages 8-11, accordingly Feekes growth stages) periods of wheat development. Several other indices (RETA, VSDI, vegetation indices) may be of help in providing information on the spatial unevenness of wheat crops that may indicate the presence of fungal infection.

scholarly journals Assimilation of Remotely-Sensed LAI into WOFOST Model with the SUBPLEX Algorithm for Improving the Field-Scale Jujube Yield Forecasts

2019 ◽  
Vol 11 (16) ◽  
pp. 1945
Author(s):  
Tiecheng Bai ◽  
Shanggui Wang ◽  
Wenbo Meng ◽  
Nannan Zhang ◽  
Tao Wang ◽  
...  
Keyword(s):  
Developmental Stages ◽  
Vegetation Indices ◽  
Remotely Sensed ◽  
High Yield ◽  
Bias Error ◽  
Growth Stages ◽  
Landsat 8 ◽  
Statistical Regression ◽  
Field Scale ◽  
Remotely Sensed Data

In order to enhance the simulated accuracy of jujube yields at the field scale, this study attempted to employ SUBPLEX algorithm to assimilate remotely sensed leaf area indices (LAI) of four key growth stages into a calibrated World Food Studies (WOFOST) model, and compare the accuracy of assimilation with the usual ensemble Kalman filter (EnKF) assimilation. Statistical regression models of LAI and Landsat 8 vegetation indices at different developmental stages were established, showing a validated R2 of 0.770, 0.841, 0.779, and 0.812, and a validated RMSE of 0.061, 0.144, 0.180, and 0.170 m2 m−2 for emergence, fruit filling, white maturity, and red maturity periods. The results showed that both SUBPLEX and EnKF assimilations significantly improved yield estimation performance compared with un-assimilated simulation. The SUBPLEX (R2 = 0.78 and RMSE = 0.64 t ha−1) also showed slightly better yield prediction accuracy compared with EnKF assimilation (R2 = 0.73 and RMSE = 0.71 t ha−1), especially for high-yield and low-yield jujube orchards. SUBPLEX assimilation produced a relative bias error (RBE, %) that was more concentrated near zero, being lower than 10% in 80.1%, and lower than 20% in 96.1% for SUBPLEX, 72.4% and 96.7% for EnKF, respectively. The study provided a new assimilation scheme based on SUBPLEX algorithm to employ remotely sensed data and a crop growth model to improve the field-scale fruit crops yield estimates.

scholarly journals Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 545 ◽  
Author(s):  
Dimitris Stavrakoudis ◽  
Dimitrios Katsantonis ◽  
Kalliopi Kadoglidou ◽  
Argyris Kalaitzidis ◽  
Ioannis Gitas
Keyword(s):  
Near Infrared ◽  
Agronomic Traits ◽  
Oryza Sativa L ◽  
N Uptake ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Linear Regression Models ◽  
Remotely Sensed Data ◽  
Cost Efficient ◽  
N Management

The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.

scholarly journals Measuring and Modeling Crop Loss of Wheat Caused by Septoria Leaf Blotch in Seven Cultivars and Lines in Iran

2009 ◽  
Vol 49 (3) ◽  
pp. 257-262 ◽  
Author(s):  
Shideh Mojerlou ◽  
Naser Safaie ◽  
Azizollah Alizadeh ◽  
Fatemeh Khelghatibana
Keyword(s):  
Grain Yield ◽  
Disease Severity ◽  
Integral Model ◽  
Yield Reduction ◽  
Multiple Point ◽  
Growth Stages ◽  
Research Station ◽  
Crop Loss ◽  
Septoria Leaf Blotch ◽  
Leaf Blotch

Measuring and Modeling Crop Loss of Wheat Caused by Septoria Leaf Blotch in Seven Cultivars and Lines in IranSeptoria leaf blotch caused bySeptoria tritici, is one of the most important diseases of wheat worldwide including Iran. To determine yield reduction caused by this disease in Golestan province, field experiments were carried out in randomized complete block design with four replications and five wheat cvs. Tajan, Zagros, Shiroodi, Koohdasht, Shanghai and two lines N-80-6 and N-80-19 at Gorgan Research Station. Artificial inoculation was performed using spore suspension at three growth stages (Zadoks scale) including tillering (GS 37), stem elongation (GS 45) and flag leaf opening (GS 53). Control plots were sprayed with water. In this study, the 1 000 kernel weight (TKW), grain yield and area under disease progress curve (AUDPC) during growth season were measured. Statistical analysis showed that the levels of yield reduction was different in various studied wheat cultivars and lines and was reduced by 30 to 50%. The highest losses were observed for cvs. Zagros and Tajan with 48.86% and 47.41% of grain yield reduction, respectively. There was a positive correlation between grain yield reduction and AUDPC. The results of crop loss modelling using integral and multiple point regression models showed that the integral model (L = 1230.91+1.37AUDPC) in which AUDPC and crop loss percentages were independent and dependent variables, respectively, could explain more than 95% of AUDPC variations in relation to crop loss in all cultivars in two years. In the study of integral model for each cultivar, cv. Shiroodi showed the highest fitness. In multiple point models, disease severity at various dates was considered as independent variables and crop loss percentage as dependent variable. This model with the highest coefficient of determination had the best fitness for crop loss estimation. Besides, the results showed that the disease severity at GS37, GS53 and GS91 stages (Zadok's scale) was more important for crop loss prediction than that in other phenological stages.

scholarly journals Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi
Keyword(s):  
Machine Learning ◽  
Carbon Stock ◽  
Vegetation Indices ◽  
Forest Biomass ◽  
Principal Component ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics ◽  
Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

scholarly journals Effects of Non-Thermal Plasma Treatment on Seed Germination and Early Growth of Leguminous Plants—A Review

Plants ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1616
Author(s):  
Božena Šerá ◽  
Vladimír Scholtz ◽  
Jana Jirešová ◽  
Josef Khun ◽  
Jaroslav Julák ◽  
...  
Keyword(s):  
Seed Germination ◽  
Plant Species ◽  
Thermal Plasma ◽  
Microbial Inactivation ◽  
Growth Stages ◽  
Agricultural Crop ◽  
Initial Growth ◽  
Total Production ◽  
Legume Seeds ◽  
Non Thermal Plasma

The legumes (Fabaceae family) are the second most important agricultural crop, both in terms of harvested area and total production. They are an important source of vegetable proteins and oils for human consumption. Non-thermal plasma (NTP) treatment is a new and effective method in surface microbial inactivation and seed stimulation useable in the agricultural and food industries. This review summarizes current information about characteristics of legume seeds and adult plants after NTP treatment in relation to the seed germination and seedling initial growth, surface microbial decontamination, seed wettability and metabolic activity in different plant growth stages. The information about 19 plant species in relation to the NTP treatment is summarized. Some important plant species as soybean (Glycine max), bean (Phaseolus vulgaris), mung bean (Vigna radiata), black gram (V. mungo), pea (Pisum sativum), lentil (Lens culinaris), peanut (Arachis hypogaea), alfalfa (Medicago sativa), and chickpea (Cicer aruetinum) are discussed. Likevise, some less common plant species i.g. blue lupine (Lupinus angustifolius), Egyptian clover (Trifolium alexandrinum), fenugreek (Trigonella foenum-graecum), and mimosa (Mimosa pudica, M. caesalpiniafolia) are mentioned too. Possible promising trends in the use of plasma as a seed pre-packaging technique, a reduction in phytotoxic diseases transmitted by seeds and the effect on reducing dormancy of hard seeds are also pointed out.

scholarly journals Use of Unmanned Aerial Vehicles (UAVs) Imagery in Phenotyping of Bambara Groundnut

2020 ◽  
Vol 12 (6) ◽  
pp. 12
Author(s):  
Tengku Adhwa Syaherah Tengku Mohd Suhairi ◽  
Siti Sarah Mohd Sinin ◽  
Eranga M. Wimalasiri ◽  
Nur Marahaini Mohd Nizar ◽  
Anil Shekar Tharmandran ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Ground Level ◽  
Bambara Groundnut ◽  
Growth Stages ◽  
Remotely Sensed Data ◽  
Nutrition Security ◽  
Lack Of Information ◽  
Normalised Difference Vegetation Index ◽  
Underutilised Crops ◽  
Crop Parameters

In this experiment, proximal measurements and Unmanned Aerial Vehicle (UAV) imagery was used to determine growth stages for bambara groundnut (Vigna subterranea (L.) Verdc.). The crop is a high potential crop due to its ability to yield in marginal environments, but neglected and underutilised due to lack of information on its growth in different environments. This study evaluated the correlation between Normalised Difference Vegetation Index (NDVI) derived from the ground as well as airborne sensors to test the ability of remotely sensed data to identify growth stages. NDVI and chlorophyll content of bambara groundnut leaves were measured at ground level at 18, 32, 46 and 88 days after planting (DAP) comprising vegetative, flowering, pod formation and maturity growth stages. The UAV imagery for the experimental plots was acquired with 0.2m resolution at maturity. The result showed a significant (p < 0.05) linear relationship between proximal NDVI and chlorophylls content at all growth stages ofgrowth. The R2 varied from 0.57 in the vegetative stage to 0.78 in the flowering stage. Furthermore, NDVI derived from proximal measurements and UAV data showed a significant (p < 0.05) correlation. The observed high correlation between proximal sensors, UAV data and crop parameters suggest that remote sensing technologies can be used for rapid phenotyping to hasten the development of models to assess the performance of underutilised crops for food and nutrition security.

scholarly journals Development of septoria leaf blotch on winter triticale varieties in the conditions of Forest-steppe of Ukraine

ScienceRise ◽  
2016 ◽  
Vol 8 (1 (25)) ◽  
pp. 54
Author(s):  
Олександр Анатолійович Демидов ◽  
Михайло Михайлович Ключевич ◽  
Сергій Іванович Волощук
Keyword(s):  
Forest Steppe ◽  
Septoria Leaf Blotch ◽  
Leaf Blotch ◽  
Winter Triticale
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