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 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 Rate of Nitrogen Rather Than Timing of Application Influence Yield and NUE of Canola in South Australian Mediterranean Environments

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1505
Author(s):  
Amritbir Riar ◽  
Gurjeet Gill ◽  
Glenn K. McDonald
Keyword(s):  
Seed Yield ◽  
N Uptake ◽  
Irrigation Treatment ◽  
Yield Response ◽  
N Recovery ◽  
Growth Stages ◽  
Supplementary Irrigation ◽  
Use Efficiency ◽  
N Management ◽  
The Impact

Canola has a high nitrogen requirement and optimal nitrogen (N) management in environments with variable rainfall is a challenge. This study investigated the impact of timing of N as a single or split application at different growth stages on seed yield, N uptake and water-use efficiency in canola. Nitrogen rates of 100 and 200 kg ha−1 were applied after sowing when two leaves were unfolded or equally split between the rosette, green bud and first flower stages. The experiments were conducted at two sites with contrasting rainfall and a supplementary irrigation treatment at the low rainfall site, generating a third environment. Nitrogen application increased seed yield by up to 20% at a high rainfall site and by up to 77% at a medium rainfall site, but the timing of N did not significantly affect the yield response to N. Seed yield was closely associated with total dry matter production and seed m−2. N-use efficiency was influenced more by N recovery and uptake efficiency, rather than physiological efficiency, which highlights the importance of soil moisture availability and the ability of the crop to exploit soil water and N reserves. The results suggest that better use of subsoil moisture by overcoming some of the subsoil constraints may be an avenue for further improvements in yield and nitrogen-use efficiency (NUE) of canola in this environment.

scholarly journals Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

2017 ◽  
Vol 52 (11) ◽  
pp. 1072-1079 ◽  
Author(s):  
Elisiane Alba ◽  
Eliziane Pivotto Mello ◽  
Juliana Marchesan ◽  
Emanuel Araújo Silva ◽  
Juliana Tramontina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Pinus Elliottii ◽  
Growth Stages ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Infrared Band ◽  
Near Infrared Reflectance ◽  
Planning And Management ◽  
Forest Planning And Management

Abstract: The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and near-infrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P.elliottii had an R2adj of 0.67 with a Syx of 31.46 m3 ha-1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.

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 A Log-Ratio Vegetation Index (LRVI) for Discrimination and Classification of Remote Sensing Data

2020 ◽  
Vol 28 (1) ◽  
pp. 45-70
Author(s):  
Francisco C. Rego ◽  
Irene S.P. Cadima ◽  
Eva K. Strand
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Ground Cover ◽  
Spectral Space ◽  
Electromagnetic Spectrum ◽  
Remotely Sensed Data ◽  
Classical Statistics ◽  
Log Ratio

Discrimination and classification are integral processes for interpreting remotely sensed data. Many spectral vegetation indices have been proposed for discriminating between vegetation, soil, and other ground cover categories. Classical remote sensing show that reflectance in the red (R) and near infrared (NIR) bands of the electromagnetic spectrum have been successful in differentiating between vegetation and other ground cover classes and they are commonly used for this purpose. Here we demonstrate how Fisher’s classical statistics can be applied to develop discriminant functions for commonly used vegetation indices simply using the R and NIR bands. We derive a new vegetation index, the Log-Ratio Vegetation Index (LRVI) and demonstrate its utility in discriminating between cork oak trees and surrounding background in woodlands in Portugal. The LRVI performed better than seven previously developed vegetation indices, likely because of its linear properties in the reflectance density spectral space. The robustness and simplicity of LRVI suggests that it deserves further exploration and should be included for comparison with other vegetation indices and functions in discrimination, classification, and modelling studies. We suggest that the demonstrated approach is widely applicable to development of indices composed of other bands than R and NIR for systems or processes that correlate better with reflectance in other regions of the electromagnetic spectrum.

scholarly journals Use of fluorescence indices as predictors of crop N status and yield for greenhouse sweet pepper crops

2021 ◽  
Author(s):  
Romina de Souza ◽  
M. Teresa Peña-Fleitas ◽  
Rodney B. Thompson ◽  
Marisa Gallardo ◽  
Rafael Grasso ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Management Practices ◽  
N Uptake ◽  
Relative Yield ◽  
Sweet Pepper ◽  
Growth Stages ◽  
N Content ◽  
Fluorescence Measurements ◽  
N Management ◽  
N Status

AbstractTo increase nitrogen (N) use efficiency and reduce water pollution from vegetable production, it is necessary to optimize N management. Fluorescence-based optical sensors are devices that can improve N fertilization through non-destructive field monitoring of crop variables. The aim of this work was to compare the performance of five fluorescence indices (SFR-R, SFR-G, FLAV, NBI-R, and NBI-G) to predict crop variables, as dry matter production, crop N content, crop N uptake, Nitrogen Nutrition Index (NNI), absolute and relative yield, in sweet pepper (Capsicum annuum) crops grown in greenhouse. Fluorescence measurements were periodically made with the Multiplex® 3.6 sensor throughout three cropping cycles subjected to five N application treatments. The performance of fluorescence indices to predict crop variables considered calibration and validation analyses. In general, the five fluorescence indices were strongly related with NNI, crop N content and relative yield. The best performing indices to predict crop N content and NNI at the early stages of the crops (i.e., vegetative and flowering phenological stages) were the SFR indices, both under red (SFR-R) and green (SFR-G) excitation. However, in the final stage of the crop (i.e., harvest stage), the best performing indices were NBI, both under red (NBI-R) and green (NBI-G) excitation, and FLAV. The two SFR indices best predicted relative yield of sweet pepper at early growth stages. Overall, the fluorescence sensor and the fluorescence indices evaluated were able to predict crop variables related to N status in sweet pepper. They have the capacity to be incorporated into best N management practices.

scholarly journals Evaluating Two Crop Circle Active Canopy Sensors for In-Season Diagnosis of Winter Wheat Nitrogen Status

Agronomy ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 201 ◽  
Author(s):  
Qiang Cao ◽  
Yuxin Miao ◽  
Jianning Shen ◽  
Fei Yuan ◽  
Shanshan Cheng ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Triticum Aestivum L ◽  
Coefficient Of Determination ◽  
Growth Stages ◽  
Canopy Reflectance ◽  
Red Edge ◽  
N Management ◽  
N Status

Active crop canopy sensors can be used for non-destructive real-time diagnosis of crop nitrogen (N) status and guiding in-season N management. However, limited studies have compared the performances of two commercially available sensors with three different wavebands: Crop Circle ACS-470 (CC-470) and Crop Circle ACS-430 (CC-430). The objective of this study was to evaluate the performances of CC-470 and CC-430 sensors for estimating winter wheat (Triticum aestivum L.) N status at different measurement heights (40 cm, 70 cm and 100 cm) and growth stages. Results indicated that the canopy reflectance values of CC-470 were more affected by height compared to the CC-430 sensor. The normalized difference red edge (NDRE) and red edge chlorophyll index (CIRE) of CC-430 were stable at the three different measuring heights. The relationships between these indices and the N status indicators were stronger at the Feekes 9–10 stages than the Feekes 6–7 stages for both sensors; however, the CC-430 sensor-based vegetation indices had higher coefficient of determination (R2) values for both stages. It is concluded that the CC-430 sensor is more reliable than CC-470 for winter wheat N status estimation due to its capability of making height-independent measurements. These results demonstrated the importance of considering the influences of height when using active canopy sensors in field measurements.

scholarly journals Deep Phenotyping of Yield-Related Traits in Wheat

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 603
Author(s):  
Lukas Prey ◽  
Urs Schmidhalter
Keyword(s):  
Yield Components ◽  
Vegetation Index ◽  
Near Infrared ◽  
Genotypic Variation ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Sowing Time ◽  
Red Edge ◽  
Grain Number Per Spike ◽  
Organ Level

The complex formation of grain yield (GY) is related to multiple dry matter (DM) traits; however, due to their time-consuming determination, they are not readily accessible. In winter wheat (Triticum aestivum L.), both agronomic treatments and genotypic variation influence GY in interaction with the environment. Spectral proximal sensing is promising for high-throughput non-destructive phenotyping but was rarely evaluated systematically for dissecting yield-related variation in DM traits. Aiming at a temporal, spectral and organ-level optimization, 48 vegetation indices were evaluated in a high-yielding environment in 10 growth stages for the estimation of 31 previously compared traits related to GY formation—influenced by sowing time, fungicide, N fertilization, and cultivar. A quantitative index ranking was evaluated to assess the stage-independent index suitability. GY showed close linear relationships with spectral vegetation indices across and within agronomic treatments (R2 = 0.47–0.67 ***). Water band indices, followed by red edge-based indices, best used at milk or early dough ripeness, were better suited than the widely used normalized difference vegetation index (NDVI). Index rankings for many organ-level DM traits were comparable, but the relationships were often less close. Among yield components, grain number per spike (R2 = 0.24–0.34 ***) and spike density (R2 = 0.23–0.46 ***) were moderately estimated. GY was mainly estimated by detecting total DM rather than the harvest index. Across agronomic treatments and cultivars, seasonal index rankings were the most stable for GY and total DM, whereas traits related to DM allocation and translocation demanded specific index selection. The results suggest using indices with water bands, near infrared/red edge and visible light bands to increase the accuracy of in-season spectral phenotyping for GY, contributing organ-level traits, and yield components, respectively.

scholarly journals Effect of nitrogen fertiliser management on soil mineral nitrogen, nitrous oxide losses, yield and nitrogen uptake of wheat growing in waterlogging-prone soils of south-eastern Australia

Soil Research ◽  
2016 ◽  
Vol 54 (5) ◽  
pp. 619 ◽  
Author(s):  
Robert H. Harris ◽  
Roger D. Armstrong ◽  
Ashley J. Wallace ◽  
Oxana N. Belyaeva
Keyword(s):  
Nitrous Oxide ◽  
Grain Yield ◽  
N Uptake ◽  
Yield Response ◽  
Growth Stages ◽  
N2o Emissions ◽  
Eastern Australia ◽  
Crop N Uptake ◽  
Western Victoria ◽  
N Management

Some of the highest nitrous oxide (N2O) emissions arising from Australian agriculture have been recorded in the high-rainfall zone (>650mm) of south-western Victoria. Understanding the association between nitrogen (N) management, crop N uptake and gaseous losses is needed to reduce N2O losses. Field experiments studied the effect of N-fertiliser management on N2O emissions, crop N uptake and crop productivity at Hamilton and Tarrington in south-western Victoria. Management included five rates of urea-N fertiliser (0, 25, 50, 100 and 200kgN/ha) topdressed at either mid-tillering or first-node growth stages of wheat development; urea-N deep-banded 10cm below the seed at sowing; and urea coated with the nitrification inhibitor DMPP (3,4-dimethylpyrazole phosphate) was either topdressed or deep-banded. Pre-sowing soil profile chemical properties were determined before static chambers were installed to measure N2O losses, accompanied by wheat dry matter, crop N uptake and grain yield and quality, to measure treatment differences. N2O losses increased significantly (P≤0.10) where urea-N was deep-banded, resulting in a 2–2.5-fold increase in losses, compared with the nil N control. The high N2O losses from deep-banding N appeared to result from winter waterlogging triggering gaseous or drainage losses before wheat reached peak growth and demand for N in spring. Despite the high losses from deep-banding urea-N, grain yields were largely unaffected by N management, except at Hamilton in 2012, where topdressed wheat growing in a soil with large reserves of NO3–-N, and later experiencing post-anthesis water deficit resulted in a negative grain yield response. All sites had high concentrations of soil organic carbon (>2.8%) and the potential for large amounts of N mineralisation throughout the growing season to supplement low N fertiliser recovery. However, topdressed urea-N resulted in significant enrichment of crop tissue (P≤0.004) and associated positive response in grain protein compared with the deep banded and nil N treatments. 3,4-Dimethylpyrazole phosphate (DMPP)-coated urea provided no additional benefit to crop yield over conventional urea N. Our study highlighted the importance of synchronising N supply with peak crop N demand to encourage greater synthetic N uptake and mitigation of N2O losses.

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.

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