Estimating Photosynthetically Active Radiation Intercepted by Almond and Walnut Trees using UAV-Captured Aerial Images and Solar Zenith Angle

2021 ◽  
Vol 37 (4) ◽  
pp. 751-761
Author(s):  
Francisco Rojo ◽  
Rajveer Dhillon ◽  
Shrinivasa K. Upadhyaya ◽  
Hunjun Liu ◽  
Jedediah Roach
Keyword(s):  
Maximum Likelihood ◽  
Zenith Angle ◽  
Light Interception ◽  
Aerial Images ◽  
Likelihood Estimator ◽  
Active Radiation ◽  
Shadow Area ◽  
Individual Trees ◽  
The Relationship ◽  
Canopy Light Interception

Highlights Measurement of canopy light interception data using a ground-based mobile system. Using UAV-captured aerial images and zenith angle to estimate canopy light interception at different times of the day. Identifying boundaries of individual trees using the maximum likelihood estimator and the watershed algorithm. Abstract. Photosynthetically Active Radiation (PAR) absorbed by the leaves is a key piece of information to study the crop response to environmental conditions that could be used to estimate crop production potential. Canopies in a commercial orchard present differences in their capability to intercept light mainly due to the spatial variability in canopy development. There is a need for developing tools that could capture spatial variability in PAR interception to predict potential yield. Unmanned Aerial Vehicles (UAV) present an interesting alternative to provide this information, as they cover a larger area than ground-based systems in a shorter period with high spatial resolution. The objective of this study was to determine the relationship between the shadow of a tree derived from a ground-based canopy light interception scan obtained using a lightbar mounted on a mobile platform and that acquired from UAV Red-Green-Blue (RGB) images. Information acquired by an UAV was classified to separate canopy from its shadow, grass and sunlit soil using maximum likelihood estimator. Boundaries of individual trees were identified based on their positions using watershed transform algorithm. The relationship between canopy PAR interception data, sun angle in the sky (zenith angle), and the information derived from aerial images was analyzed. Coefficient of determination (R2) values of 0.92 and 0.88 were found for the multiple linear regression between PAR, the shadow area and the cosine of zenith angle obtained for almond and walnut crops, respectively. Moreover, R2 values of 0.81 and 0.86 were found for the relationship between the shadow’s area obtained underneath the canopy and the shadow’s area obtained from the UAV images and the cosine of the zenith angle for almond and walnut crops, respectively. The results show that the PAR interception can be estimated using the zenith angle and the area of the shadow, which can be obtained from a RGB aerial image. Keywords: Almond, Canopy segmentation, Image classification, PAR interception, Shadow area, UAV, Walnut.

scholarly journals High Density and Uniform Plant Distribution Improve Soybean Yield by Regulating Population Uniformity and Canopy Light Interception

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1880
Author(s):  
Cailong Xu ◽  
Ruidong Li ◽  
Wenwen Song ◽  
Tingting Wu ◽  
Shi Sun ◽  
...  
Keyword(s):  
Seed Yield ◽  
Dry Matter ◽  
Light Interception ◽  
Plant Distribution ◽  
Planting Density ◽  
Dry Matter Accumulation ◽  
Plant Spacing ◽  
Soybean Yield ◽  
The Relationship ◽  
Canopy Light Interception

Optimizing the spatial distribution of plants under normal conditions of water and fertilizer is widely used by farmers to improve soybean yield. However, the relationship between soybean yield and spatial plant distribution in the field has not been well studied. This study examined the effect of planting density and plant distribution pattern on soybean plant growth, yield components, canopy light interception, and dry matter accumulation. We also analyzed the relationship between photosynthetic rate, dry matter accumulation, and yield under different planting densities and plant distribution. A two year field experiment was conducted during the 2018 and 2019 soybean planting seasons. Two planting densities (1.8 × 105 and 2.7 × 105 plants ha−1) and two plant distribution patterns (uniform and non-uniform plant spacing) were tested. Higher planting density significantly increased the canopy light interception and dry matter accumulation during soybean growth, leading to increased soybean productivity. The seed yield of soybean under higher planting density was 22.8% higher than under normal planting density. Soybean planted under uniform spacing significantly reduced the differences plant-to-plant. Uniform plant spacing significantly increased the canopy light interception and dry matter accumulation of the soybean population. In addition, the coefficient of variation of seed weight per plant between individual plants under uniform plant distribution decreased by 71.5% compared with non-uniform plant distribution. Furthermore, uniform plant distribution increased soybean seed yield by 9.5% over non-uniform plant distribution. This study demonstrates that increasing planting density under uniform plant distribution can be useful to obtain higher seed yield without increasing other farm inputs.

scholarly journals The Relationship Between Leaf Area and Light Interception by Spur and Extension Shoot Leaves and Apple Orchard Productivity

HortScience ◽  
2000 ◽  
Vol 35 (7) ◽  
pp. 1202-1206 ◽  
Author(s):  
Jens N. Wünsche ◽  
Alan N. Lakso
Keyword(s):  
Leaf Area ◽  
Production Systems ◽  
Light Interception ◽  
Apple Orchard ◽  
Fruit Yield ◽  
Area Index ◽  
Canopy Development ◽  
Slender Spindle ◽  
The Relationship ◽  
Canopy Light Interception

The study evaluated the relationship of spur vs. extension shoot leaf area and light interception to apple (Malus {XtimesX}domesticaBorkh.) orchard productivity. Fifteen-year-old `Marshall McIntosh'/M.9 trees had significantly greater leaf area and percentage of light interception at 3-5 and 10-12 weeks after full bloom (AFB) than did 4-year-old `Jonagold'/Mark trees. Despite significant increases in leaf area and light interception with canopy development, linear relationships between total, spur, and extension shoot canopy leaf area index (LAI) and 1) light interception and 2) fruit yield were similar at both times. Mean total and spur canopy LAI and light interception were significantly and positively correlated with fruit yield; however, extension shoot LAI and light interception were poorly correlated with yield. In another study total, spur and extension shoot canopy light interception varied widely in five apple production systems: 15-year-old central leader `Redchief Delicious' MM.111, 15-year-old central leader `Redchief Delicious' MM.111/M.9, 16-year-old slender spindle `Marshall McIntosh' M.9, 14-year-old `Jerseymac' M.9 on 4-wire trellis, and 17-year-old slender spindle `MacSpur' M.9. Yields in these orchards were curvilinearly related to total and extension shoot canopy light interception and decreased when total light interception exceeded 60% and extension shoot interception exceeded 25%. Fruit yields were linearly and highly correlated (r2 = 0.78) with spur light interception. The findings support the hypothesis that fruit yields of healthy apple orchards are better correlated with LAI and light interception by spurs than by extension shoots. The results emphasize the importance of open, well-illuminated, spur-rich tree canopies for high productivity.

Large-sample confidence intervals of information-theoretic measures in linguistics

2020 ◽  
Vol 6 (1) ◽  
pp. 19-54
Author(s):  
Ryan Ka Yau Lai ◽  
Youngah Do
Keyword(s):  
Maximum Likelihood ◽  
Corpus Linguistics ◽  
Delta Method ◽  
Confidence Bounds ◽  
Likelihood Estimator ◽  
Information Theoretic ◽  
Leibler Divergence ◽  
Information Theoretic Measures ◽  
Data Points ◽  
Measure Of Uncertainty

This article explores a method of creating confidence bounds for information-theoretic measures in linguistics, such as entropy, Kullback-Leibler Divergence (KLD), and mutual information. We show that a useful measure of uncertainty can be derived from simple statistical principles, namely the asymptotic distribution of the maximum likelihood estimator (MLE) and the delta method. Three case studies from phonology and corpus linguistics are used to demonstrate how to apply it and examine its robustness against common violations of its assumptions in linguistics, such as insufficient sample size and non-independence of data points.

The Comparison Between the Bayes Estimator and the Maximum Likelihood Estimator of the Reliability Function for Negative Exponential Distribution

2018 ◽  
pp. 439
Author(s):  
Hazim Mansour Gorgees ◽  
Bushra Abdualrasool Ali ◽  
Raghad Ibrahim Kathum
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Exponential Distribution ◽  
Reliability Function ◽  
Bayes Estimator ◽  
Likelihood Estimator ◽  
Monte Carlo Simulation Technique ◽  
Negative Exponential Distribution ◽  
Negative Exponential ◽  
Better Than

     In this paper, the maximum likelihood estimator and the Bayes estimator of the reliability function for negative exponential distribution has been derived, then a Monte –Carlo simulation technique was employed to compare the performance of such estimators. The integral mean square error (IMSE) was used as a criterion for this comparison. The simulation results displayed that the Bayes estimator performed better than the maximum likelihood estimator for different samples sizes.

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

scholarly journals 115 Nitrogen fertilizer effects on above ground sward characteristics and beef heifer performance from native warm-season grass blends

2020 ◽  
Vol 98 (Supplement_2) ◽  
pp. 26-27
Author(s):  
Caroline Chappell ◽  
Landon Marks ◽  
Katie Mason ◽  
Mary K Mullenix ◽  
Sandra L Dillard ◽  
...  
Keyword(s):  
Nutritive Value ◽  
Warm Season ◽  
Ground Cover ◽  
Application Rate ◽  
Light Interception ◽  
Botanical Composition ◽  
Forage Production ◽  
Beef Heifers ◽  
Fertilizer Application Rate ◽  
Canopy Light Interception

Abstract A 2-yr study was conducted at Black Belt Research and Extension Center in Marion Junction, AL, to evaluate the effect of nitrogen (N) fertilizer application rate on forage production characteristics, nutritive value, and animal performance of beef heifers grazing a mixture of native warm-season grasses (NWSG) including big bluestem, little bluestem, and indiangrass. Six, two-hectare plots were randomly assigned to one of two treatments (0 or 67 kg N ha-1 applied in early April; n = 3 replications per treatment). Paddocks were continuously stocked with four weaned Angus × Simmental beef heifers (initial BW 288 ± 7 kg) from late May/early June through mid-to-late August during 2018 (73 grazing d) and 2019 (70 grazing d), respectively. Put-and-take cattle were used to manage forage to a target of 38 cm. Forage mass and canopy heights were collected every two weeks during the trial. Visual ground cover ratings, canopy light interception, and botanical composition were measured at the beginning and end of the trial in each year. Hand-plucked samples were collected every two weeks during the grazing trial to determine forage nutritional value. Data were analyzed using the PROC MIXED procedure in SAS 9.4, and differences were declared significant when P ≤ 0.05. Nitrogen fertilized NWSG had greater crude protein (P < 0.0001), sward heights (P = 0.0003), and canopy light interception at the beginning of the season (P = 0.0049) compared to non-fertilized paddocks. However, there were no differences (P ≥ 0.05) among N-fertility treatments for mean forage mass, heifer ADG, or BCS across the 2-yr study. Botanical composition data indicated that indiangrass decreased from 64% to 61% (P = 0.0022) and weed pressure increased from 11% to 15% (P = 0.0064) across the summer grazing season. Canopy light interception decreased by 51% from early June to August in fertilized NWSG and 26% in unfertilized paddocks, respectively. These data illustrate that NWSG systems may provide a viable grazing system in the summer months under reduced N inputs.

Metric for Disassembly and Reuse Decisions: Formulation and Validation

2008 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Deborah Thurston
Keyword(s):  
Maximum Likelihood ◽  
Value Function ◽  
Choice Theory ◽  
Decision Makers ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Maximum Value ◽  
Long Range Planning ◽  
The Relationship ◽  
The Value Function

Design for disassembly and reuse focuses on developing methods to minimize difficulty in disassembly for maintenance or reuse. These methods can gain substantially if the relationship between component attributes (material mix, ease of disassembly etc.) and their likelihood of reuse or disposal is understood. For products already in the marketplace, a feedback approach that evaluates willingness of manufacturers or customers (decision makers) to reuse a component can reveal how attributes of a component affect reuse decisions. This paper introduces some metrics and combines them with ones proposed in literature into a measure that captures the overall value of a decision made by the decision makers. The premise is that the decision makers would choose a decision that has the maximum value. Four decisions are considered regarding a component’s fate after recovery ranging from direct reuse to disposal. A method on the lines of discrete choice theory is utilized that uses maximum likelihood estimates to determine the parameters that define the value function. The maximum likelihood method can take inputs from actual decisions made by the decision makers to assess the value function. This function can be used to determine the likelihood that the component takes a certain path (one of the four decisions), taking as input its attributes, which can facilitate long range planning and also help determine ways reuse decisions can be influenced.

Covariance matrix of the bias-corrected maximum likelihood estimator in generalized linear models

2013 ◽  
Vol 55 (3) ◽  
pp. 643-652
Author(s):  
Gauss M. Cordeiro ◽  
Denise A. Botter ◽  
Alexsandro B. Cavalcanti ◽  
Lúcia P. Barroso
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Covariance Matrix ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Likelihood Estimator
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