scholarly journals Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1108 ◽  
Author(s):  
Jiayi Zhang ◽  
Xia Liu ◽  
Yan Liang ◽  
Qiang Cao ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Vegetation Index ◽  
Field Experiments ◽  
Growth Parameters ◽  
Leaf Nitrogen ◽  
Crop Growth ◽  
Nitrogen Accumulation ◽  
Wheat Cultivars ◽  
Area Index

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.

scholarly journals Development of an Apparatus for Crop-Growth Monitoring and Diagnosis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3129 ◽  
Author(s):  
Jun Ni ◽  
Jingchao Zhang ◽  
Rusong Wu ◽  
Fangrong Pang ◽  
Yan Zhu
Keyword(s):  
Field Experiments ◽  
Leaf Nitrogen ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Nitrogen Accumulation ◽  
Leaf Nitrogen Content ◽  
Area Index ◽  
Linear Fitting ◽  
Monitoring And Diagnosis ◽  
Mean Square Errors

To non-destructively acquire leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW) data at high speed and low cost, a portable apparatus for crop-growth monitoring and diagnosis (CGMD) was developed according to the spectral monitoring mechanisms of crop growth. According to the canopy characteristics of crops and actual requirements of field operation environments, splitting light beams by using an optical filter and proper structural parameters were determined for the sensors. Meanwhile, an integral-type weak optoelectronic signal processing circuit was designed, which changed the gain of the system and guaranteed the high resolution of the apparatus by automatically adjusting the integration period based on the irradiance received from ambient light. In addition, a coupling processor system for a sensor information and growth model based on the microcontroller chip was developed. Field experiments showed that normalised vegetation index (NDVI) measured separately through the CGMD apparatus and the ASD spectrometer showed a good linear correlation. For measurements of canopy reflectance spectra of rice and wheat, their linear determination coefficients (R2) were 0.95 and 0.92, respectively while the root mean square errors (RMSEs) were 0.02 and 0.03, respectively. NDVI value measured by using the CGMD apparatus and growth indices of rice and wheat exhibited a linear relationship. For the monitoring models for LNC, LNA, LAI, and LDW of rice based on linear fitting of NDVI, R2 were 0.64, 0.67, 0.63 and 0.70, and RMSEs were 0.31, 2.29, 1.15 and 0.05, respectively. In addition, R2 of the models for monitoring LNC, LNA, LAI, and LDW of wheat on the basis of linear fitting of NDVI were 0.82, 0.71, 0.72 and 0.70, and RMSEs were 0.26, 2.30, 1.43, and 0.05, respectively.

Crop Yield and Water Productivity Responses in Management Zones for Variable-Rate Irrigation Based on Available Soil Water Holding Capacity

2017 ◽  
Vol 60 (5) ◽  
pp. 1659-1667 ◽  
Author(s):  
Weixia Zhao ◽  
Jiusheng Li ◽  
Rumiao Yang ◽  
Yanfeng Li
Keyword(s):  
Winter Wheat ◽  
Growth Parameters ◽  
Water Productivity ◽  
Crop Growth ◽  
Variable Rate ◽  
Summer Maize ◽  
Area Index ◽  
Management Zones ◽  
Water Holding ◽  
Variable Rate Irrigation

Abstract. Effective management of variable-rate irrigation (VRI) is a critical factor for maximizing the benefit of a VRI system. In this study, the influences of soil properties on winter wheat and summer maize were studied to verify whether differences in soil available water holding capacity (AWC) had an influence on crop growth parameters, yield, and water productivity (WP). A center-pivot VRI system was employed to deliver irrigation water across the field in an alluvial flood plain in China, and AWC was used to delineate VRI management zones. Three management zones with substantial differences in AWC were created, with AWC varying from 152 to 161 mm, from 161 to 171 mm, and from 171 to 185 mm for zones 1, 2, and 3, respectively. All zones were managed using the same allowed depletion. In the two-year study, the seasonal irrigation amount was basically equivalent among management zones for both winter wheat and summer maize. Differences in crop growth parameters were detected in plant height and leaf area index for winter wheat. The maximum plant height and leaf area index observed in zone 2 were 5 cm and 2.1 greater, respectively, than in the other zones. For both winter wheat and summer maize, the highest yield and WP were observed in zone 2, except for summer maize WP in the 2014 season. Compared with the average value for this field, the yields in zone 2 were 27% and 23% greater for winter wheat and 4% and 11% greater for summer maize in the 2014 and 2015 seasons, respectively. We demonstrate that AWC is an effective parameter for zone identification in VRI management, and differences in AWC and the layered-textural soils in a field may influence the crop growth parameters, yield, and WP of winter wheat and summer maize. Keywords: Center-pivot irrigation, Critical soil moisture deficit, Management zone, Summer maize, Variable-rate irrigation, Winter wheat.

Soft Red Winter Wheat (Triticum aestivum) Response to Dicamba and Dicamba Plus 2,4-D

1989 ◽  
Vol 3 (1) ◽  
pp. 67-71 ◽  
Author(s):  
Jill Schroeder ◽  
Philip A. Banks
Keyword(s):  
Triticum Aestivum ◽  
Winter Wheat ◽  
Grain Yield ◽  
Field Experiments ◽  
Wheat Cultivars ◽  
Soft Red Winter Wheat ◽  
Winter Wheat Cultivars ◽  
Red Winter Wheat

Soft red winter wheat cultivars were evaluated in field experiments in Georgia for tolerance to dicamba alone and mixed with 2,4-D. Treatments reduced ‘Florida 302’ yield more than ‘Florida 301’ or ‘Coker 983’ at Tifton in 1986. Mid-tillering Florida 302 wheat was more sensitive to treatment than fully tillered wheat. In 1987, dicamba plus 2,4-D applied at mid-tillering reduced yields of all cultivars in Watkinsville. Injury and yield reductions occurred primarily when mid-tiller treatments were applied to wheat that was planted 10 or 21 days later than recommended at Tifton or Watkinsville, respectively. When applied according to labeling, dicamba or dicamba plus 2,4-D use in Georgia soft red winter wheat can reduce grain yield.

scholarly journals Effect of Different Spacing and Weed Management Practices on Weeds, Crop Growth and Productivity of Pigeon Pea Variety Pusa Arhar-16

2021 ◽  
Author(s):  
Yadwinder Singh ◽  
Kanwaljit Singh
Keyword(s):  
Weed Management ◽  
Management Practices ◽  
Field Experiments ◽  
Growth Parameters ◽  
Growth Parameter ◽  
Pigeon Pea ◽  
Crop Growth ◽  
Growth And Yield ◽  
Area Index ◽  
Yield Attributes

Background: To study effect of various spacing and weed management practices on weed control, growth parameter, yield and yield attributes of pigeon pea variety Pusa Arhar-16, field experiments were conducted at Student’s Research Farm, Khalsa College Amritsar, Punjab, India during kharif season of 2019-20.Methods: The experiment was laid out in Split plot design with three replications. The treatments comprised of four different spacing i.e. S1 (30×10 cm), S2 (30×15 cm), S3 (40×15 cm) and S4 (50×25 cm) which were considered as main plot treatments and three weed management practices W1 (weedy check), W2 (pendimethalin @ 1.5 kg a.i. ha-1 at 1DAS) and W3 (rice straw mulch) as sub-plot treatments.Result: It was found that maximum plant height (218.2 cm) and highest leaf area index (2.104) was recorded in treatment S1 (30×10 cm) whereas treatment S4 (50×25 cm) was superior in rest of growth parameters viz. maximum crop growth rate (0.85 gram per plant per day), number of primary branches (18.2 per plant) and number of secondary branches (13.8 per plant). Among yield and yield attributes, spacing S1 (30×10 cm) showed maximum grain yield (16.29 q ha-1) and stover yield (49.29 q ha-1) whereas, maximum number of pods per plant (154.6), pod length (4.83 cm), number of seeds per pod (3.90) and 100-seed weight (7.14 grams) were recorded in spacing S4 (50×25 cm). The weed management treatments showed non-significant effect on growth and yield parameters due to weed suppressing ability of the crop. which might be due to weed suppressing ability of the crop. It may be concluded that pigeon pea cultivar Pusa Arhar-16 performed better in narrower spacing of 30×10 cm due to its dwarf nature and it also showed good smothering effect on the weeds due to its smothering effect.

Evaluation of the chlorophyll meter and GreenSeeker for the assessment of rice nitrogen status

2017 ◽  
Vol 8 (2) ◽  
pp. 359-363 ◽  
Author(s):  
Ke Zhang ◽  
Xiaokang Ge ◽  
Xia Liu ◽  
Zeyu Zhang ◽  
Yan Liang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stem Elongation ◽  
Leaf Nitrogen ◽  
Nitrogen Accumulation ◽  
Plant Analysis ◽  
Area Index ◽  
Spad Readings ◽  
N Status

This work was to evaluate the differences of soil and plant analysis development (SPAD) and normalized difference vegetation index (NDVI) readings and their relationship with leaf nitrogen accumulation (LNA). The study explored new indices to diagnose nitrogen (N) status. These indices were obtained by multiplying SPAD readings and leaf area index (LAI). Linear regression relationships between Chlorophyll values and N indicators showed the SPAD readings (Chl: LNA=0.0546×Chl-0.479, R2=0.94***, P<0.001). The projected results suggested that Chl values could play an important role for improving N status diagnosis from stem elongation to heading stages in paddy rice.

Crop growth and relative growth rates of old and modern wheat cultivars

1991 ◽  
Vol 42 (1) ◽  
pp. 13 ◽  
Author(s):  
MM Karimi ◽  
KHM Siddique
Keyword(s):  
Growth Rate ◽  
Grain Yield ◽  
Dry Matter ◽  
Crop Growth ◽  
Wheat Cultivars ◽  
Area Index ◽  
Relative Growth ◽  
Vegetative Phase ◽  
Historical Series ◽  
Modern Wheat

An analysis of the dynamics of green area index (GAI), dry matter (DM), relative growth rate (RGR) and crop growth rate (CGR) based on growing degree days (GDD) is presented for a historical series of wheats commercially released in Western Australia. Relative to the old cultivars, modern wheats were characterized by a greater RGR during the vegetative phase. This was achieved at a lower initial GAI, which persisted as the season progressed and was associated with a higher CGR at anthesis and greater grain yield at the end of the season. In the old cultivars, a greater GAI during the mid season declined rapidly as temperatures and soil moisture stress increased in spring, resulting in a lower GAI at anthesis. Together with lower CGR at anthesis this resulted in less dry matter and grain yield at final harvest. The higher grain yield of modern wheat cultivars was achieved with a high RGR during the vegetative phase and greater CGR from ear emergence to harvest.

Competition Between Winter Wheat (Triticum aestivum) Cultivars and Downy Brome (Bromus tectorum)

Weed Science ◽  
1986 ◽  
Vol 34 (5) ◽  
pp. 689-693 ◽  
Author(s):  
Challaiah ◽  
Orvin C. Burnside ◽  
Gail A. Wicks ◽  
Virgil A. Johnson
Keyword(s):  
Triticum Aestivum ◽  
Winter Wheat ◽  
Grain Yield ◽  
Stepwise Regression ◽  
Field Experiments ◽  
Bromus Tectorum ◽  
Growth Parameters ◽  
Yield Reduction ◽  
Downy Brome ◽  
Wheat Grain

Field experiments were conducted to select winter wheat (Triticum aestivumL.) cultivar(s) that were competitive to downy brome (Bromus tectorumL. # BROTE). Downy brome significantly reduced winter wheat grain yields of all cultivars by 9 to 21% at Lincoln, while at North Platte yield reduction ranged from 20 to 41% depending upon cultivar. ‘Turkey’ was the most competitive cultivar to downy brome but it had the lowest grain yield. Compared to ‘Centurk 78’, ‘Centura’ at Lincoln and ‘SD 75284’ at North Platte proved to be significantly higher yielding and more competitive to downy brome. Winter wheat tiller number, canopy diameter, and plant height were negatively correlated with downy brome yield, but changes in these growth parameters did not always translate into grain yield advantage in downy brome-infested plots. Based on stepwise regression analysis, wheat height was better correlated with reduction in downy brome yield than were canopy diameter or number of tillers.

Application of drone borne LiDAR technology for monitoring agricultural biomass and plant growth.

2020 ◽  
Author(s):  
Katerina Trepekli ◽  
Andreas Westergaard-Nielsen ◽  
Thomas Friborg
Keyword(s):  
Point Cloud ◽  
Field Experiments ◽  
Growth Parameters ◽  
Growth Period ◽  
Crop Growth ◽  
Yield Potential ◽  
Geometrical Parameters ◽  
Agricultural Crop ◽  
Area Index ◽  
Vegetation Growth

&lt;p&gt;With rising demand for increasing the yield potential of agricultural products and for reducing greenhouse gas emissions during food production, strengthening our scientific and technological capacity to monitor crop growth and above ground biomass (AGB) is indispensable to move towards more sustainable management of our agricultural resources. Pivotal to meet this goal is the application of high-throughput field-phenotyping tools such as drone borne Light Detection and Ranging (Lidar) systems for accurate, fine-grained, rapid and labor-saving measurements of vegetation growth parameters. Our objective is to develop and assess a workflow to estimate AGB, leaf area index (LAI), plant height (PH) and volume of a homogeneous and highly dense agricultural field using the capabilities of UAV-Lidar technology. The experimental site is located in Denmark and populated by potato plants. Aerial campaigns and field experiments, including destructive biomass sampling and measurements of LAI and plants&amp;#8217; geometrical characteristics at 1m&lt;sup&gt;2&lt;/sup&gt; square plots, were performed once per month during the vegetation growth period (May&amp;#8211;September 2019). The high resolution (3.6 cm) Canopy Height model (CHM) is generated by first evaluating the performance of different filtering algorithms that separate the ground points from the Lidar-derived point cloud datasets. To extract the geometrical parameters of individual crop plants, we delineate the CHM by applying segmentation directly to the Lidar point cloud rather than segmenting the CHM as an interpolated raster surface. The PH obtained by the Lidar scanner is highly correlated with the field-measured PH (R&amp;#178;=0.89 and RMSE=0.028 m) implying that the point cloud data processing evaluated here is efficient and able to generate serviceably accurate CHMs for agricultural sites with similar vegetation structures. Throughout the observed vegetation growth period, the AGB can be quantified with high accuracy when it is considered to be a function of plant volume (R&amp;#178;=0.81 and RMSE=31.65 %) rather than a function of PH, as the latter approximating an exponential relationship with AGB. Height and density Lidar metrics were more effective in predicting in situ LAI measurements in comparison with remotely sensed LAI calculated directly from Lidar vegetation points following the Beer Lambert law. The predictive frameworks emerging from this approach indicate the applicability of drone borne Lidar systems for obtaining agricultural crop growth parameters in both high spatial and temporal resolution.&lt;/p&gt;

Competitive Ability of Winter Wheat Cultivars with Wild Oat (Avena ludoviciana)

Weed Science ◽  
1991 ◽  
Vol 39 (2) ◽  
pp. 154-158 ◽  
Author(s):  
R. S. Balyan ◽  
R. K. Malik ◽  
R. S. Panwar ◽  
S. Singh
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Dry Matter ◽  
Competitive Ability ◽  
Field Experiments ◽  
Unit Area ◽  
Dry Matter Accumulation ◽  
Wild Oat ◽  
Wheat Cultivars ◽  
Winter Wheat Cultivars

Field experiments were conducted during the winters of 1986–87 and 1987–88 at Haryana Agricultural University, Hisar, India to classify the ability of winter wheat cultivars to compete with wild oat. Wild oat reduced winter wheat grain yield by 17 to 62% depending upon cultivar. WH-147 and HD-2285 were the most competitive cultivars. Winter wheat dry matter accumulation and grain yield were negatively correlated with wild oat dry matter. A high number of tillers, particularly in HD-2009, WH-291, and S-308, did not always translate into grain yield advantage in wild oat-infested plots. Wheat height and dry matter accumulation per unit area during early crop growth were better characters than number of tillers for predicting the competitive ability of wheat cultivars to wild oat.

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