scholarly journals High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR

2018 ◽  
Vol 9 ◽  
Author(s):  
Jose A. Jimenez-Berni ◽  
David M. Deery ◽  
Pablo Rozas-Larraondo ◽  
Anthony (Tony) G. Condon ◽  
Greg J. Rebetzke ◽  
...  
Keyword(s):  
Plant Height ◽  
High Throughput ◽  
Ground Cover ◽  
Above Ground Biomass ◽  
Ground Biomass

scholarly journals Correlating the Plant Height of Wheat with Above-Ground Biomass and Crop Yield Using Drone Imagery and Crop Surface Model, A Case Study from Nepal

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 28 ◽  
Author(s):  
Uma Shankar Panday ◽  
Nawaraj Shrestha ◽  
Shashish Maharjan ◽  
Arun Kumar Pratihast ◽  
Shahnawaz ◽  
...  
Keyword(s):  
Food Security ◽  
Plant Height ◽  
Crop Yield ◽  
Earth Observation ◽  
Wheat Crop ◽  
Surface Model ◽  
Observation Data ◽  
Land Resources ◽  
Above Ground Biomass ◽  
Ground Biomass

Food security is one of the burning issues in the 21st century, as a tremendous population growth over recent decades has increased demand for food production systems. However, agricultural production is constrained by the limited availability of arable land resources, whereas a significant part of these is already degraded due to overexploitation. In order to get optimum output from the available land resources, it is of prime importance that crops are monitored, analyzed, and mapped at various stages of growth so that the areas having underdeveloped/unhealthy plants can be treated appropriately as and when required. This type of monitoring can be performed using ultra-high-resolution earth observation data like the images captured through unmanned aerial vehicles (UAVs)/drones. The objective of this research is to estimate and analyze the above-ground biomass (AGB) of the wheat crop using a consumer-grade red-green-blue (RGB) camera mounted on a drone. AGB and yield of wheat were estimated from linear regression models involving plant height obtained from crop surface models (CSMs) derived from the images captured by the drone-mounted camera. This study estimated plant height in an integrated setting of UAV-derived images with a Mid-Western Terai topographic setting (67 to 300 m amsl) of Nepal. Plant height estimated from the drone images had an error of 5% to 11.9% with respect to direct field measurement. While R2 of 0.66 was found for AGB, that of 0.73 and 0.70 were found for spike and grain weights respectively. This statistical quality assurance contributes to crop yield estimation, and hence to develop efficient food security strategies using earth observation and geo-information.

scholarly journals Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

2019 ◽  
Vol 11 (11) ◽  
pp. 1261 ◽  
Author(s):  
Yaxiao Niu ◽  
Liyuan Zhang ◽  
Huihui Zhang ◽  
Wenting Han ◽  
Xingshuo Peng
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Plant Height ◽  
Linear Regression Model ◽  
Vegetation Indices ◽  
Point Clouds ◽  
Above Ground Biomass ◽  
Sensing System ◽  
Ground Biomass ◽  
Application Potential

The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

Imidazolinone-Resistant Sunflower Tolerance to Imazapic

2009 ◽  
Vol 23 (1) ◽  
pp. 188-190
Author(s):  
Eric P. Prostko ◽  
Timothy L. Grey ◽  
Jerry W. Davis
Keyword(s):  
Plant Height ◽  
Production Systems ◽  
Field Trials ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Crop Injury ◽  
Seed Head ◽  
Herbicide Treatment ◽  
Biomass Plant ◽  
Sunflower Production

Field trials were conducted in Georgia in 2007 to 2008 to evaluate the tolerance of three imidazolinone-resistant sunflower cultivars to POST applications of imazapic. There was no interaction between sunflower cultivar and herbicide treatment. When averaged over sunflower cultivars, imazapic, at 70 and 140 g ai/ha and applied at 30 d after planting, had no effect on sunflower above-ground biomass, plant height, seed-heads per meter row, and seed-head weights. Sunflower response to imazapic was similar to that of imazamox. Imazapic could be used in imidazolinone-resistant sunflower production systems without risk of unacceptable crop injury.

scholarly journals Effects of Nitrogen and NPS Fertilizer Rates on Fresh Yield of Garlic (Allium sativum L.) at Debre Berhan, Ethiopia

2020 ◽  
pp. 113-118
Author(s):  
Getachew Amare ◽  
Temesgen Mamo
Keyword(s):  
Plant Height ◽  
Growth Parameters ◽  
Block Design ◽  
Leaf Length ◽  
Above Ground Biomass ◽  
Growth Physiology ◽  
Ground Biomass ◽  
Debre Berhan ◽  
Significant Difference ◽  
Fertilizer Rates

Keeping in view of lack of recommended rates of N and NPS fertilizers, a field experiment was conducted to evaluate the effect of the newly introduced NPS fertilizer and nitrogen on growth, physiology and above ground biomass of garlic. Four NPS (0-0-0, 78.75-69-12.75, 105-92-17 and 131.25-115-21.25 kg N-P-S ha-1) and three nitrogen fertilizer rates (114.13, 228.26 and 278.33 kg N ha-1) were laid out in Randomized Complete Block Design with three replications. Significantly highest plant height (28.02 cm), leaf diameter (1.27 cm), dry and fresh weight (4.71 g and 6.11 g) and leaf length were recorded on garlic plants supplied with 105-92-17 kg N-P-S ha-1 and also the highest plant height (27.75 cm), leaf length (24.02 cm), fresh and dry weight (6.23 g and 5.04 g) were recorded on garlic plants supplied with 278.33 kg N ha-1. The interaction effect also show a significant effect in almost all the growth parameters; the early day to 50% emergence was recorded from a plot which received 228.26 kg N ha-1 and 105-92-17 kg NPS ha-1 and the highest plant height, leaf length, fresh and dry above ground biomass and leaf diameter were 29.62 cm, 25.60 cm, 6.93 g, 5.59 g and 1.4 cm, respectively were observed by the interaction of 278.33 kg N ha-1 and 105-92-17 kg N-P-S ha-1 with no significant difference with 228.26 N and 78.75-69-12.75 kg N-P-S ha−1. From this one season experiment, fertilizer rates 307.01-69-12.75 kg N-P-S ha−1 could be recommended for garlic production.

An Approach for Indirect Determination of Leaf Area Index

2019 ◽  
Vol 62 (3) ◽  
pp. 655-659 ◽  
Author(s):  
Sally D. Logsdon ◽  
Cindy A. Cambardella
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Height ◽  
Canopy Cover ◽  
Ground Cover ◽  
Plant Canopy ◽  
Area Index ◽  
Indirect Determination ◽  
Crop Models

Abstract. Leaf area index (LAI) is important in crop models, but determination of LAI can be challenging for crops and mixtures of crops. The purpose of this study was to determine LAI indirectly from ground cover (determined from photographic images) and canopy height using defined relationships based on crop coefficients. Images of plant canopies were obtained to estimate the green cover fraction. An equation was derived to determine LAI from ground cover and plant height, and the technique was tested on different crops. For three of the measurement dates, the data were compared to LAI measurements obtained with a LAI-2200 plant canopy analyzer. The trend was the same for the two LAI procedures, but there was some scatter. The photographic technique was adequate for LAI determination. Keywords: Ground cover, Plant canopy cover, Plant height.

scholarly journals UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax

2020 ◽  
Vol 12 (20) ◽  
pp. 3464
Author(s):  
Mauro Maesano ◽  
Sacha Khoury ◽  
Farid Nakhle ◽  
Andrea Firrincieli ◽  
Alan Gay ◽  
...  
Keyword(s):  
Drought Stress ◽  
Plant Height ◽  
Fossil Fuels ◽  
Arundo Donax ◽  
Bioenergy Crops ◽  
Stepwise Multiple Regression ◽  
Surface Model ◽  
Above Ground Biomass ◽  
Crop Height ◽  
Ground Biomass

Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining.

Logistic model analysis of winter wheat growth on China's Loess Plateau

2014 ◽  
Vol 94 (8) ◽  
pp. 1471-1479 ◽  
Author(s):  
Wang Xiangxiang ◽  
Wang Quanjiu ◽  
Fan Jun ◽  
Su Lijun ◽  
Shen Xinlei
Keyword(s):  
Winter Wheat ◽  
Plant Height ◽  
Loess Plateau ◽  
Logistic Model ◽  
Model Analysis ◽  
Above Ground Biomass ◽  
Wheat Growth ◽  
Area Index ◽  
Ground Biomass ◽  
Irrigation Treatments

Xiangxiang, W., Quanjiu, W., Jun, F., Lijun, S. and Xinlei, S. 2014. Logistic model analysis of winter wheat growth on China's Loess Plateau. Can. J. Plant Sci. 94: 1471–1479. The leaf area index (LAI) and above-ground biomass are closely related to crop growth status and yields. Therefore, analysis of their variation and development of a mathematical model for their prediction can provide a theoretical basis for further research. This paper presents a new equation for logistic pattern that calculates above-ground biomass and LAI for different irrigation treatments independent of growing degree days (GDD) and plant height. The model root mean square of error (RMSE) for the LAI was from 0.25 to 1.36, and for above-ground biomass it was from 0.49 to 1.34. The r2 values for the model's output under the single irrigation, double irrigation, triple irrigation, and quadruple irrigation treatments were 0.98, 0.87, 0.96, 0.98 and 0.99, respectively. For above-ground biomass they were 0.96, 0.97, 0.99, 0.97, and 0.97, respectively. The relative error for LAI ranged from 0.026 to 15.2%. For above-ground biomass, the Re ranged from 5.78 to 8.79%. The results gave good agreement between the estimated values and the measured values. The Logistic model was good at estimating the LAI and the above-ground biomass from the plant height.

ESTIMATION OF ABOVE GROUND BIOMASS OF THE THREE SITES (GRAZED SITE, PROTECTED SITE AND SEED SOWN SITE) FOR COMPARING THEIR PRODUCTIVITY IN KASHMIR VALLEY

2017 ◽  
Vol 23 (2) ◽  
Author(s):  
AFSHAN ANJUM BABA ◽  
SYED NASEEM UL-ZAFAR GEELANI ◽  
ISHRAT SALEEM ◽  
MOHIT HUSAIN ◽  
PERVEZ AHMAD KHAN ◽  
...  
Keyword(s):  
Protected Areas ◽  
Aboveground Biomass ◽  
Plant Biomass ◽  
Summer Season ◽  
Above Ground Biomass ◽  
Spring Season ◽  
Kashmir Valley ◽  
Ground Biomass ◽  
Maximum Value ◽  
Protected Site

The plant biomass for protected areas was maximum in summer (1221.56 g/m2) and minimum in winter (290.62 g/m2) as against grazed areas having maximum value 590.81 g/m2 in autumn and minimum 183.75 g/m2 in winter. Study revealed that at Protected site (Kanidajan) the above ground biomass ranged was from a minimum (1.11 t ha-1) in the spring season to a maximum (4.58 t ha-1) in the summer season while at Grazed site (Yousmarag), the aboveground biomass varied from a minimum (0.54 t ha-1) in the spring season to a maximum of 1.48 t ha-1 in summer seasonandat Seed sown site (Badipora), the lowest value of aboveground biomass obtained was 4.46 t ha-1 in spring while as the highest (7.98 t ha-1) was obtained in summer.

scholarly journals The climatic imprint of bimodal distributions in vegetation cover for western Africa

2016 ◽  
Vol 13 (11) ◽  
pp. 3343-3357 ◽  
Author(s):  
Zun Yin ◽  
Stefan C. Dekker ◽  
Bart J. J. M. van den Hurk ◽  
Henk A. Dijkstra
Keyword(s):  
Land Cover ◽  
Congo Basin ◽  
Shortwave Radiation ◽  
Mean Annual Precipitation ◽  
Above Ground Biomass ◽  
Precipitation Regime ◽  
Ground Biomass ◽  
Western Africa ◽  
Precipitation Regimes ◽  
Woody Cover

Abstract. Observed bimodal distributions of woody cover in western Africa provide evidence that alternative ecosystem states may exist under the same precipitation regimes. In this study, we show that bimodality can also be observed in mean annual shortwave radiation and above-ground biomass, which might closely relate to woody cover due to vegetation–climate interactions. Thus we expect that use of radiation and above-ground biomass enables us to distinguish the two modes of woody cover. However, through conditional histogram analysis, we find that the bimodality of woody cover still can exist under conditions of low mean annual shortwave radiation and low above-ground biomass. It suggests that this specific condition might play a key role in critical transitions between the two modes, while under other conditions no bimodality was found. Based on a land cover map in which anthropogenic land use was removed, six climatic indicators that represent water, energy, climate seasonality and water–radiation coupling are analysed to investigate the coexistence of these indicators with specific land cover types. From this analysis we find that the mean annual precipitation is not sufficient to predict potential land cover change. Indicators of climate seasonality are strongly related to the observed land cover type. However, these indicators cannot predict a stable forest state under the observed climatic conditions, in contrast to observed forest states. A new indicator (the normalized difference of precipitation) successfully expresses the stability of the precipitation regime and can improve the prediction accuracy of forest states. Next we evaluate land cover predictions based on different combinations of climatic indicators. Regions with high potential of land cover transitions are revealed. The results suggest that the tropical forest in the Congo basin may be unstable and shows the possibility of decreasing significantly. An increase in the area covered by savanna and grass is possible, which coincides with the observed regreening of the Sahara.

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