scholarly journals Establishment of rice yield prediction model using canopy reflectance

2012 ◽  
Vol 4 (4) ◽  
pp. 39-50 ◽  
Author(s):  
W. Chang
Keyword(s):  
Prediction Model ◽  
Rice Yield ◽  
Canopy Reflectance ◽  
Yield Prediction

Yield Prediction Model for Potato Using Landsat Time Series Images Driven Vegetation Indices

2018 ◽  
Vol 1 (1-2) ◽  
pp. 29-38 ◽  
Author(s):  
Imran Hossain Newton ◽  
A. F. M Tariqul Islam ◽  
A. K. M. Saiful Islam ◽  
G. M. Tarekul Islam ◽  
Anika Tahsin ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Vegetation Indices ◽  
Yield Prediction ◽  
Time Series Images

scholarly journals UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 202
Author(s):  
Zhen Chen ◽  
Qian Cheng ◽  
Fuyi Duan ◽  
Xiuqiao Huang ◽  
Honggang Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Winter Wheat ◽  
Prediction Model ◽  
Grain Filling ◽  
Yield Prediction ◽  
Spectral Indices ◽  
Learner Model ◽  
Ensemble Machine Learning ◽  
Base Learner

Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

scholarly journals Comparison Statistical Rice Yield Prediction with Multiple Weather Parameters

2021 ◽  
pp. 31-38
Author(s):  
T. Thurkkaivel ◽  
G. A. Dheebakaran ◽  
V. Geethalakshmi ◽  
S. G. Patil ◽  
K. Bhuvaneshwari
Keyword(s):  
Bayesian Model ◽  
Tamil Nadu ◽  
Rice Yield ◽  
Model Performance ◽  
Research Centre ◽  
Yield Prediction ◽  
Sunshine Hours ◽  
Tamil Nadu State ◽  
Weather Parameters ◽  
Essential Food

Advance knowledge of harvestable products, especially essential food crops such as rice, wheat, maize, and pulses, would allow policymakers and traders to plan procurement, processing, pricing, marketing, and related infrastructure and procedures. There are many statistical models are being used for the yield prediction with different weather parameter combinations. The performance of these models are dependent on the location’s weather input and its accuracy. In this context, a study was conducted at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore during Kharif (2020) season to compare the performance of four multivariate weather-based models viz., SMLR, LASSO, ENET and Bayesian models for the rice yield prediction at Tanjore district of Tamil Nadu State with Tmax, Tmin, Mean RH, WS, SSH, EVP and RF.  The results indicated that the R2, RMSE, and nRMSE values of the above models were ranged between 0.54 to 0.79 per cent, 149 to 398 kg/ha, 4.0 to 10.6 per cent, respectively. The study concluded that the Bayesian model was found to be more reliable followed by LASSO and ENET. In addition, it was found that the Bayesian model could perform better even with limited weather parameters and detention of wind speed, sunshine hours and evaporation data would not affect the model performance. It is concluded that Bayesian model may be a better option for rice yield forecasting in Thanjavur districts of Tamil Nadu.

User Friendly Corn Yield Prediction Model

2008 ◽  
Vol 7 (1) ◽  
pp. 1-6 ◽  
Author(s):  
E. C. A. Runge ◽  
John F. Benci
Keyword(s):  
Prediction Model ◽  
Corn Yield ◽  
Yield Prediction ◽  
User Friendly
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