scholarly journals Theory Survey of Stock Yield Prediction Models

2009 ◽  
Vol 1 (1) ◽  
Author(s):  
Wei Shen
Keyword(s):  
Prediction Models ◽  
Yield Prediction

Assessment of rice yield prediction models based on big data analytics for better supply chain decision-making in Thailand

2021 ◽  
Vol 12 (3) ◽  
pp. 221
Author(s):  
John K.M. Kuwornu ◽  
Chutiporn Anutariya ◽  
Attaphongse Taparugssanagorn ◽  
Sumanya Ngandee
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Prediction Models ◽  
Rice Yield ◽  
Big Data Analytics ◽  
Yield Prediction

scholarly journals Predicting tomato field-yield using continuous monitoring of young tomato water status

2021 ◽  
Author(s):  
Sanbon Gosa ◽  
Amit Koch ◽  
Itamar Shenhar ◽  
Joseph Hirschberg ◽  
Dani Zamir ◽  
...  
Keyword(s):  
Prediction Models ◽  
Water Status ◽  
Total Yield ◽  
Field Measurements ◽  
Carotenoid Biosynthesis ◽  
Yield Prediction ◽  
Drought Treatment ◽  
Recessive Mutations ◽  
Tomato Yield ◽  
Tomato Field

To address the challenge of predicting tomato yields in the field, we used whole-plant functional phenotyping to evaluate water relations under well-irrigated and drought conditions. The genotypes tested are known to exhibit variability in their yields in wet and dry fields. The examined lines included two lines with recessive mutations that affect carotenoid biosynthesis, zeta z2083 and tangerine t3406, both isogenic to the processing tomato variety M82. The two mutant lines were reciprocally grafted onto M82 and multiple physiological characteristics were measured continuously, as well as before, during and after drought treatment in the greenhouse. A comparative analysis of greenhouse and field yields showed that the whole-canopy stomatal conductance (gsc) in the morning and cumulative transpiration (CT) were strongly correlated with field measurements of total yield (TY: r2 = 0.9 and 0.77, respectively) and plant vegetative weight (PW: r2 = 0.6 and 0.94, respectively). Furthermore, the minimum CT during drought and the rate of recovery when irrigation was resumed were both found to predict resilience. Keywords: drought tolerance, functional genomic mapping, functional phenotyping, physiological trait, time-series measurements, tomato, yield prediction, yield-prediction models

scholarly journals Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

2019 ◽  
Vol 11 (2) ◽  
pp. 533 ◽  
Author(s):  
Gniewko Niedbała
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Precision Agriculture ◽  
Prediction Models ◽  
Approximation Error ◽  
Main Element ◽  
Forecast Errors ◽  
Yield Prediction ◽  
Winter Rapeseed ◽  
Artificial Neural

The aim of the work was to produce three independent, multi-criteria models for the prediction of winter rapeseed yield. Each of the models was constructed in such a way that the yield prediction can be carried out on three dates: April 15th, May 31st, and June 30th. For model building, artificial neural networks with multi-layer perceptron (MLP) topology were used, on the basis of meteorological data (temperature and precipitation) and information about mineral fertilisation. The data were collected from the years, 2008–2015, from 328 production fields located in Greater Poland, Poland. An assessment of the quality of forecasts produced based on neural models was verified by determination of forecast errors using RAE (relative approximation error), RMS (root mean square error), MAE (mean absolute error) error indicators, and MAPE (mean absolute percentage error). An important feature of the produced prediction models is the ability to realize the forecast in the current agrotechnical year on the basis of the current weather and fertiliser information. The lowest MAPE error values were obtained for the neural model WR15_04 (April 15th) based on the MLP network with structure 15:15-18-11-1:1, which reached 7.51%. Other models reached MAPE errors of 7.85% for model WR31_05 (May 31st) and 8.12% for model WR30_06 (June 30th). The performed sensitivity analysis gave information about the factors that have the greatest impact on winter rapeseed yields. The highest rank of 1 was obtained by two networks for the same independent variable in the form of the sum of precipitation within a period from September 1st to December 31st of the previous year. However, in model WR15_04, the highest rank obtained a feature in the form of a sum of molybdenum fertilization in the current year (MO_CY). The models of winter rapeseed yield produced in the work will be the basis for the construction of new forecasting tools, which may be an important element of precision agriculture and the main element of decision support systems.

Intelligent yield prediction models for high-speed microprocessors

2002 ◽  
Author(s):  
Tae Seon Kim ◽  
Se Hwan Ahn ◽  
Young Gyun Jang ◽  
Jeong In Lee ◽  
Kil Jae Lee ◽  
...  
Keyword(s):  
High Speed ◽  
Prediction Models ◽  
Yield Prediction

Yield prediction models for optimization of high-speed micro-processor manufacturing processes

2002 ◽  
Author(s):  
Tae Seon Kim ◽  
Se Hwan Ahn ◽  
Young Gyun Jang ◽  
Jeong In Lee ◽  
Kil Jae Lee ◽  
...  
Keyword(s):  
High Speed ◽  
Prediction Models ◽  
Manufacturing Processes ◽  
Yield Prediction

Use of live ultrasound, weight and linear measurements to predict carcass composition of young beef bulls

2005 ◽  
Vol 85 (1) ◽  
pp. 23-35 ◽  
Author(s):  
R. Bergen ◽  
S. P. Miller ◽  
I. B. Mandell ◽  
W. M. Robertson
Keyword(s):  
Prediction Models ◽  
Carcass Composition ◽  
Muscle Size ◽  
Yield Prediction ◽  
Longissimus Muscle ◽  
Marbling Score ◽  
Meat Yield ◽  
Lean Meat ◽  
Short Probe ◽  
Beef Bulls

Pre-slaughter ultrasound and whole side dissection data from 47 crossbred bulls were used to assess (1) the relative value of six previously published equations based on live animal measurements, (2) the value of alternative pre-slaughter measurements, and (3) the value of alternative ultrasound probes as predictors of whole side lean meat yield. Analysis of absolute bias-corrected residuals indicated that all six previously published equations predicted whole side lean meat yield with similar accuracy (P = 0.62), but analysis of absolute rank residuals indicated that an equation originally based on carcass measurements tended (P = 0.17) to rank bulls less precisely than five ultrasound-based equations. Breed composition, age, liveweight, hip width, heart girth, and round muscle depths did not contribute to new lean meat yield prediction equations (P > 0.10), but height, 12th/13th rib body wall, rump fat, and gluteus medius muscle depths and marbling score did (P < 0.10). However, examination of absolute residuals and absolute rank residuals indicated that accuracy (P = 0.55) and precision (P = 0.64) did not improve significantly compared to equations based only on height, rib fat and longissimus muscle size. Similarly, analysis of absolute residuals and absolute rank residuals indicated that fat and longissimus muscle depth measurements collected with a short probe predicted whole side lean meat yield as accurately and precisely as measurements collected with a long probe. Results indicated that (1) equations based on live measurements may provide more precise predictions of lean meat yield than equations derived from carcass measurements, (2) supplementing ultrasonic rib fat and longissimus muscle measurements with additional ultrasound measurements did not improve the accuracy or precision of lean meat yield prediction, and (3) lean meat yield of yearling bulls can be accurately predicted using fat and longissimus muscle depth measurements collected with a short probe. Key words: Ultrasound, beef bulls, carcass composition, prediction models

scholarly journals Soybean Yield Preharvest Prediction Based on Bean Pods and Leaves Image Recognition Using Deep Learning Neural Network Combined With GRNN

2022 ◽  
Vol 12 ◽  
Author(s):  
Wei Lu ◽  
Rongting Du ◽  
Pengshuai Niu ◽  
Guangnan Xing ◽  
Hui Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Recognition ◽  
Clustering Algorithm ◽  
Prediction Models ◽  
Soybean Seed ◽  
Soybean Plant ◽  
Single Shot ◽  
Yield Prediction ◽  
Soybean Yield

Soybean yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. The earlier the prediction during the growing season the better. Accurate soybean yield prediction is important for germplasm innovation and planting environment factor improvement. But until now, soybean yield has been determined by weight measurement manually after soybean plant harvest which is time-consuming, has high cost and low precision. This paper proposed a soybean yield in-field prediction method based on bean pods and leaves image recognition using a deep learning algorithm combined with a generalized regression neural network (GRNN). A faster region-convolutional neural network (Faster R-CNN), feature pyramid network (FPN), single shot multibox detector (SSD), and You Only Look Once (YOLOv3) were employed for bean pods recognition in which recognition precision and speed were 86.2, 89.8, 80.1, 87.4%, and 13 frames per second (FPS), 7 FPS, 24 FPS, and 39 FPS, respectively. Therefore, YOLOv3 was selected considering both recognition precision and speed. For enhancing detection performance, YOLOv3 was improved by changing IoU loss function, using the anchor frame clustering algorithm, and utilizing the partial neural network structure with which recognition precision increased to 90.3%. In order to improve soybean yield prediction precision, leaves were identified and counted, moreover, pods were further classified as single, double, treble, four, and five seeds types by improved YOLOv3 because each type seed weight varies. In addition, soybean seed number prediction models of each soybean planter were built using PLSR, BP, and GRNN with the input of different type pod numbers and leaf numbers with which prediction results were 96.24, 96.97, and 97.5%, respectively. Finally, the soybean yield of each planter was obtained by accumulating the weight of all soybean pod types and the average accuracy was up to 97.43%. The results show that it is feasible to predict the soybean yield of plants in situ with high precision by fusing the number of leaves and different type soybean pods recognized by a deep neural network combined with GRNN which can speed up germplasm innovation and planting environmental factor optimization.

scholarly journals DEVELOPMENT OF YIELD PREDICTION MODELS IN THE MAIZE CROP USING SPECTRAL DATA FOR PRECISION AGRICULTURE APPLICATIONS

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Victor Rueda Ayala ◽  
Seshadri Kunapuli ◽  
Javier Maiguashca
Keyword(s):  
Precision Agriculture ◽  
Prediction Models ◽  
Polynomial Regression ◽  
Machine Learning Techniques ◽  
Yield Prediction ◽  
Multinomial Regression ◽  
Maize Crop ◽  
Learning Techniques ◽  
Predictive Algorithms ◽  
National Production

Machine learning techniques were applied with statistical tools such as linear, logistic and multinomial regression, to work out predictive algorithms for yield estimation. Spectroradiometer readings were collected throughout the main maiz producing provinces of Ecuador, at two crop development stages.A model using six degree polynomial regression is recommended for acceptable yield prediction. This model could contribute to decide about imports strategies and avoid the overlapping with the national production.

scholarly journals Prediction Strategies for Leveraging Information of Associated Traits under Single- and Multi-Trait Approaches in Soybeans

Agriculture ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 308
Author(s):  
Reyna Persa ◽  
Arthur Bernardeli ◽  
Diego Jarquin
Keyword(s):  
Prediction Models ◽  
Predictive Ability ◽  
Yield Prediction ◽  
Genomic Information ◽  
Nested Association Mapping ◽  
Phenotypic Information ◽  
Partially Observed ◽  
Superior Genotypes ◽  
Trait Model ◽  
Selection Of

The availability of molecular markers has revolutionized conventional ways to improve genotypes in plant and animal breeding through genome-based predictions. Several models and methods have been developed to leverage the genomic information in the prediction context to allow more efficient ways to screen and select superior genotypes. In plant breeding, usually, grain yield (yield) is the main trait to drive the selection of superior genotypes; however, in many cases, the information of associated traits is also routinely collected and it can potentially be used to enhance the selection. In this research, we considered different prediction strategies to leverage the information of the associated traits ([AT]; full: all traits observed for the same genotype; and partial: some traits observed for the same genotype) under an alternative single-trait model and the multi-trait approach. The alternative single-trait model included the information of the AT for yield prediction via the phenotypic covariances while the multi-trait model jointly analyzed all the traits. The performance of these strategies was assessed using the marker and phenotypic information from the Soybean Nested Association Mapping (SoyNAM) project observed in Nebraska in 2012. The results showed that the alternative single-trait strategy, which combines the marker and the information of the AT, outperforms the multi-trait model by around 12% and the conventional single-trait strategy (baseline) by 25%. When no information on the AT was available for those genotypes in the testing sets, the multi-trait model reduced the baseline results by around 6%. For the cases where genotypes were partially observed (i.e., some traits observed but not others for the same genotype), the multi-trait strategy showed improvements of around 6% for yield and between 2% to 9% for the other traits. Hence, when yield drives the selection of superior genotypes, the single-trait and multi-trait genomic prediction will achieve significant improvements when some genotypes have been fully or partially tested, with the alternative single-trait model delivering the best results. These results provide empirical evidence of the usefulness of the AT for improving the predictive ability of prediction models for breeding applications.

Growth and Yield Prediction Models for Hybrid Acacia (A. auriculiformis X A. mangium) Grown in the Plantations of Bangladesh

2005 ◽  
Vol 8 (8) ◽  
pp. 1137-1141 ◽  
Author(s):  
M. Shah Newaz . ◽  
M. Kamaluddin . ◽  
A.Z.M. Manzoor Rashi .
Keyword(s):  
Prediction Models ◽  
Growth And Yield ◽  
Yield Prediction
Sign in / Sign up

Export Citation Format

Share Document