Agricultural Crop Characteristics Research Articles

Sowing characteristics of agricultural crops using innovative fertilizer complexes

Sowing characteristics of agricultural crops using innovative fertilizer complexes

Hyperspectral reflectance and machine learning to monitor legume biomass and nitrogen accumulation

Hyperspectral remote sensing provides opportunity for a nondestructive tool for estimating biochemical or biophysical characteristics of agricultural crops. Importantly, among rotational legume production, timely identification of the extent of nitrogen accumulation (N accum) by the legumes is vital for optimization of N fertilizer application and simultaneous reduction of environmental impacts (i.e. excess runoff). We developed in-situ hyperspectral data-based models to predict nitrogen concentration (N conc), biomass, and N accum for three legumes (soybean, tepary bean, mothbean) through application and testing of four methods of machine learning (k-Nearest Neighbors [KNN], partial least squares regression [PLS], support vector machine [SVM], and random forest [RF]). An exploration of potential applications of a future hyperspectral satellite (i.e. CHIME) was also conducted by convolving hyperspectral wavelengths to fit hyperspectral bands of the satellite to conduct a similar analysis to that of the in-situ hyperspectral data. Other analytics such as legume type incorporation and direct versus derived N accum from hyperspectral data were explored. Results suggest hyperspectral remote sensing has great promise across both the in-situ and convolved satellite (i.e. CHIME) bands. Moreover, models based on SVM and RF machine learning algorithms had the greatest outcomes across the tested algorithms, with SVM being less computationally expensive than RF. Moreover, legume type was not significantly important to model development, and N accum is best modeled directly rather than being derived independently from the modeling of N concentrations and biomass. These findings enable the modeling of biophysical and biochemical characteristics of legumes using combinations of hyperspectral remote sensing and machine learning methodologies.

Results of using geoinformation and statistical analysis methods to study spectral reflectance characteristics of agricultural crops of Belarus

The results of using geoinformation and statistical analysis methods to study spectral reflectance characteristics of the nine most typical agricultural crops of Belarus are presented. Spectral brightness coefficients and normalised difference vegetation index (NDVI) values were extracted from Landsat-8 multispectral satellite images in the software package ENVI (version 5.2) and analysed based on the methods of zonal statistics in the software complex ArcGIS (version 10.2) and mathematical and statistical analysis in the program Statistica (version 10). The verification of satellite data with the corresponding field measurements was carried out on the basis of correlation analysis, namely, a reliable strong positivelinear relationship between the measured in the field by a specialised GreenSeeker instrument NDVI values and the calculated by Landsat-8 satellite data NDVI values was established. The character of the distribution of spectral brightness coefficients and average NDVI values depending on the type of agricultural crop was assessed using a dispersion analysis, which allowed revealing patterns hidden in the spectral data. In particular, after applying the procedure of multiple comparisons using post hoc tests, it was established which types of crops significantly differ from each other and for which dates these differences were observed. The obtained scientific results were systematised and presented in the form of correspondingtables. The data contained in the tables made it possible to improve the methodology of automated recognition of the crops considered in the study.

АНАЛИЗ РЕГЕНЕРАЦИОННОЙ АКТИВНОСТИ VITIS VINIFERA В УСЛОВИЯХ IN VITRO

Obtaining healthy planting material valuable in terms of economic characteristics of agricultural crops is an advanced task for import substitution and development of own production of high-quality products. The technology of cells and tissues in vitro by the method of clonal micropropagation makes it possible to improve the health of plant material and replicate it in large volumes without changing the genotype of the culture. At the same time, an important component is the regenerative activity of plants, the individual factor is different depending on the breed and variety. The research was conducted in the laboratory "Biotechnology of Agricultural Plants" of the Chechen State University named after A.A. Kadyrov in 2021-2022. The main purpose of the research was to study the regenerative activity of micro-runs of grape varieties obtained in vitro conditions by the apical meristem method. The dynamics of the formation and development of the root system of table grape varieties, the reproduction rate and other parameters at each stage of the technology of microclonal reproduction in vitro conditions are studied. The results of plant development in vitro conditions indicate a high percentage of plant rooting. When studying and evaluating the regenerative activity of Vitis Vinifera, it was found that all the studied varieties are sufficiently rooted after 50-55 days in sterile conditions. By this time, it is possible to transfer microplants to adapt to in vivo conditions, with an average survival rate of 95%. The results obtained in the studies can be used to work with cell and tissue culture in the biotechnology of fruit crops and grapes, as well as in fruit growing and nursery to obtain healthy and homogeneous planting material.

Determination the Normalized Difference Vegetation Index (NDVI) for the NH-38-2 Al-Najaf Plateau, Iraq with the Assistance of the Remote Sensing Technology

The satellites and the data that is provided by are an effective way to distinguish the characteristics of agricultural crops, crops density, and the changes that affect the vegetation cover. The Normalized Difference Vegetation Index (NDVI) has been widely used in conducting a quantitative assessment of plants that cover the surface of the earth, as it revealed many problems facing the agricultural environment, especially those located in arid or semi-arid areas. This study dealt with analyzing the vegetation cover of the plateau NH-38-2 located in Al-Najaf, Iraq by finding the NDVI using remote sensing technology and Geological Information System (GIS), in addition to finding its value for the study area. The study showed that the vegetation cover is largely non-existent and ranges between (0-1) as the region suffers from desertification, which requires the relevant authorities to rectify the matter by developing projects that will increase the vegetation cover as it helps to improve the environment. In addition, the study showed that there is an area that does not exceed 110 km2 that contains vegetation, which is a very small area compared to the total area under study, which is 15225 km2.

Crop Yield Map Development Based on Satellite and UAV Data

Agriculture is one of the promising areas for the introduction of remote sensing technologies. With its help, you can timely receive a wide range of dynamic information about the conditions for the crops growth and development and make the necessary adjustments to obtain the planned result. The paper presents the results of early forecasting of spring wheat yields based on Dove (PlanetScope) satellite data from Planet Labs and DJI P4 Multispectral unmanned data. The maps of the yield of spring wheat were constructed using satellite and unmanned data with a spatial resolution of 3 m and 5 cm, respectively. A statistical assessment of the inhomogeneity of the spectral optical characteristics of agricultural crops has been carried out. The degree of correlation dependence between the value of the integral of the index (NDVI, VARI, MSAVI2, ClGreen) and the yield in different periods of the growing season has been determined.

Using Vegetative Indices to Quantify Agricultural Crop Characteristics

Using Vegetative Indices to Quantify Agricultural Crop Characteristics

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

English

Herbicides can cause negative effects on the morphophysiological characteristics of agricultural crops when proper knowledge about their selectivity is not available. For erva-mate plantations, herbicides which are registered and recommended are scarce. In this sense, this study was carried out to evaluate the selectivity of herbicides applied in different doses to erva-mate. The experiment was installed in a greenhouse at the Federal University of Fronteira Sul (UFFS), Campus Erechim, state of Rio Grande do Sul (RS), Brazil. The experimental design was a randomized block, arranged in a 7 × 4 factorial scheme, with four replications. Factor A comprised the herbicides (tembotrione, chlorimuron-ethyl, oxyfluorfen, sethoxydim + diclosulam, metsulfuron-methyl, Fomesafen + fluazifop-p-butyl and nicosulfuron) and factor B comprised the doses of these herbicides (0, 0.5, 1 and 2‑fold the recommended dose for other crops, on the respective herbicide labels). The phytotoxicity, plant height, stem diameter, dry mass of shoots and roots, chlorophyll content, sub-stomatal CO2 concentration, photosynthetic rate, CO2 consumed, stomatal conductance of water vapors, transpiration rate, and water use efficiency were assessed. Oxyfluorfen, fomesafen + fluazifop-p-butyl and nicosulfuron affected the morphophysiological characteristics of erva-mate plants at all doses tested. The chlorimuron-ethyl and sethoxydim + diclosulam were presented as being potential to be used in erva-mate plantations until 2‑fold the label dose, because they present low plant toxicity and reduced interference in the morphophysiological characteristics of erva-mate. Key words: Chlorimuron-ethyl, alternative crop, chemical management.

Root Traits and Carbon Input in Field‐Grown Sweet Pearl Millet, Sweet Sorghum, and Grain Corn

Little information exists on root morphological characteristics of agricultural crops under field conditions, which can be a major determinant of plant N uptake efficiency and C input to soil. Sweet pearl millet [Pennisetum glaucum (L.) R.BR.] and sweet sorghum [Sorghum bicolor (L.) Moench] are envisioned as energy crops in eastern Canada, to complement corn starch ethanol. The aims of this study were to characterize and compare root biomass and root traits of field‐grown sweet pearl millet, sweet sorghum, and grain corn (Zea mays L.), and to estimate their annual C input to soil. At two sites in eastern Canada, root samples recovered from 30‐cm deep soil cores in sandy loams were weighed and scanned. Image analysis was performed with the WinRhizo software. Roots and shoots were analyzed for C concentration. Estimated C input to soil at harvest was higher for corn (243 g C m−2) than for sorghum and millet (197 and 131 g C m−2, respectively). In contrast, millet and sorghum had the greatest specific root lengths (83, 39, and 22 m g−1 for millet, sorghum, and corn, respectively), and a larger contribution of very fine roots (<0.5 mm diam.) to the total root surface (60–63% for millet and sorghum, and 45–55% for corn). The longer and finer roots of millet and sorghum could contribute to their high N uptake efficiency. However, compared to grain corn, their lower C input to soil needs to be recognized to ensure a balanced C budget.

Regulatory mechanisms tuning ethylen signaling in plants

Plant hormone ethylene regulates a wide range of physiological processes during plant development and coordinates different stress responses. Among others ethylene controls such practically significant characteristics of agricultural crops as fruit ripening rates and plant tolerance to stress conditions. That is why understanding molecular mechanisms underlying ethylene action is one of the basic questions in plant biology that is addressed in the context of both fundamental research and application in agriculture. Ethylene biosynthesis from methionine amino acid and the main points of its signaling pathway from membrane receptors to effector genes are studied in details and widely reviewed. Far less is known about genetic regulation of these two processes although it is the one that ensures accurate plant reaction to different endogenous and exogenous signals and causes the multiplicity of different physiological responses to ethylene. This review summarizes data about regulatory mechanisms of ethylene biosynthesis and signaling. It reports the key transcriptional and post-translational regulatory factors which control expression and stability of the main components of ethylene biosynthesis and signaling pathways, and describes multiple feed-backs supplementing the linear model of ethylene signaling. Particular attention is given to the role of hormonal crosstalk in the process. Different mechanisms of hormonal interaction are illustrated by synergy or antagonism of ethylene and auxin, jasmonates, cytokinins, brassinosteroids. Possible molecular basics of multiplicity of different physiological responses to ethylene is also discussed.

Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices

The paper investigates the value of using distinct vegetation indices to quantify and characterize agricultural crop characteristics at different growth stages. Research was conducted on four crops (corn, soybean, wheat, and canola) over eight years grown under different tillage practices and nitrogen management practices that varied rate and timing. Six different vegetation indices were found most useful, depending on crop phenology and management practices: (a) simple ratio for biomass, (b) NDVI for intercepted PAR, (c) SAVI for early stages of LAI, (d) EVI for later stages of LAI, (e) CIgreen for leaf chlorophyll, (f) NPCI for chlorophyll during later stages, and (g) PSRI to quantify plant senescence. There were differences among varieties of corn and soybean for the vegetation indices during the growing season and these differences were a function of growth stage and vegetative index. These results clearly imply the need to use multiple vegetation indices to best capture agricultural crop characteristics.

A Conceptual Framework for the Simultaneous Extraction of Sub-pixel Spatial Extent and Spectral Characteristics of Crops

The sub-pixel spectral contribution of background soils and shadows hampers the accurate site-specific monitoring of agricultural crop characteristics from aerial or satellite images. To address this problem, the present study combines measured in situ and hyperspectral data in an alternative unmixing algorithm. The proposed algorithm, referred to as Soil Modeling Mixture Analysis (SMMA), incorporates a soil reflectance model in a traditional unmixing algorithm and as such opens up the opportunity to simultaneously extract the sub-pixel spatial extent of crops as well as its pure spectral information. The performance of the algorithm is evaluated using a soil moisture reflectance model, calibrated for an in situ measured Albic Luvisol dataset. Synthetic mixtures, i.e., compiled from in situ measured hyperspectral bare soil and citrus tree canopy spectra, were decomposed and the sub-pixel crop cover fractions (R 2 � 0.94, RMSE � 0.03) and pure vegetation signals (average extraction error 350 to 2,500 nm � 0.017, RMSE � 0.02) were adequately extracted from the mixtures.

Application of narrow-band TBVI in estimating fruit yield in citrus

Recently, the two-band vegetation index (TBVI) has been developed as an alternative to the conventional normalised difference vegetation index (NDVI) for assessing various agricultural crop characteristics. The potential of the narrow-band TBVI derived from airborne hyperspectral imagery to predict fruit yield in citrus was examined in this paper. Hyperspectral images in 72 visible and near-infrared (NIR) wavelength bands (from 407 to 898 nm) were acquired three times over a citrus orchard in Japan on May 26, June 21 and July 21, 2005 by an airborne imaging spectrometer for applications (AISA) Eagle system. Spectra for individual trees were obtained by averaging reflectance values of all pixels on each citrus canopy that was recognised from the acquired images. Narrow-band TBVI involving all possible two-band combinations of 72 channels was tested in terms of its potential to predict fruit yield on individual citrus trees. Multiple linear regression (MLR) models based on several significant wavelengths were also evaluated. Results indicate that the hyperspectral image obtained on May 26 demonstrated the highest correlation with citrus yield. This suggests that canopy features at the fast vegetation growth stage provide more relevant information on the yield variability among individual trees. Moreover, the TBVI showed a greater potential than the simple combination of several significant wavelengths in providing an indication of fruit yield in citrus. Based on the hyperspectral image obtained on May 26, the TBVIs calculated for the red and NIR regions were significantly correlated with citrus yield, and the “red edge” region showed a higher relevance than the red region in calculating the TBVI that has a higher correlation with citrus yield. The TBVI based on the 823 nm (NIR) and 728 nm (red edge) wavelengths was found to provide optimal citrus yield information ( R 2 =0.5795, RRMSE=0.6636). Due to the significant effect of the alternate bearing mechanism on individual citrus trees, canopy size cannot be used as a single predictor for citrus yield ( R 2 =0.4516, RRMSE=0.8298). However, by incorporating the TBVI and canopy size into the model, the prediction result was greatly improved ( R 2 =0.6913, RRMSE=0.6071). This suggests that the yield on individual trees is determined by the combined effect of the TBVI and canopy size. This study demonstrates the potential of narrow-band TBVI derived from airborne hyperspectral imagery to predict the fruit yield in citrus. Yield estimates can provide valuable information for forecasting yields, planning harvest schedules and generating prescription maps for tree-specific application of alternate bearing control measures and other management practices.

A multivariate model for ordinal trait analysis

Many economically important characteristics of agricultural crops are measured as ordinal traits. Statistical analysis of the genetic basis of ordinal traits appears to be quite different from regular quantitative traits. The generalized linear model methodology implemented via the Newton-Raphson algorithm offers improved efficiency in the analysis of such data, but does not take full advantage of the extensive theory developed in the linear model arena. Instead, we develop a multivariate model for ordinal trait analysis and implement an EM algorithm for parameter estimation. We also propose a method for calculating the variance-covariance matrix of the estimated parameters. The EM equations turn out to be extremely similar to formulae seen in standard linear model analysis. Computer simulations are performed to validate the EM algorithm. A real data set is analyzed to demonstrate the application of the method. The advantages of the EM algorithm over other methods are addressed. Application of the method to QTL mapping for ordinal traits is demonstrated using a simulated baclcross (BC) population.

Оценка основных параметров сельскохозяйственных посевов по спектру отражения растительности в оптическом диапазоне

Оценка основных параметров сельскохозяйственных посевов по спектру отражения растительности в оптическом диапазоне

Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics

The objective of this paper is to determine spectral bands that are best suited for characterizing agricultural crop biophysical variables. The data for this study comes from ground-level hyperspectral reflectance measurements of cotton, potato, soybeans, corn, and sunflower. Reflectance was measured in 490 discrete narrow bands between 350 and 1,050 nm. Observed crop characteristics included wet biomass, leaf area index, plant height, and (for cotton only) yield. Three types of hyperspectral predictors were tested: optimum multiple narrow band reflectance (OMNBR), narrow band normalized difference vegetation index (NDVI) involving all possible two-band combinations of 490 channels, and the soil-adjusted vegetation indices. A critical problem with OMNBR models was that of “over fitting” (i.e., using more spectral channels than experimental samples to obtain a highly maximum R 2 value). This problem was addressed by comparing the R 2 values of crop variables with the R 2 values computed for random data of a large sample size. The combinations of two to four narrow bands in OMNBR models explained most (64% to 92%) of the variability in crop biophysical variables. The second part of the paper describes a rigorous search procedure to identify the best narrow band NDVI predictors of crop biophysical variables. Special narrow band lambda (λ 1) versus lambda (λ 2) plots of R 2 values illustrate the most effective wavelength combinations (λ 1 and λ 2) and bandwidths (Δλ 1 and Δλ 2) for predicting the biophysical quantities of each crop. The best of these two-band indices were further tested to see if soil adjustment or nonlinear fitting could improve their predictive accuracy. The best of the narrow band NDVI models explained 64% to 88% variability in different crop biophysical variables. A strong relationship with crop characteristics is located in specific narrow bands in the longer wavelength portion of the red (650 nm to 700 nm), with secondary clusters in the shorter wavelength portion of green (500 nm to 550 nm), in one particular section of the near-infrared (900 nm to 940 nm), and in the moisture sensitive near-infrared (centered at 982 nm). This study recommends a 12 narrow band sensor, in the 350 nm to 1,050 nm range of the spectrum, for optimum estimation of agricultural crop biophysical information.

Agricultural crop condition monitoring using airborne C-band synthetic aperture radar in southern Alberta

Abstract Applications of airborne C-band synthetic aperture radar imagery for determining variations in agricultural crop characteristics were investigated at a test site in southern Alberta, Canada. Synthetic aperture radar (SAR) imagery and ground-based crop characteristics data were acquired on 19–20 July 1994 for wheat, canola, beans, peas, and wheat + alfalfa cultivated under a variety of irrigation conditions. The results indicate that the statistically significant relationships that were derived between the ground-based data and SAR imagery are a function of crop type, crop condition parameter, and image processing procedures, and that crop characteristics such as leaf area index and plant height are negatively correlated with radar backscatter.

A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap

In an extension of previous simulation studies, a transformation of actual TM data in the six reflective bands is described which achieves three objectives: a fundamental view of TM data structures is presented, the vast majority of data variability is concentrated in a few (three) features, and the defined features can be directly associated with physical scene characteristics. The underlying TM data structure, based on three TM scenes as well as simulated data, is described, as are the general spectral characteristics of agricultural crops and other scene classes in the transformed data space.

Spectral reflectance characteristics of agricultural crops and application to crop growth monitoring

Information provided by analysis of remotely sensed multispectral reflectance data of agricultural crops can be applied to improve crop type inventory and crop yield assessment. A general introduction into the principles of canopy reflectance is presented. A deterministic reflectance model developed by G.H. Suits has been used to demonstrate the influence of varying crop parameters on canopy relectance at significant wavelength values. Based on reflectance data from three spectral bands in the visible light and near-infrared region, crop growth monitoring parameters are proposed for detection of changing soil cover, leaf area index and canopy colour.