Yield Estimation Technique Research Articles

Yield Estimation of the August 2020 Beirut Explosion by Using Physics‐Based Propagation Simulations of Regional Infrasound

AbstractThe August 2020 Beirut explosion is the largest single‐fired ammonium nitrate explosion documented in history. The massive explosion excited loud infrasound in the atmosphere, and clear waveforms were recorded by a regional infrasound array at an epicentral distance of 100 km, allowing for accurate measurements of explosion energy. We estimate the explosion size based on the infrasound waveform inversion. Unlike conventional inversions using empirical models, we perform full 3‐D finite‐difference simulations to obtain a physics‐based propagation model for the inversion. Accurate numerical modeling of infrasound is challenging as the propagation is substantially affected by the turbulent atmosphere. Instead of a single deterministic prediction, we provide a range of waveform predictions by running multiple simulations with stochastic weather forecast models, which allows for comprehensive uncertainty analysis of numerical modeling and estimated yields. Finally, we expand the yield estimation technique for seismoacoustic analysis and demonstrate the substantial advantage of the joint approach.

Spatial Rice Yield Estimation Using Multiple Linear Regression Analysis, Semi-Physical Approach and Assimilating SAR Satellite Derived Products with DSSAT Crop Simulation Model

Accurate and consistent information on the area and production of field crops is vital for national and state planning and ensuring food security in India. Satellite-based remote sensing offers a suitable and cost-effective technique for regional- and national-scale crop monitoring. The use of remote sensing data for crop yield estimation has been demonstrated using a semi-physical approach with reasonable success. Assimilating remote sensing data with the DSSAT model and spectral indices-based regression analysis are promising methods for spatially estimating rice crop yields. Rice area and yield in the Cauvery delta zone of Tamil Nadu, India was estimated during samba (August–January) season in the years 2020–2021 using Sentinel 1A Synthetic Aperture Radar satellite data with three different spatial yield estimation methods, namely a spectral indices-based regression analysis, semi-physical approach, and integrating remote products with DSSAT crop growth model. A rice area map was generated for the study area using a rule-based classifier approach utilizing parameterization with a classification accuracy of 94.5% and a kappa score of 0.89. The total classified rice area in Cauvery Delta Region was 379,767 ha, and the Start of Season (SoS) maps for samba season revealed that the major planting period for rice was between 22 September and 9 November in 2020. The study also aimed to identify promising spatial yield estimation techniques for optimal rice yield prediction over large areas. Regression models resulted in rice yields of 3234 to 3905 kg ha−1 with a mean of 3654 kg ha−1. The net primary product was computed using the periodical PAR, fAPAR, Wstress, Tstress, and maximum radiation use efficiency in a semi-physical approach. The resultant rice yields ranged between 2652 and 3438 kg ha−1 with the mean of 3076 kg ha−1. During the integration of remote sensing products with crop growth models, LAI values were extracted from dB images and utilized to simulate rice yields in the range of 3684 to 4012 kg ha−1 with the mean of 3855 kg ha−1. When compared to the semi-physical approach, both integrating remote sensing products with the DSSAT crop growth model and spectral indices-based regression analysis had R2 greater than 0.80, NRMSE of less than 10%, and agreement of more than 90%, indicating that these two approaches could be used for spatial rice yield estimation.

Yield estimation of citrus fruit using rapid image processing in natural background

Yield estimation can provide critical information for high-value crop growers. It helps them to plan and coordinate the logistics of harvesting operations. Most existing yield estimation techniques are based on manual sampling and statistical approaches, which are complex, labor-intensive, and time-consuming. In recent years, advances in image processing and the ubiquitous use of autonomous platforms, such as unmanned aerial platforms, have provided an opportunity for faster and more accurate yield estimation techniques for different crops. A new rapid, efficient, and accurate image processing algorithm was proposed in this study. The image segmentation technique was able to separate mature citrus trees and natural background in CIE (Commission International Eclairage) L*a*b* (i.e., Lab) color space. The effect of camera distance to object, affected resolution, and detection accuracy were evaluated and discussed. The results showed that the distance between 1.524 meters (5 feet) and 2.134 meters (7 feet) could provide high detection accuracy. The number of correctly counted fruit reached 91.69% at the average processing time of 1.1 s per picture for 132 pictures. A correlation coefficient (R2) of 0.98 was obtained between the citrus counting algorithm and counting performed through human observation of 66 sample trees. The comparative analysis of the proposed method showed a higher accuracy rate and robustness compared to other similar studies.

Improved yield estimation technique for rice and wheat in Uttar Pradesh, Madhya Pradesh and Maharashtra States in India

Improved yield estimation technique for rice and wheat in Uttar Pradesh, Madhya Pradesh and Maharashtra States in India

Efficient Yield Estimation of Microwave Structures Using Mesh Deformation-Incorporated Space Mapping Surrogates

Accurateyield estimation is of particular importance in microwave component design due to the presence of manufacturing tolerances. Coarse- and fine-mesh space mapping (SM) methodology has been regarded as a powerful tool for facilitating electromagnetic (EM)-based yield estimation, especially when equivalent circuit coarse models are not available. This letter proposes a novel EM-based yield estimation technique using mesh deformation-incorporated coarse- and fine-mesh SM. Through the incorporation of mesh deformation into coarse-mesh simulation, both the coarse-mesh EM responses and the coarse-mesh EM sensitivities change continuously as geometrical parameters change. This allows the proposed technique to use a coarser mesh in building the SM surrogate than the conventional SM technique, thereby accelerating the overall EM-based yield estimation process. The proposed yield estimation technique is demonstrated through two microwave examples.

Rice Yield Estimation Based on Spaceborne SAR: A Review From 1988 to 2018

Rice is a staple food for nearly half the world's population. Therefore, rice yield estimation has far-reaching significance for food security. As the population continues to expand, rice is often in short supply. Timely and accurate data on rice production is crucial for governments and markets. Traditional rice yield estimation techniques cannot meet the current demand. With its ability to penetrate clouds and rain and the advantage of abundant remote sensing information, spaceborne synthetic aperture radar (S-SAR) can be used in the application of rice yield measurement. In this paper, the history and recent developments of rice yield measurement (1988-2018) based on spaceborne synthetic aperture radar are introduced, and the application prospect and technical development of rice yield monitoring are sought.

An innovative intelligent system based on remote sensing and mathematical models for improving crop yield estimation

There are many crop yield estimation techniques which are used in countries around the world, but the most effective is the one based on remote sensing data and technologies. However, remote sensing data which are needed to estimate crop yield is incomplete most of the time due to many obstacles such as climate conditions (percentage of cloud cover), and low temporal resolution. These problems reduce the effectiveness of the known crop yield estimation techniques and render them obsolete. There was many attempts to solve these problems by using high temporal resolution and low spatial resolution images. However, this type of images are suitable for very large homogeneous crop fields. To compensate for the lack of high spatial resolution satellite images, a new mathematical model is created. Based on the new mathematical model an intelligent system is implemented that includes the use of energy balance equation to improve the crop yield estimation. To verify the results of the intelligent system, several farmers are interviewed and information about their crops yield is collected. The comparison between the estimated crop yield and the actual production in different fields proves the high accuracy of the intelligent system.

Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques

Many crop yield estimation techniques are being used, however the most effective one is based on using geospatial data and technologies such as remote sensing. However, the remote sensing data which are needed to estimate crop yield are insufficient most of the time due to many problems such as climate conditions (% of clouds), and low temporal resolution. There have been many attempts to solve the lack of data problem using very high temporal and very low spatial resolution images such as Modis. Although this type of image can compensate for the lack of data due to climate problems, they are only suitable for very large homogeneous crop fields. To compensate for the lack of high spatial resolution remote sensing images due to climate conditions, a new optimization model was created. Crop yield estimation is improved and its precision is increased based on the new model that includes the use of the energy balance equation. To verify the results of the crop yield estimation based on the new model, information from local farmers about their potato crop yields for the same year were collected. The comparison between the estimated crop yields and the actual production in different fields proves the efficiency of the new optimization model.

TECHNOLOGIES FOR ENHANCING PECAN PRODUCTION AND PROCESSING

This paper summarizes research at Oklahoma State University (OSU) to develop innovative technologies that provide solutions to issues and problems affecting pecan production and processing. Some of the projects areas include; Pecan yield estimation technique using backscattered terrestrial microwave sensing. Dielectric spectroscopy for estimating quality of in-shell pecans. Wireless image sensor networks in estimating the population of pecan weevils. Low-cost small-scale sanitizer for in-shell pecans. Optical sensors and algorithms that adequately predict plant N status for nitrogen management. X-ray machine vision inspection systems for pecan defect identification. Accurate estimates of pecans in the field prior to harvest are critically important for production management decisions and marketing. Pecan producers, processors and marketers identified improved accuracy of crop estimates as a research priority for the industry. The pecan weevil (Curculio caryae) is considered a key pest. Without timely insecticide treatments, crop losses can exceed 75%. Research has focused on the design of a wireless sensor network for real-time monitoring and population estimation of pecan weevils. Nitrogen (N) has become a major cost in producing pecans. Traditionally, N is applied once or twice per season. Application rates usually exceed the minimum N requirement for optimum production. Research has focused on reducing N inputs while maintaining production levels. Sorting of defective nuts is difficult because nutmeat defects are not fully recognizable by physical properties, color and appearance of whole unshelled nuts. Commercial sorters are available to sort nutmeat after shelling the nuts, but this results in unnecessary shelling of defective nuts. Development of automated inspection systems to identify good pecan nuts from defective ones before shelling would reduce processing costs. Due to space limitations, only the highlights of each research area will be presented. Interested readers desiring more comprehensive discussion of the projects are encouraged to access the references.

Fast Monte Carlo Estimation of Timing Yield With Importance Sampling and Transistor-Level Circuit Simulation

Considerable effort has been expended in the electronic design automation community in trying to cope with the statistical timing problem. Most of this effort has been aimed at generalizing the static timing analyzers to the statistical case. On the other hand, detailed transistor-level simulations of the critical paths in a circuit are usually performed at the final stage of performance verification. We describe a transistor-level Monte Carlo (MC) technique which makes final transistor-level timing verification practically feasible. The MC method is used as a golden reference in assessing the accuracy of other timing yield estimation techniques. However, it is generally believed that it can not be used in practice as it requires too many costly transistor-level simulations. We present a novel approach to constructing an improved MC estimator for timing yield which provides the same accuracy as standard MC but at a cost of much fewer transistor-level simulations. This improved estimator is based on a unique combination of a variance reduction technique, importance sampling, and a cheap but approximate gate delay model. The results we present demonstrate that our improved yield estimator achieves the same accuracy as standard MC at a cost reduction reaching several orders of magnitude.

Nonlinear statistical modeling and yield estimation technique for use in Monte Carlo simulations [microwave devices and ICs

A novel nonlinear statistical modeling technique for microwave devices and a new approach to yield estimation for microwave integrated circuits are presented. The statistical modeling methodology is based on a combination of applied multivariate methods with heuristic techniques. These include principal component analysis and factor analysis in conjunction with maximally flat quadratic interpolation and group method of data handling. The proposed modeling approach, when applied to the database of extracted equivalent circuit parameters (ECPs) for a pseudomorphic high electron mobility transistor device, has proven that it can generate simulated ECPs, S-parameters, that are statistically indistinguishable from measured ones. A new yield estimation technique based on a Latin hypercube sampling (LHS) is also demonstrated. The LHS-based simulation is utilized as an alternative to primitive Monte Carlo (PMC) simulation in yield analysis. An equally confident yield estimate based on the LHS method requires only one-fourth of those simulations needed when the PMC technique is used.

A rigorous error analysis of D/A conversion with dynamic element matching

A known approach to reducing harmonic distortion in D/A converters involves a technique called dynamic element matching, The idea is not to reduce the power of the overall conversion error but rather to give it a random, noise-like structure. By reducing the correlation among successive samples of the conversion error, harmonic distortion is reduced. This paper presents the first rigorous and quantitative theoretical analysis of the conversion error introduced by an important type of D/A converter with dynamic element matching, In addition to supporting previously published experimental results that indicate the conversion error consists of white noise instead of harmonic distortion, the analysis provides an expression for the power of the white noise in terms of the power of the input sequence and the component matching errors. A yield estimation technique based on the expression is presented that can be used to estimate how the power of the white noise varies across different copies of the same D/A converter circuit for any given component matching error statistics.

CAD for statistical analysis and design of microwave circuits

AbstractStatistical circuit design is essentially a method of design for reliability and high yield. For all but the smallest circuits, this is a CAD problem. Within this problem there are three subproblems: design centering, tolerance assignment, and variability reduction. This paper concentrates on design centering. Yield optimization is inherently a difficult problem because yield cannot, in general, be calculated exactly; only estimates of the yield are available. Furthermore, yield gradients are not available when some yield estimation techniques are used. This paper presents a review of useful definitions using parameter space concepts. Yield estimation techniques and some of their properties are also presented. The two distinct yield optimization strategies commercially available today, statistical and deterministic, are each discussed in detail. Examples of each are given.