scholarly journals COMPARISON OF UNCALIBRATED RGBVI WITH SPECTROMETER-BASED NDVI DERIVED FROM UAV SENSING SYSTEMS ON FIELD SCALE

2016 ◽  
Vol XLI-B8 ◽  
pp. 837-843 ◽  
Author(s):  
G. Bareth ◽  
A. Bolten ◽  
M. L. Gnyp ◽  
S. Reusch ◽  
J. Jasper
Keyword(s):  
Winter Wheat ◽  
Data Acquisition ◽  
Precision Agriculture ◽  
Growth Parameters ◽  
Crop Protection ◽  
Crop Management ◽  
Hyperspectral Data ◽  
Field Scale ◽  
Sensing Systems ◽  
Low Weight

The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers’ field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.

scholarly journals COMPARISON OF UNCALIBRATED RGBVI WITH SPECTROMETER-BASED NDVI DERIVED FROM UAV SENSING SYSTEMS ON FIELD SCALE

2016 ◽  
Vol XLI-B8 ◽  
pp. 837-843 ◽  
Author(s):  
G. Bareth ◽  
A. Bolten ◽  
M. L. Gnyp ◽  
S. Reusch ◽  
J. Jasper
Keyword(s):  
Winter Wheat ◽  
Data Acquisition ◽  
Precision Agriculture ◽  
Growth Parameters ◽  
Crop Protection ◽  
Crop Management ◽  
Hyperspectral Data ◽  
Field Scale ◽  
Sensing Systems ◽  
Low Weight

The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers’ field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.

scholarly journals Are intensification and winter wheat yield increase efficient?

2017 ◽  
Vol 63 (No. 9) ◽  
pp. 428-434
Author(s):  
Křen Jan ◽  
Houšť Martin ◽  
Tvarůžek Ludvík ◽  
Jergl Zdeněk
Keyword(s):  
Winter Wheat ◽  
Management Practices ◽  
Management Practice ◽  
Crop Protection ◽  
Wheat Yield ◽  
International Comparisons ◽  
Crop Management ◽  
Optimal Level ◽  
Gross Margin ◽  
Production Factors

The results of small-plot field trials of international comparisons of a series of crop management practices for winter wheat grown during 2014–2016 on fertile soils of Central Moravia were assessed. The objective of the experiments was to obtain the highest gross margin (GM), which is the difference between revenues and direct costs. The analyses showed that an optimal level of inputs and costs for obtaining the highest GM could exist. In the assessed series of crop management practices, the optimum input costs corresponded to 11 000–12 000 CZK/ha and 6–9 input measures. At high levels of grains (above 10 t/ha), higher values of GM were obtained by increased efficiency of inputs, but not by increasing their amount to maximize the yields. This indicates the multifunctional and synergic effects of production factors, which can be used at the so-called ecological intensification. Optimizations of inputs can be obtained rather by crop protection than by crop nutrition, which means rather in protection of high yields than in their maximization. Under field conditions, soil and plant processes affected by weather cannot be controlled. Therefore, optimisation of production factors is based both on scientific findings and practical agronomic experience. That is why a universal crop management practice with increased economic and ecological effects cannot be practically proposed.

Assessment of PRISMA imaging spectrometer data for the estimation of topsoil properties of agronomic interest at the field scale

2020 ◽  
Author(s):  
Raffaele Casa ◽  
Stefano Pignatti ◽  
Simone Pascucci ◽  
Victoria Ionca ◽  
Nada Mzid ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Precision Agriculture ◽  
Central Italy ◽  
Bare Soil ◽  
Hyperspectral Data ◽  
Least Squares Regression ◽  
Field Scale ◽  
Sampling Campaign ◽  
Spectrometer Data ◽  
Sentinel 2

&lt;p&gt;On the 22 March 2019 the Italian Space Agency (ASI) launched the PRISMA satellite, having onboard a hyperspectral imager covering the 400-2500 nm range with 234 spectral bands and about 10 nm of bandwidth. The ground spatial resolution is 30 m, plus a panchromatic camera with 5 m spatial resolution. One of the potential application areas of this scientific mission is for precision agriculture applications, among which the mapping of field-scale variability of topsoil properties is of particular interest.&lt;/p&gt;&lt;p&gt;PRISMA clear-sky hyperspectral images were acquired in autumn and spring 2019 on the Maccarese farm in Central Italy, in the framework of the PRISCAV project, which is aimed at a first assessment of the PRISMA data. An intensive soil sampling campaign was performed, using a ground sampling scheme adapted to PRISMA and Sentinel-2 spatial resolutions, in the fields where bare soil was exposed at the satellite acquisition dates. Soil texture (clay, silt, sand), carbonates, pH and soil organic carbon (SOC) for the collected soil samples were then determined in the laboratory.&lt;/p&gt;&lt;p&gt;The dataset was then used to test calibration and validation of PLSR (Partial Least Squares Regression) and RF (Random Forest) models developed using PRISMA surface reflectance data. To this aim, several pre-treatment tests were performed, including pan-sharpening at 5 m using PRISMA panchromatic data as well as Sentinel-2 multispectral data.&lt;/p&gt;&lt;p&gt;The results show that the good results could be obtained in particular for clay estimation. The best-performing algorithm for topsoil properties retrieval using PRISMA hyperspectral data was RF algorithm as compared with PLSR.&lt;/p&gt;

scholarly journals Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

2021 ◽  
Vol 13 (15) ◽  
pp. 3024
Author(s):  
Huiqin Ma ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Linyi Liu ◽  
Anting Guo
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Hyperspectral Imagery ◽  
Spectral Feature ◽  
Support Vector ◽  
Detection Accuracy ◽  
Feature Combination ◽  
Field Scale ◽  
Head Blight ◽  
Detection Model

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.

scholarly journals Feed Composition Differences Resulting from Organic and Conventional Farming Practices Affect Physiological Parameters in Wistar Rats—Results from a Factorial, Two-Generation Dietary Intervention Trial

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 377
Author(s):  
Marcin Barański ◽  
Dominika Średnicka-Tober ◽  
Leonidas Rempelos ◽  
Gultakin Hasanaliyeva ◽  
Joanna Gromadzka-Ostrowska ◽  
...  
Keyword(s):  
Crop Production ◽  
Wistar Rats ◽  
Dietary Intervention ◽  
Growth Parameters ◽  
Crop Protection ◽  
The Body ◽  
Interactive Effects ◽  
Supporting Evidence ◽  
Feed Composition ◽  
Npk Fertilizer

Recent human cohort studies reported positive associations between organic food consumption and a lower incidence of obesity, cancer, and several other diseases. However, there are very few animal and human dietary intervention studies that provide supporting evidence or a mechanistic understanding of these associations. Here we report results from a two-generation, dietary intervention study with male Wistar rats to identify the effects of feeds made from organic and conventional crops on growth, hormonal, and immune system parameters that are known to affect the risk of a number of chronic, non-communicable diseases in animals and humans. A 2 × 2 factorial design was used to separate the effects of contrasting crop protection methods (use or non-use of synthetic chemical pesticides) and fertilizers (mineral nitrogen, phosphorus and potassium (NPK) fertilizers vs. manure use) applied in conventional and organic crop production. Conventional, pesticide-based crop protection resulted in significantly lower fiber, polyphenol, flavonoid, and lutein, but higher lipid, aldicarb, and diquat concentrations in animal feeds. Conventional, mineral NPK-based fertilization resulted in significantly lower polyphenol, but higher cadmium and protein concentrations in feeds. Feed composition differences resulting from the use of pesticides and/or mineral NPK-fertilizer had a significant effect on feed intake, weight gain, plasma hormone, and immunoglobulin concentrations, and lymphocyte proliferation in both generations of rats and in the second generation also on the body weight at weaning. Results suggest that relatively small changes in dietary intakes of (a) protein, lipids, and fiber, (b) toxic and/or endocrine-disrupting pesticides and metals, and (c) polyphenols and other antioxidants (resulting from pesticide and/or mineral NPK-fertilizer use) had complex and often interactive effects on endocrine, immune systems and growth parameters in rats. However, the physiological responses to contrasting feed composition/intake profiles differed substantially between the first and second generations of rats. This may indicate epigenetic programming and/or the generation of “adaptive” phenotypes and should be investigated further.

scholarly journals Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Shanjun Luo ◽  
Yingbin He ◽  
Qian Li ◽  
Weihua Jiao ◽  
Yaqiu Zhu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Growth Stage ◽  
Estimation Error ◽  
Potato Yield ◽  
Hyperspectral Data ◽  
Single Stage ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Area Index ◽  
Red Edge

Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

The soybean yield gap in Brazil – magnitude, causes and possible solutions for sustainable production

2015 ◽  
Vol 153 (8) ◽  
pp. 1394-1411 ◽  
Author(s):  
P. C. SENTELHAS ◽  
R. BATTISTI ◽  
G. M. S. CÂMARA ◽  
J. R. B. FARIAS ◽  
A. C. HAMPF ◽  
...  
Keyword(s):  
Water Deficit ◽  
Precision Agriculture ◽  
Crop Management ◽  
Sustainable Production ◽  
Biodiesel Production ◽  
Yield Gap ◽  
Soybean Yield ◽  
South Central ◽  
Crop Simulation ◽  
Yield Gaps

SUMMARYBrazil is one of the most important soybean producers in the world. Soybean is a very important crop for the country as it is used for several purposes, from food to biodiesel production. The levels of soybean yield in the different growing regions of the country vary substantially, which results in yield gaps of considerable magnitude. The present study aimed to investigate the soybean yield gaps in Brazil, their magnitude and causes, as well as possible solutions for a more sustainable production. The concepts of yield gaps were reviewed and their values for the soybean crop determined in 15 locations across Brazil. Yield gaps were determined using potential and attainable yields, estimated by a crop simulation model for the main maturity groups of each region, as well as the average actual famers’ yield, obtained from national surveys provided by the Brazilian Government for a period of 32 years (1980–2011). The results showed that the main part of the yield gap was caused by water deficit, followed by sub-optimal crop management. The highest yield gaps caused by water deficit were observed mainly in the south of Brazil, with gaps higher than 1600 kg/ha, whereas the lowest were observed in Tapurah, Jataí, Santana do Araguaia and Uberaba, between 500 and 1050 kg/ha. The yield gaps caused by crop management were mainly concentrated in South-central Brazil. In the soybean locations in the mid-west, north and north-east regions, the yield gap caused by crop management was <500 kg/ha. When evaluating the integrated effects of water deficit and crop management on soybean yield gaps, special attention should be given to Southern Brazil, which has total yield gaps >2000 kg/ha. For reducing the present soybean yield gaps observed in Brazil, several solutions should be adopted by growers, which can be summarized as irrigation, crop rotation and precision agriculture. Improved dissemination of agricultural knowledge and the use of crop simulation models as a tool for improving crop management could further contribute to reduce the Brazilian soybean yield gap.

Digital Display Recognition towards Connected Sensing Systems for Precision Agriculture

2021 ◽  
Author(s):  
Sanket Junagade ◽  
Prachin Jain ◽  
Sanat Sarangi ◽  
Srinivasu Pappula
Keyword(s):  
Precision Agriculture ◽  
Digital Display ◽  
Sensing Systems

scholarly journals Evaluation of yield quality and weed infestation of common valerian (Valeriana officinalis L.) in dependence on weed control method and forecrop.

2012 ◽  
Vol 63 (2) ◽  
pp. 179-188 ◽  
Author(s):  
Cezary Kwiatkowski
Keyword(s):  
Winter Wheat ◽  
Weed Control ◽  
Cover Crop ◽  
Crop Protection ◽  
Field Pea ◽  
Raw Material ◽  
Wheat Field ◽  
Protection Method ◽  
Weed Infestation ◽  
Positive Effect

A field experiment involving the cultivation of common valerian was conducted on loess soil in Abramów (Lublin region) in the period 2007-2009. Qualitative parameters of herbal raw material obtained from this plant as well as in-crop weed infestation were evaluated depending on the protection method and forecrop. Hand-weeded plots, in which a hand hoe was used, were the control. In the other treatments, weeds were controlled using various herbicides and a mechanical implement (brush weeder). Potato and winter wheat + field pea cover crop were the forecrops for common valerian crops. A hypothesis was made that the use of a brush weeder and herbicides not registered for application in valerian crops would have a positive effect on this plant's productivity and weed infestation in its crops. It was also assumed that the introduction of a cover crop would allow the elimination of differences in the forecrop value of the crop stands in question. The best quantitative and qualitative parameters of common valerian raw material as well as the largest reduction of incrop weed infestation were recorded after the application of the herbicides which were not type approved. The use of the brush weeder in the interrows also had a beneficial effect on productivity of the plant in question, but secondary weed infestation at the end of the growing season of common valerian turned out to be its disadvantage. Traditional crop protection methods used in common valerian crops were less effective in weed infestation reduction and they resulted in lower plant productivity and raw material quality. Potato proved to be a better forecrop for common valerian than winter wheat + field pea; however, this positive effect was not confirmed statistically. The following annual weeds: <i>Chenopodium album</i>, <i>Galinsoga parviflora</i>, <i>Stellaria media</i>, were predominant in the common valerian crop. Traditional weed control methods resulted in the dominance of some dicotyledonous weeds, such as <i>Viola arvensis</i>, <i>Galium aparine</i>, <i>Capsella bursa-pastoris</i>.

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