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1.
Sensors (Basel) ; 21(2)2021 Jan 17.
Artigo em Inglês | MEDLINE | ID: mdl-33477350

RESUMO

Nitrogen is an important indicator for monitoring wheat growth. The rapid development and wide application of non-destructive detection provide many approaches for estimating leaf nitrogen content (LNC) in wheat. Previous studies have shown that better results have been obtained in the estimation of LNC in wheat based on spectral features. However, the lack of automatically extracted features leads to poor universality of the estimation model. Therefore, a feature fusion method for estimating LNC in wheat by combining spectral features with deep features (spatial features) was proposed. The deep features were automatically obtained with a convolutional neural network model based on the PyTorch framework. The spectral features were obtained using spectral information including position features (PFs) and vegetation indices (VIs). Different models based on feature combination for evaluating LNC in wheat were constructed: partial least squares regression (PLS), gradient boosting decision tree (GBDT), and support vector regression (SVR). The results indicate that the model based on the fusion feature from near-ground hyperspectral imagery has good estimation effect. In particular, the estimation accuracy of the GBDT model is the best (R2 = 0.975 for calibration set, R2 = 0.861 for validation set). These findings demonstrate that the approach proposed in this study improved the estimation performance of LNC in wheat, which could provide technical support in wheat growth monitoring.

2.
Plant Methods ; 16: 132, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33005214

RESUMO

Background: The tiller number per unit area is one of the main agronomic components in determining yield. A real-time assessment of this trait could contribute to monitoring the growth of wheat populations or as a primary phenotyping indicator for the screening of cultivars for crop breeding. However, determining tiller number has been conventionally dependent on tedious and labor-intensive manual counting. In this study, an automatic tiller-counting algorithm was developed to estimate the tiller density under field conditions based on terrestrial laser scanning (TLS) data. The novel algorithm, which is named ALHC, involves two steps: (1) the use of an adaptive layering (AL) algorithm for cluster segmentation and (2) the use of a hierarchical clustering (HC) algorithm for tiller detection among the clusters. Three field trials during the 2016-2018 wheat seasons were conducted to validate the algorithm with twenty different wheat cultivars, three nitrogen levels, and two planting densities at two ecological sites (Rugao & Xuzhou) in Jiangsu Province, China. Result: The results demonstrated that the algorithm was promising across different cultivars, years, growth stages, planting densities, and ecological sites. The tests from Rugao and Xuzhou in 2016-2017 and Rugao in 2017-2018 showed that the algorithm estimated the tiller number of the wheat with regression coefficient (R2) values of 0.61, 0.56 and 0.65, respectively. In short, tiller counting with the ALHC generally underestimated the tiller number and performed better for the data with lower plant densities, compact plant types and the jointing stage, which were associated with overlap and noise between plants and inside the dense canopy. Conclusions: Differing from the previous methods, the ALHC proposed in this paper made full use of 3D crop information and developed an automatic tiller counting method that is suitable for the field environment.

3.
Glob Chang Biol ; 2020 Oct 16.
Artigo em Inglês | MEDLINE | ID: mdl-33063940

RESUMO

Crop production will likely face enormous challenges against the occurrences of extreme climatic events projected under future climate change. Heat waves that occur at critical stages of the reproductive phase have detrimental impacts on the grain yield formation of rice (Oryza sativa). Accurate estimates of these impacts are essential to evaluate the effects of climate change on rice. However, the accuracy of these predictions by crop models has not been extensively tested. In this study, we evaluated 14 rice growth models against four year phytotron experiments with four levels of heat treatments imposed at different times after flowering. We found that all models greatly underestimated the negative effects of heat on grain yield, suggesting that yield projections with these models do not reflect food shocks that may occur under short-term extreme heat stress (SEHS). As a result, crop model ensembles do not help to provide accurate estimates of grain yield under heat stress. We examined the functions of grain-setting rate response to temperature (TRF_GS) used in eight models and showed that adjusting the effective periods of TRF_GS improved the model performance, especially for models simulating accumulative daily temperature effects. For TRF_GS which uses daily maximum temperature averaged for the effective period, the models provided better grain yield estimates by using maximum temperatures averaged only when daily maximum temperatures exceeded the base temperature (Tbase ). An alternative method based on heating-degree days and stage-dependent heat sensitivity parameters further decreased the prediction uncertainty of grain yield under heat stress, where stage-dependent heat sensitivity was more important than heat dose for model improvement under SEHS. These results suggest the limitation of the applicability of existing rice models to variable climatic conditions and the urgent need for an alternative grain-setting function accounting for the stage-dependent heat sensitivity.

4.
J Exp Bot ; 71(19): 6015-6031, 2020 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-32964926

RESUMO

Grain yield of wheat and its components are very sensitive to heat stress at the critical growth stages of anthesis and grain filling. We observed negative impacts of heat stress on biomass partitioning and grain growth in environment-controlled phytotron experiments over 4 years, and we quantified relationships between the stress and grain number and potential grain weight at anthesis and during grain filling using process-based heat stress routines. These relationships included reduced grain set under stress at anthesis and decreased potential grain weight under stress during early grain filling. Biomass partitioning to stems and spikes was modified under heat stress based on a source-sink relationship. The integration of our process-based stress routines into the original WheatGrow model significantly enhanced the predictions of the biomass dynamics of the stems and spikes, the grain yield, and the yield components under heat stress. Compared to the original model, the improved version decreased the simulation errors for grain yield, grain number, and grain weight by 73%, 48%, and 49%, respectively, in an evaluation using independent data under heat stress in the phytotron conditions. When tested with data obtained under field conditions, the improved model showed a good ability to reproduce the decreasing dynamics of grain yield and its components with increasing post-anthesis temperatures. Sensitivity analysis showed that the improved model was able to reproduce the responses to various observed heat-stress treatments. These improvements to the crop model will be of significant importance for assessing the effects on crop production of projected increases in heat-stress events under future climate scenarios.

5.
Aquat Toxicol ; 224: 105504, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32450458

RESUMO

Due to their unique structure and properties, carbon nanotubes (CNTs) released into the aquatic environment can potentially influence the behavior of other coexisting pollutants, thereby altering their toxicity to aquatic organisms. In this study, the toxicities of multi-walled CNTs and three heavy metals, copper (Cu), cadmium (Cd) and zinc (Zn) were determined individually. Following this, CNTs with low concentrations (1 and 5 mg/L) were co-exposed with Cu, Cd or Zn to the microalgae Scenedesmus obliquus, to investigate the effects and underlying mechanisms of CNTs on metal toxicity. Results showed that CNTs, especially at a concentration of 5 mg/L, promoted algae growth and enhanced photosynthetic efficiency via increasing exciton trap efficiency and quantum yield for electron transport. Introduction of CNTs appeared to alleviate the adverse effects of Cu, Cd or Zn on microalgae, indicated by algae growth, total chlorophyll content and photosynthetic indices. However, these effects differed greatly for different metals, depending on both the toxicity of each metal and the exposure period (4 day and 8 day). Enhancement of photosynthesis and interference of metal uptake by CNTs, have a crucial role in the effects of CNTs on metal toxicity.


Assuntos
Água Doce/química , Metais Pesados/toxicidade , Microalgas/efeitos dos fármacos , Nanotubos de Carbono/química , Scenedesmus/efeitos dos fármacos , Poluentes Químicos da Água/toxicidade , Organismos Aquáticos/efeitos dos fármacos , Cádmio/toxicidade , Clorofila/metabolismo , Cobre/toxicidade , Fotossíntese/efeitos dos fármacos , Zinco/toxicidade
6.
Sensors (Basel) ; 20(10)2020 May 20.
Artigo em Inglês | MEDLINE | ID: mdl-32443796

RESUMO

An instrument developed to monitor and diagnose crop growth can quickly and non-destructively obtain crop growth information, which is helpful for crop field production and management. Focusing on the problems with existing two-band instruments used for crop growth monitoring and diagnosis, such as insufficient information available on crop growth and low accuracy of some growth indices retrieval, our research team developed a portable three-band instrument for crop-growth monitoring and diagnosis (CGMD) that obtains a larger amount of information. Based on CGMD, this paper carried out studies on monitoring wheat growth indices. According to the acquired three-band reflectance spectra, the combined indices were constructed by combining different bands, two-band vegetation indices (NDVI, RVI, and DVI), and three-band vegetation indices (TVI-1 and TVI-2). The fitting results of the vegetation indices obtained by CGMD and the commercial instrument FieldSpec HandHeld2 was high and the new instrument could be used for monitoring the canopy vegetation indices. By fitting each vegetation index to the growth index, the results showed that the optimal vegetation indices corresponding to leaf area index (LAI), leaf dry weight (LDW), leaf nitrogen content (LNC), and leaf nitrogen accumulation (LNA) were TVI-2, TVI-1, NDVI (R730, R815), and NDVI (R730, R815), respectively. R2 values corresponding to LAI, LDW, LNC and LNA were 0.64, 0.84, 0.60, and 0.82, respectively, and their relative root mean square error (RRMSE) values were 0.29, 0.26, 0.17, and 0.30, respectively. The addition of the red spectral band to CGMD effectively improved the monitoring results of wheat LAI and LDW. Focusing the problem of vegetation index saturation, this paper proposed a method to construct the wheat-growth-index spectral monitoring models that were defined according to the growth periods. It improved the prediction accuracy of LAI, LDW, and LNA, with R2 values of 0.79, 0.85, and 0.85, respectively, and the RRMSE values of these growth indices were 0.22, 0.23, and 0.28, respectively. The method proposed here could be used for the guidance of wheat field cultivation.

7.
Environ Sci Pollut Res Int ; 27(23): 28749-28767, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-32468373

RESUMO

Recent research interest has focused on microalgae cultivation for biogas slurry purification and biogas upgrading due to the requirement of high efficiency for nutrient uptake and CO2 capture, with economic feasibility and environmental benefits. Numerous studies have suggested that biogas slurry purification and biogas upgrading can occur simultaneously via microalgae-based technology. However, there is no comprehensive review on this technology with respect to the nutrient removal from biogas slurry and biogas upgrading. This article summarizes microalgal cultivation with biogas slurry and biogas from anaerobic digestion. The parameters, techniques, and modes of microalgae cultivation have been discussed in detail to achieve high efficiency in biogas slurry purification and biogas upgrading. In addition, the evaluation of energy efficiency and safety has also been explored. Compared with mono-cultivation of microalgae and co-cultivation of microalgae and bacteria, microalgae-fungi symbiosis has demonstrated greater development prospect and higher energy efficiency and the energy consumption for pollutants and CO2 removal were 14.2-39.0% · USD-1 and 19.9-23.3% · USD-1, respectively. Further, a sustainable recycling scheme is proposed for the purification of biogas slurry from anaerobic digestion process and biogas upgrading via microalgae-based technology.


Assuntos
Poluentes Ambientais , Microalgas , Biocombustíveis , Biomassa , Dióxido de Carbono , Nutrientes
8.
Plant Methods ; 16: 23, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32127910

RESUMO

Background: Image processing techniques have been widely used in the analysis of leaf characteristics. Earlier techniques for processing digital RGB color images of plant leaves had several drawbacks, such as inadequate de-noising, and adopting normal-probability statistical estimation models which have few parameters and limited applicability. Results: We confirmed the skewness distribution characteristics of the red, green, blue and grayscale channels of the images of tobacco leaves. Twenty skewed-distribution parameters were computed including the mean, median, mode, skewness, and kurtosis. We used the mean parameter to establish a stepwise regression model that is similar to earlier models. Other models based on the median and the skewness parameters led to accurate RGB-based description and prediction, as well as better fitting of the SPAD value. More parameters improved the accuracy of RGB model description and prediction, and extended its application range. Indeed, the skewed-distribution parameters can describe changes of the leaf color depth and homogeneity. Conclusions: The color histogram of the blade images follows a skewed distribution, whose parameters greatly enrich the RGB model and can describe changes in leaf color depth and homogeneity.

9.
Sensors (Basel) ; 20(6)2020 Mar 12.
Artigo em Inglês | MEDLINE | ID: mdl-32178244

RESUMO

Critical nitrogen (N) dilution curves (CNDCs) have been developed to describe the dilution dynamic of N and to diagnose N status in plants. In this study, to develop a convenient alternative CNDC determination method, four field experiments involving different N rates (0-360 kg N ha-1) and six wheat varieties were performed at different eco-sites from 2014 to 2019. The normalised difference red-edge (NDRE) index extracted from the RapidSCAN CS-45 (Holland Scientific Inc., Lincoln, NE, USA) sensor was used as a driving factor instead of plant dry matter (PDM) to establish a new alternative winter wheat CNDC. The newly developed CNDC was described by the equation Nc = 0.90NDRE-0.88, when NDRE values were ≤ 0.19 and constant Nc = 3.81%, which was independent of the NDRE values. Compared to PDM-derived CNDC (R2 = 0.73) developed with the same dataset, a comparable precision was obtained using NDRE-derived CNDC (R2 = 0.76) and both CNDCs could accurately discriminate wheat N status. Moreover, the NDRE could be inexpensively and rapidly measured using the active sensor. The relationship between NDRE-derived CNDC and grain yield was also analysed to facilitate in-season N management, and the R2 value reached 0.79 and 0.87 at jointing and booting stages, respectively. The NDRE-based CNDC can be used to effectively diagnose wheat N status and as an alternative approach for non-destructive determination of crop N levels.

10.
Chemosphere ; 244: 125514, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31812061

RESUMO

Atrazine is a widely-applied herbicide used primarily to control weeds, which can persist in the ecosystem and exert potential toxicity to phytoplankton in the aquatic environment. In this study, acute toxicity of atrazine on microalgae Chlorella sp. was investigated with different initial cell densities (1 × 105 and 1 × 106 cells mL-1) and exposure periods (4 d and 8 d). Both growth rate and photosynthetic parameters of the microalgae in response of atrazine stress were determined to find out the sensitive indices and toxicological mechanisms. Because of the independence of initial cell density as well as the high sensitivity and reliability, the performance index PIABS was verified as the most convincing photosynthetic parameter for indicating IC50 of atrazine on Chlorella sp., being superior to the traditional parameters of growth rate and FV/FM. The IP amplitude (ΔFIP, fluorescence amplitude of the I-to-P-rise in the OJIP curve) was another sensitive biomarker to reflect atrazine stress. Results from chlorophyll fluorescence transient revealed that atrazine damaged the photosystem II (PS II) reaction center, suppressed the electron transport at the donor and receptor sides, and acted on the absorption, transfer, and utilization of light energy. Our results provide confirmatory references for understanding the toxicity and mechanisms of atrazine on freshwater microalgae.


Assuntos
Atrazina/toxicidade , Chlorella/fisiologia , Herbicidas/toxicidade , Chlorella/metabolismo , Clorofila , Ecossistema , Transporte de Elétrons , Fluorescência , Água Doce , Microalgas/metabolismo , Fotossíntese , Complexo de Proteína do Fotossistema II/metabolismo , Fitoplâncton/metabolismo , Reprodutibilidade dos Testes , Testes de Toxicidade , Poluentes Químicos da Água/toxicidade
11.
Sensors (Basel) ; 19(20)2019 Oct 12.
Artigo em Inglês | MEDLINE | ID: mdl-31614815

RESUMO

Nitrogen (N) content is an important basis for the precise management of wheat fields. The application of unmanned aerial vehicles (UAVs) in agriculture provides an easier and faster way to monitor nitrogen content. Previous studies have shown that the features acquired from UAVs yield favorable results in monitoring wheat growth. However, since most of them are based on different vegetation indices, it is difficult to meet the requirements of accurate image interpretation. Moreover, resampling also easily ignores the structural features of the image information itself. Therefore, a spectral-spatial feature is proposed combining vegetation indices (VIs) and wavelet features (WFs), especially the acquisition of wavelet features from the UAV image, which was transformed from the spatial domain to the frequency domain with a wavelet transformation. In this way, the complete spatial information of different scales can be obtained to realize good frequency localization, scale transformation, and directional change. The different models based on different features were compared, including partial least squares regression (PLSR), support vector regression (SVR), and particle swarm optimization-SVR (PSO-SVR). The results showed that the accuracy of the model based on the spectral-spatial feature by combining VIs and WFs was higher than that of VIs or WF indices alone. The performance of PSO-SVR was the best (R2 = 0.9025, root mean square error (RMSE) = 0.3287) among the three regression algorithms regardless of the use of all the original features or the combination features. Our results implied that our proposed method could improve the estimation accuracy of aboveground nitrogen content of winter wheat from UAVs with consumer digital cameras, which have greater application potential in predicting other growth parameters.

12.
Bioresour Technol ; 293: 122051, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31472405

RESUMO

In this work, a 30-days batched mesophilic assay on pretreated food waste (PFW) under different inoculum/substrate (I/S) ratios (1:5, 1:2, 1:1, 2:1, 4:1 and 1:0) was carried out, to target the most important parameters in AD matrix on regulating iron (Fe) chemical speciation. Correlation coefficients were calculated within four Fe chemical forms and AD parameters of pH, volatile fatty acids (VFAs), inorganic acid radicals (IARs), and alkalinity. Results showed that IARs were not key factors on regulating Fe speciation. Without acidification, IARs showed weak correlations (coefficients < 0.40) with Fe chemical dynamics while other parameters showed stronger correlations (coefficients ≥ 0.60). Under acidification, VFAs initiated the conversion of exchangeable Fe into water soluble fraction. Residual fraction might play important role in regulating Fe shifting to more bioavailable states.


Assuntos
Ácidos Graxos Voláteis , Ferro , Anaerobiose , Reatores Biológicos , Alimentos
13.
Artigo em Inglês | MEDLINE | ID: mdl-31405094

RESUMO

The Yi River, the second longest river in Shandong Province, China, flows through Linyi City and is fed by three tributary rivers, Beng River, Liuqing River, and Su River in the northeastern part of the city. In this study, we determined the concentrations of five heavy metals (Cr, Ni, Cu, Zn, and Pb) in water, sediment, and aquatic macrophyte samples collected from the junction of the four rivers and evaluated the potential ecological risk of heavy metal pollution. Most of the heavy metals in water were in low concentrations with the water quality index (WQI) below 1, suggesting low metal pollution. The sediments showed low heavy metal concentrations, suggesting a low ecological risk based on the potential ecological risk index (RI) and the geo-accumulation index (Igeo). The aquatic plant species Potamogeton crispus accumulated considerable amounts of heavy metals, which were closely related to the metal concentrations of the sediment. The plant species Salvinia natans also showed an excellent metal accumulation capability. Based on our results, the junction of the four rivers is only slightly polluted in terms of heavy metals, and the plant species P. crispus is a suitable bioindicator for sediment heavy metal pollution.


Assuntos
Metais Pesados/análise , Poluentes Químicos da Água/análise , China , Cidades , Monitoramento Ambiental , Sedimentos Geológicos/análise , Metais Pesados/metabolismo , Medição de Risco , Rios , Traqueófitas/química , Traqueófitas/metabolismo , Poluentes Químicos da Água/metabolismo
14.
Front Plant Sci ; 10: 818, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31293611

RESUMO

Drought is among the main environmental stressors that reduces wheat production. Nitrogen (N) availability affects plant adaptation to abiotic stress, but the effect of low N (LN) on drought tolerance is unclear. To identify the effect of LN priming on water-deficit stress tolerance in wheat seedlings, we primed cultivar Yangmai158 with 0.25 mM N for 7 days, and then added 20% polyethylene glycol 6000 as a water-deficit treatment for 5 days. The net photosynthetic rate (Pn), plant biomass, and plant growth rate (GR) were significantly reduced under water-deficit conditions; such decreases were less severe in LN-primed (LND) plants than non-primed (CKD) plants. The leaf relative water content (LRWC) decreased under water-deficit conditions, which in turn led to a reduced transpiration rate, stomatal conductance, and intercellular CO2 concentration (C i), causing a stomatal limitation on photosynthesis. LN priming also enhanced root growth, resulting in a higher LRWC and less stomatal limitation in LND plants than CKD plants. PSII quantum efficiency, photochemical quenching, and maximum PSII quantum efficiency were reduced under water-deficit conditions, indicating photoinhibition. However, LN priming increased the electron flux to photorespiration and the Mehler pathway, reducing photoinhibition. In conclusion, LN priming improved the leaf water status and increased alternative electron flux to attenuate photoinhibition, thus alleviating the inhibition of photosynthesis, and growth due to water deficiency.

15.
Bioresour Technol ; 291: 121806, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31326683

RESUMO

A biophotoreactor with a transparent glass flat panel with polymethyl methacrylate (PMMA) grid columnar for enhanced biofilm growth with Rhodopseudomonas palustris GCA009 was developed and tested at 590 nm incident light. Continuous photofermentative hydrogen production from glucose was tested using this novel reactor. At light intensity of 210 W/m2, feed substrate concentration of 56.0 mmol/L, and crossflow velocity of 1.68 × 10-6 m/s, a maximum hydrogen production rate of 32.6 mmol/L-d (3.56 mmol/m2-h), hydrogen yield of 1.15 mol H2/mol glucose and light conversion efficiency of 5.34% can be achieved. Since the revised grid columnar effectively enlarged the surface area of reactor and enhanced cell attachment, the present reactor design led to higher hydrogen production rates than literature works.


Assuntos
Fotobiorreatores , Rodopseudomonas , Biofilmes , Hidrogênio , Luz
16.
Front Plant Sci ; 10: 771, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31275335

RESUMO

To develop efficient N management strategies for high wheat NUE and minimizing the environmental impact of N losses under asymmetric warming, 15N micro-plot experiments were conducted to investigate the effects of night-warming during winter (warming by 1.47-1.56°C from tillering to jointing), spring (warming by 1.68-1.82°C from jointing to booting), and winter + spring (warming by 1.53-1.64°C from tillering to booting) on root growth and distribution of winter wheat, the fates of 15N-labeled fertilizer, and their relationships in 2015-2017. The results showed that night-warming increased the recovery of basal 15N and top-dressed 15N, while reduced the residual and loss of basal 15N and top-dressed 15N. The losses decreases of top-dressed 15N were higher than those of basal 15N, indicating that night-warming reduced losses of fertilizer 15N mainly by reducing losses of top-dressed 15N. Moreover, pre-anthesis root dry matter accumulation rate in 0-60 cm soil layer were promoted, resulted in improved root biomass and root/shoot ratio, which favored increasing recovery of fertilizer 15N and reducing losses of fertilizer 15N. Furthermore, residual fertilizer 15N content in 0-100 cm soil layer was reduced, which was associated with improved root weight density in 0-60 cm soil layer, resulted in reduced leaching losses of fertilizer 15N. The path analysis showed that root dry matter distribution in 0-20 cm soil layer was the most important in contributing to reducing losses of total fertilizer 15N compared with other soil layers. Two years data showed that winter and spring night-warming gave better root growth and distribution in 0-20 cm soil layer, resulted in reduced the losses of fertilizer 15N and improved the recovery of fertilizer 15N, while maximizing grain yield of winter wheat, and winter + spring night-warming resulted in higher advantages than winter night-warming and spring night-warming.

17.
Waste Manag ; 90: 100-120, 2019 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-31088666

RESUMO

The different physicochemical properties of various agro-waste biomasses require a diversity of bioenergy utilization patterns. This study investigated the characteristics of a total of 74 manures and 78 crop straw samples from East China to identify the primary characteristic indicators that are essential to distinguish specific agro-wastes from others. Principal component analysis was applied, to discover critical features of biomass for the decision-making regarding the bioenergy production mode. The results identified the following four principal components of manures: "organic nutrients", "metals", "bioavailability of nutrients", and "toxic potential". For crop straws these were "lignocellulose/plant strength elements", "organics/inorganic metals for chlorophyll", "C/N", and "Na/Zn/fixed C". Considering the practical application significance of anaerobic digestion (AD) in the ecological civilization construction in rural China, the theoretical bio-methane potential was calculated based on the average values of different agro-biomasses from different areas. The results were 335.5-620.4 STP mL/g VS for manures and 434.0-540.3 STP mL/g VS for crop straws.


Assuntos
Esterco , Metano , Biomassa , China
18.
Sensors (Basel) ; 19(5)2019 Mar 05.
Artigo em Inglês | MEDLINE | ID: mdl-30841552

RESUMO

Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop growth information. This study intended to expand the applicability of GreenSeeker in monitoring growth status and predicting grain yield of winter wheat (Triticum aestivum L.). Four field experiments with multiple wheat cultivars and N treatments were conducted during 2013⁻2015 for obtaining canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) synchronized with four agronomic parameters: leaf area index (LAI), leaf dry matter (LDM), leaf nitrogen concentration (LNC), and leaf nitrogen accumulation (LNA). Duration models based on NDVI and RVI were developed to monitor these parameters, which indicated that NDVI and RVI explained 80%, 68⁻70%, 10⁻12%, and 67⁻73% of the variability in LAI, LDM, LNC and LNA, respectively. According to the validation results, the relative root mean square error (RRMSE) were all <0.24 and the relative error (RE) were all <23%. Considering the variation among different wheat cultivars, the newly normalized vegetation indices rNDVI (NDVI vs. the NDVI for the highest N rate) and rRVI (RVI vs. the RVI for the highest N rate) were calculated to predict the relative grain yield (RY, the yield vs. the yield for the highest N rate). rNDVI and rRVI explained 77⁻85% of the variability in RY, the RRMSEs were both <0.13 and the REs were both <6.3%. The result demonstrates the feasibility of monitoring growth parameters and predicting grain yield of winter wheat with portable GreenSeeker sensor.


Assuntos
Triticum/crescimento & desenvolvimento , Grão Comestível , Monitorização Fisiológica , Nitrogênio/metabolismo , Folhas de Planta/crescimento & desenvolvimento , Folhas de Planta/metabolismo , Folhas de Planta/fisiologia , Poaceae/crescimento & desenvolvimento , Poaceae/fisiologia , Estações do Ano , Triticum/metabolismo , Triticum/fisiologia
19.
Plant Methods ; 15: 17, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30828356

RESUMO

Background: Aboveground biomass (AGB) is a widely used agronomic parameter for characterizing crop growth status and predicting grain yield. The rapid and accurate estimation of AGB in a non-destructive way is useful for making informed decisions on precision crop management. Previous studies have investigated vegetation indices (VIs) and canopy height metrics derived from Unmanned Aerial Vehicle (UAV) data to estimate the AGB of various crops. However, the input variables were derived either from one type of data or from different sensors on board UAVs. Whether the combination of VIs and canopy height metrics derived from a single low-cost UAV system can improve the AGB estimation accuracy remains unclear. This study used a low-cost UAV system to acquire imagery at 30 m flight altitude at critical growth stages of wheat in Rugao of eastern China. The experiments were conducted in 2016 and 2017 and involved 36 field plots representing variations in cultivar, nitrogen fertilization level and sowing density. We evaluated the performance of VIs, canopy height metrics and their combination for AGB estimation in wheat with the stepwise multiple linear regression (SMLR) and three types of machine learning algorithms (support vector regression, SVR; extreme learning machine, ELM; random forest, RF). Results: Our results demonstrated that the combination of VIs and canopy height metrics improved the estimation accuracy for AGB of wheat over the use of VIs or canopy height metrics alone. Specifically, RF performed the best among the SMLR and three machine learning algorithms regardless of using all the original variables or selected variables by the SMLR. The best accuracy (R 2 = 0.78, RMSE = 1.34 t/ha, rRMSE = 28.98%) was obtained when applying RF to the combination of VIs and canopy height metrics. Conclusions: Our findings implied that an inexpensive approach consisting of the RF algorithm and the combination of RGB imagery and point cloud data derived from a low-cost UAV system at the consumer-grade level can be used to improve the accuracy of AGB estimation and have potential in the practical applications in the rapid estimation of other growth parameters.

20.
Sensors (Basel) ; 19(4)2019 Feb 17.
Artigo em Inglês | MEDLINE | ID: mdl-30781552

RESUMO

Unmanned aerial vehicles (UAVs) equipped with dual-band crop-growth sensors can achieve high-throughput acquisition of crop-growth information. However, the downwash airflow field of the UAV disturbs the crop canopy during sensor measurements. To resolve this issue, we used computational fluid dynamics (CFD), numerical simulation, and three-dimensional airflow field testers to study the UAV-borne multispectral-sensor method for monitoring crop growth. The results show that when the flying height of the UAV is 1 m from the crop canopy, the generated airflow field on the surface of the crop canopy is elliptical, with a long semiaxis length of about 0.45 m and a short semiaxis of about 0.4 m. The flow-field distribution results, combined with the sensor's field of view, indicated that the support length of the UAV-borne multispectral sensor should be 0.6 m. Wheat test results showed that the ratio vegetation index (RVI) output of the UAV-borne spectral sensor had a linear fit coefficient of determination (R²) of 0.81, and a root mean square error (RMSE) of 0.38 compared with the ASD Fieldspec2 spectrometer. Our method improves the accuracy and stability of measurement results of the UAV-borne dual-band crop-growth sensor. Rice test results showed that the RVI value measured by the UAV-borne multispectral sensor had good linearity with leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW); R² was 0.62, 0.76, and 0.60, and RMSE was 2.28, 1.03, and 10.73, respectively. Our monitoring method could be well-applied to UAV-borne dual-band crop growth sensors.


Assuntos
Técnicas Biossensoriais , Produtos Agrícolas/fisiologia , Monitorização Fisiológica/métodos , Tecnologia de Sensoriamento Remoto/métodos , Nitrogênio/metabolismo , Oryza/crescimento & desenvolvimento , Triticum/crescimento & desenvolvimento
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