scholarly journals Nitrogen Estimation for Wheat Using UAV-Based and Satellite Multispectral Imagery, Topographic Metrics, Leaf Area Index, Plant Height, Soil Moisture, and Machine Learning Methods

Nitrogen ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 1-25
Author(s):  
Jody Yu ◽  
Jinfei Wang ◽  
Brigitte Leblon ◽  
Yang Song
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Height ◽  
Satellite Imagery ◽  
N Fertilizer ◽  
Support Vector ◽  
Multispectral Imagery ◽  
Area Index ◽  
Canopy Nitrogen

To improve productivity, reduce production costs, and minimize the environmental impacts of agriculture, the advancement of nitrogen (N) fertilizer management methods is needed. The objective of this study is to compare the use of Unmanned Aerial Vehicle (UAV) multispectral imagery and PlanetScope satellite imagery, together with plant height, leaf area index (LAI), soil moisture, and field topographic metrics to predict the canopy nitrogen weight (g/m2) of wheat fields in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models, applied to either UAV imagery or satellite imagery, were evaluated for canopy nitrogen weight prediction. The top-performing UAV imagery-based validation model used SVR with seven selected variables (plant height, LAI, four VIs, and the NIR band) with an R2 of 0.80 and an RMSE of 2.62 g/m2. The best satellite imagery-based validation model was RF, which used 17 variables including plant height, LAI, the four PlanetScope bands, and 11 VIs, resulting in an R2 of 0.92 and an RMSE of 1.75 g/m2. The model information can be used to improve field nitrogen predictions for the effective management of N fertilizer.

scholarly journals Genetic Variability, Correlation and Path Coefficient Studies in Elephant Foot Yam (Amorphophallus campanulatus Bl.)

2013 ◽  
Vol 5 (2) ◽  
pp. 371-381 ◽  
Author(s):  
K. K. Paul ◽  
M. A. B. Miah
Keyword(s):  
Genetic Variability ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Height ◽  
Correlation Study ◽  
Path Coefficient ◽  
Area Index ◽  
Depth Study ◽  
Elephant Foot Yam ◽  
Positive Direct

An investigation has been made to characterize the local accessions of Elephant foot yam collected from thirteen aroid growing districts and in-depth study on genetic variability, correlation and path coefficient for plant height, petiole length, petiole breadth, leaf area index, corm length, corm breadth, corm weight, cormel number, cormel length, cormel breadth, cormel weight and yield per plant has also been carried out. Genotypic variances and coefficient of variation for most of the characters were remarkably higher than their corresponding environmental variances, which also indicate the existence of variation in genotypic origin. High heritability with high genetic advance in percentage of mean was also observed for all characters. In the correlation study plant height, leaf area index, corm length, corm breadth, corm weight, cormel number, cormel length, cormel breath showed positive correlation with yield per plant in genotypic and phenotypic level. Leaf area index, cormel number in phenotypically and cormel number in genotypic level showed relatively high positive direct effect on yield per plant.Keywords: Amorphophallus; Genetic variability; Correlation; Path coefficient.© 2013 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi: http://dx.doi.org/10.3329/jsr.v5i2.13853        J. Sci. Res. 5 (2), 371-381 (2013)

Spring top-dressings of ‘Nitro-Chalk’ and late sprays of a liquid N-fertilizer and a broad spectrum fungicide for consecutive crops of winter wheat at Saxmundham, Suffolk

1978 ◽  
Vol 90 (3) ◽  
pp. 509-516 ◽  
Author(s):  
A. Penny ◽  
F. V. Widdowson ◽  
J. F. Jenkyn
Keyword(s):  
Calcium Carbonate ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Ammonium Nitrate ◽  
Leaf Area ◽  
Broad Spectrum ◽  
N Fertilizer ◽  
N Content ◽  
Area Index ◽  
Liquid Fertilizer

SummaryAn experiment at Saxmundham, Suffolk, during 1974–6, tested late sprays of a liquid N-fertilizer (ammonium nitrate/urea) supplying 50 kg N/ha, and a broad spectrum fungicide (benomyl and maneb with mancozeb) on winter wheat given, 0, 50, 100 or 150 kg N/ha as ‘Nitro-Chalk’ (ammonium nitrate/calcium carbonate) in springMildew (Erysiphe graminisf. sp. tritici) was most severe in 1974. It was increased by N and decreased by the fungicide in both 1974 and 1975, but was negligible in 1976. Septoria (S. nodorum) was very slight in 1974 and none was observed in 1976. It was much more severe in 1975, but was unaffected by the fungicide perhaps because this was applied too late.Yield and N content, number of ears and leaf area index were determined during summer on samples taken from all plots given 100 or 150 kg N/ha in spring; each was larger with 150 than with 100 kg N/ha. The effects of the liquid N-fertilizer on yield and N content varied, but leaf area index was consistently increased. None was affected consistently by the fungicide.Yields, percentages of N in, and amounts of N removed by grain and straw were greatly and consistently increased by each increment of ‘Nitro-Chalk’. Yields of grain were increased (average, 9%) by the liquid fertilizer in 1974 and 1975, and most where most ‘Nitro-Chalk’ had been given, but not in 1976 when the wheat ripened in July; however, both the percentage of N in and the amount of N removed by the grain were increased by the liquid fertilizer each year. The fungicide increased the response to the liquid N-fertilizer in 1974, but not in 1975 when Septoria was not controlled, nor in 1976 when leaf diseases were virtually absent.The weight of 1000 grains was increased by each increment of ‘Nitro-Chalk’ in 1975 but only by the first one (50 kg N/ha) in 1974 and 1976; it was very slightly increased by the liquid fertilizer and by fungicide each year.

scholarly journals Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing

2021 ◽  
Vol 13 (16) ◽  
pp. 3263
Author(s):  
Zhijie Liu ◽  
Pengju Guo ◽  
Heng Liu ◽  
Pan Fan ◽  
Pengzong Zeng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Shaanxi Province ◽  
Gradient Boosting ◽  
Support Vector ◽  
Apple Orchards ◽  
Apple Trees ◽  
Area Index ◽  
Spatial Efficiency

The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees.

scholarly journals Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

scholarly journals Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

2020 ◽  
Vol 12 (13) ◽  
pp. 2148 ◽  
Author(s):  
Adnan Rajib ◽  
I Luk Kim ◽  
Heather E. Golden ◽  
Charles R. Lane ◽  
Sujay V. Kumar ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Dynamics ◽  
Watershed Modeling ◽  
Remotely Sensed ◽  
Area Index ◽  
Daily Streamflow ◽  
Modis Lai

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

scholarly journals Evaluations of In-Furrow Insecticides and Fungicides, 1995

1996 ◽  
Vol 21 (1) ◽  
pp. 241-241
Author(s):  
Gene Burris ◽  
Don Cook ◽  
B. R. Leonard ◽  
J. B. Graves ◽  
J. Pankey
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Height ◽  
Pest Control ◽  
Cotton Seed ◽  
Stand Density ◽  
Research Station ◽  
Area Index ◽  
Seed Rate

Abstract The test was conducted at the Northeast Research Station in St. Joseph, LA. Plots were replicated 4 times in a RCB design and were four rows (40-inch spacing) X 65 ft. ‘Stoneville LA 887’ cotton seed was planted 2 and 3 May on a commerce silt soil which was fertilized sidedress with 90 lb N/acre. Cotton seed were planted with a John Deere model 7100 series planter which was equipped with 10 inch seed cones mounted to replace the seed hoppers. The seed rate was 4 seed/row ft. Granular in-furrow treatments were applied with 8 inch belt cone applicators mounted to replace the standard granular applicators. Control of thrips and aphids was evaluated on 5 randomly selected plants/plot. Evaluations were made on 18, 19, 24, 26, and 29 May and 8 Jun. Plant height counts were taken on 10 randomly selected plants/plot on 8 Jun. Stand density and leaf area was determined by counting the number of plants in a randomly selected meter on 29 May. Leaf area was recorded using a Li Cor leaf area machine. The data was recorded as cm2 and converted to a leaf area index (LAI). Major pests and/or secondary pest control was initiated in Jun and continued on an “as needed” basis through Aug.

scholarly journals Influence of Spiral Nematodes (Scutellonema spp.) on Maize Performance and Growth under Natural Field Infestation in Mwea, Kenya

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Samuel Maina ◽  
Rossa Nyoike Ng’endo
Keyword(s):  
Food Security ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Height ◽  
Management Practices ◽  
Block Design ◽  
Negative Relationship ◽  
Stress Factors ◽  
Area Index ◽  
Number Of Leaves

Maize (Zea mays L.) is a significant food security crop in Kenya and it serves as the main source of nutrition and calories among the small-holder farmers. The overall maize yields per hectare have been fluctuating in the past few years posing a great risk to food security. Among the stress factors associated with maize yield loss include plant-feeding nematodes. In this regard, this study was conducted to evaluate the impacts of plant-parasitic nematodes specifically Scutellonema spp. under field conditions on maize performance in Mwea, Kenya. The field trials were laid out in a randomized complete block design with each treatment comprising of four replicates. The treatments included maize plots without nematicide (MPWN) and control plots treated with nematicide. The experiments were conducted in two trials. Soil samples were taken at a 0–20 cm depth at monthly intervals during 2018–2019. During the two trials, MPWN recorded significantly lower plant height and number of leaves per plant. Correlation analysis revealed a significant negative relationship between Scutellonema abundance with leaf area index, plant height, and number of functional leaves in MPWN during the 2019 trial. This implies that high population of Scutellonema perhaps has the potential to affect leaf area index, plant height, number of leaves per plant, which are aspects that in turn influence maize productivity. Therefore, holistic sustainable management practices to control Scutellonema spp. in maize fields such as use of organic amendments, resistant maize cultivars, and antagonistic organisms are crucial in order to alleviate negative impacts linked to Scutellonema infestation.

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