A Novel Approach for Rice Yield Prediction in Andhra Pradesh

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.

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Application of data mining tools for rice yield prediction on clustered regions of Bangladesh

2014 17th International Conference on Computer and Information Technology (ICCIT) ◽

10.1109/iccitechn.2014.7073081 ◽

2014 ◽

Cited By ~ 2

Author(s):

Mohammad Motiur Rahman ◽

Naheena Haq ◽

Rashedur M Rahman

Keyword(s):

Data Mining ◽

Rice Yield ◽

Yield Prediction ◽

Mining Tools

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Forecasting of Kharif Rice and Jute Yield in North Bengal through Statistical Model

International Journal of Environment and Climate Change ◽

10.9734/ijecc/2021/v11i230362 ◽

2021 ◽

pp. 60-65

Author(s):

Kousik Nandi ◽

Anwesh Rai ◽

Soumen Mondal ◽

Subhendu Bandyopadhyay ◽

Deb Sankar Gupta

Keyword(s):

Statistical Model ◽

Crop Yield ◽

Climate Change Scenario ◽

Rice Yield ◽

State Level ◽

Change Scenario ◽

Yield Prediction ◽

Yield Forecast ◽

Crop Yield Forecasting ◽

North Bengal

Crop yield forecasting under the present climate change scenario needs an effective model and its parameter that how crop respond to the weather variable. A number of weather based models have been developed to estimate the crop yield for the various crops at block, district and state level. Among the different model statistical model is more popular and commonly used. The current study was undertaken to evaluate the performance of statistical model for rice and jute yield forecast of four different district viz. Cooch Behar, Jalpaiguri, Uttar Dinajpurand and Dakhin Dinajpur. Among the four districts Cooch Behar district found superior for kharif rice yield prediction (1.46% error with RMSE 177.68 kg/ha) whereas in case of jute crop its performance was the best in the Jalpaiguri district (-0.44% error with RMSE 217.50 kg/ha).

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Machine learning model for rice yield prediction using KNN regression.

10.31220/agrirxiv.2021.00070 ◽

2021 ◽

Author(s):

Akhil Wilson ◽

Raji Sukumar ◽

N. Hemalatha

Keyword(s):

Machine Learning ◽

Rice Yield ◽

Support Vector ◽

Yield Prediction ◽

Challenging Problem ◽

Random Forest Regression ◽

Machine Learning Model ◽

Smart Farming ◽

Regression Algorithms ◽

The One

Abstract The prediction of agriculture yield is the one of the challenging problem in smart farming, we have predicted the yield of rice in the state of Kerala, India with the help of Machine Learning by considering the soil properties, micro climatic condition and area of the rice. Here we have used Decision Tree Regression, Random Forest Regression, Linear Regression, K Nearest Neighbour Regression, Xgboost Regression and Support Vector Regression algorithms in order to predict the rice yield. From the experiments we got KNN regression to be the best with 98.77% accuracy.

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

International Journal of Value Chain Management ◽

10.1504/ijvcm.2021.118289 ◽

2021 ◽

Vol 12 (3) ◽

pp. 221

Author(s):

Sumanya Ngandee ◽

Attaphongse Taparugssanagorn ◽

Chutiporn Anutariya ◽

John K.M. Kuwornu

Keyword(s):

Decision Making ◽

Big Data ◽

Supply Chain ◽

Data Analytics ◽

Prediction Models ◽

Rice Yield ◽

Big Data Analytics ◽

Yield Prediction

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Site-specific management zones delineation and Yield prediction for rice based cropping system using on-farm data sets in Tolima (Colombia)

10.5194/egusphere-egu2020-2466 ◽

2020 ◽

Author(s):

Sofiane Ouazaa ◽

Oscar Barrero ◽

Yeison Mauricio Quevedo Amaya ◽

Nesrine Chaali ◽

Omar Montenegro Ramos

Keyword(s):

Machine Learning ◽

Chlorophyll Content ◽

Rice Yield ◽

Cropping System ◽

Fuzzy Cluster ◽

Sampling Point ◽

Yield Prediction ◽

Management Zones ◽

On Farm ◽

Relative Chlorophyll Content

In the valley of the Alto Magdalena, Colombia, intensive agriculture and inefficient soil and water management techniques have generated a within field yield spatial variability, which have increased the production costs for the rice-based cropping system (rice, cotton and maize crops rotation field). Crop yield variations depend on the interaction between climate, soil, topography and management, and it is strongly influenced by the spatial and temporal availabilities of water and nutrients in the soil during the crop growth season. Understanding why the yield in certain portions of a field has a high variability is of paramount importance both from an economic and an environmental point of view, as it is through the better management of these areas that we can improve yields or reduce input costs and environmental impact.&#160;The aim of this study was 1) to predict rice yield using on farm data set and machine learning and 2) to compare delimited management zones (MZ) for rice-based cropping system with physiological parameters and within field variation yield.A 72 sampling points spatially distributed were defined in a 5 hectares plot at the research center Nataima, Agrosavia. For each sampling point, physical and chemical properties, biomass and relative chlorophyll content were determined at different vegetative stages. A multispectral camera mounted to an Unmanned Aerial Vehicle (UAV) was used to acquire multispectral images over the rice canopy in order to estimate vegetation indices. Five nonlinear models and two multilinear algorithms were employed to estimate rice yield. The fuzzy cluster analysis algorithm was used to classify soil data into two to six MZ. The appropriate number of MZ was determined according to the results of a fuzziness performance index and normalized classification entropy.Results of the rice yield prediction model showed that the best performance was obtained by K-Nearest Neighbors (KNN) regression algorithm with an average absolute error of 10.74%. Nonetheless, the performance of the other algorithms was acceptable except the Multiple Linear regression (MLR). The MLR showed the highest RMSE with 2712.26 kg.ha-1 in the testing dataset, while KNN regression was the best with 1029.69 kg.ha-1. These findings show the importance of machine learning could have for supporting decisions in agriculture processes management.The cluster analyses revealed that two zones was the optimal number of classes based on different criteria. Delineated zones were evaluated and revealed significant differences (p&#8804;0.05) in sand, apparent density, total porosity, pH, organic matter, phosphorus, calcium, magnesium, iron, zinc, cover and boron. The relative chlorophyll content of cotton and maize crops showed a similar spatial distribution pattern to delimited MZ. The results demonstrate the ability of the proposed procedure to delineate a farmer&#8217;s field into zones based on spatially varying soil and crop properties that should be considered for irrigation and fertilization management.

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