Study and application of grain yield forecasting model

Xingmei, Xu; Cao, Lei; Zhou, Jing; Fengyan, Su

doi:10.1109/iccsnt.2015.7490829

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A gray model (GM) and an autoregressive integrated moving average (ARIMA) model are implemented in [24] to predict grain crop yields from yield data for the years 1998 to 2008. They performed the predictions for the years from 2009 to 2013 and obtained an average error of 7.88% (GM) and 12.32% (ARIMA), and an average accuracy of 92.12% and 87.68% respectively.…”

Section: State Of the Artmentioning

confidence: 99%

A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

Rodr韌uez¹,

Corrales²,

Griol³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Coffee plays a key role in the generation of rural employment in Colombia. More than 785,000 workers are directly employed in this activity, which represents the 26% of all jobs in the agricultural sector. Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities, and resources (number of workers, required infrastructures), anticipating negotiations, estimating, price, and foreseeing losses of coffee production in a specific territory. These important processes can be affected by several factors that are not easy to predict (e.g., weather variability, diseases, or plagues.). In this paper, we propose a non-destructive time series model, based on weather and crop management information, that estimate coffee production allowing coffee growers to improve their management of agricultural activities such as flowering calendars, harvesting seasons, definition of irrigation methods, nutrition calendars, and programming the times of concentration of production to define the amount of personnel needed for harvesting. The combination of time series and machine learning algorithms based on regression trees (XGBOOST, TR and RF) provides very positive results for the test dataset collected in real conditions for more than a year. The best results were obtained by the XGBOOST model (MAE = 0.03; RMSE = 0.01), and a difference of approximately 0.57% absolute to the main harvest of 2018.

show abstract

Section: State Of the Artmentioning

confidence: 99%

A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

Rodr韌uez¹,

Corrales²,

Griol³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…ARMA( p , q ) model in formula (7) can be expressed as ARIMA( p , d , q ) after d order difference transformation

\begin{matrix} T1 \\ φ (L) {(1 - L)}^{d} y_{t} = Θ (L) ε_{t} . \end{matrix}

ε t is a white noise process with its mean value which is 0 and variance is σ 2 [12]. …”

Section: Relational Coefficient Analysis Of Influence Factors To Fmentioning

confidence: 99%

A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model

Yang

Zhu

2016

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In order to improve the long-term prediction accuracy of feed grain demand, a dynamic forecast model of long-term feed grain demand is realized with joint multivariate regression model, of which the correlation between the feed grain demand and its influence factors is analyzed firstly; then the change trend of various factors that affect the feed grain demand is predicted by using ARIMA model. The simulation results show that the accuracy of proposed combined dynamic forecasting model is obviously higher than that of the grey system model. Thus, it indicates that the proposed algorithm is effective.

show abstract

Fruit yield estimation and forecasting for precision agriculture

Ashokan,

Akbar

2024

AIP Conference Proceedings

View full text Add to dashboard Cite

Study and application of grain yield forecasting model

Cited by 3 publications

References 0 publications

A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

A Forecasting Model for Feed Grain Demand Based on Combined Dynamic Model

Fruit yield estimation and forecasting for precision agriculture

Contact Info

Product

Resources

About