Weather Based Yield Prediction and PDI Model for Grape Production Quality Forecast in Tamil Nadu using Mathematical Modelling

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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Crop Yield Prediction using XG Boost Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9547.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 3516-3520

Keyword(s):

Crop Yield ◽

Tamil Nadu ◽

Crop Yields ◽

Absolute Error ◽

Climatic Conditions ◽

Support Vector ◽

Yield Prediction ◽

Indian States ◽

South Indian ◽

Proposed Model

The main objective of this research is to predict crop yields based on cultivation area, Rainfall and maximum and minimum temperature data. It will help our Indian farmers to predict crop yielding according to the environment conditions. Nowadays, Machine learning based crop yield prediction is very popular than the traditional models because of its accuracy. In this paper, linear regression, Support Vector Regression, Decision Tree and Random forest is compared with XG Boost algorithm. The above mentioned algorithms are compared based on R2 , Minimum Square Error and Minimum Absolute Error. The dataset is prepared from the data.gov.in site for the year from 2000 to 2014. The data for 4 south Indian states Andhra Pradesh, Karnataka, Tamil Nadu and Kerala data alone is taken since all these states has same climatic conditions. The proposed model in this paper based on XG Boost is showing much better results than other models. In XG Boost R2 is 0.9391 which is the best when compared with other models.

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Gooseberry (Ribes uva-crispa L.) yield prediction on the basis of the mathematical modelling depending on the sphaerotheca development

Horticulture: Interdepartment Subdject Scientific Collection ◽

10.35205/0558-1125-2019-74-83-90 ◽

2019 ◽

pp. 83-90

Author(s):

O.G. Polgorodnik ◽

◽

O.F. Denysyuk ◽

Keyword(s):

Mathematical Modelling ◽

Yield Prediction

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Soil Data Analysis and Crop Yield Prediction in Data Mining using R-Tool

Indian Journal of Agricultural Research ◽

10.18805/ijare.a-5449 ◽

2020 ◽

Author(s):

K. Samundeeswari ◽

K. Srinivasan

Keyword(s):

Data Mining ◽

Decision Tree ◽

Crop Yield ◽

Tamil Nadu ◽

Crop Production ◽

Experimental Result ◽

Yield Prediction ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Data Mining Techniques

Background: Crop yield prediction is an important issue for the proper selection of crop for sowing. Earlier prediction of crop is done by the farmer’s experience on a particular type of field and crop. Predicting the crop is done by the farmer’s experience based on the factors like soil types, climatic condition, seasons and weather, rainfall and irrigation facilities. Methods: Data mining techniques is the better choice for predicting the crop. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year’s crop production. This research proposes and implements a system to predict crop yield from soil data. This is achieved by applying Decision Tree Algorithm on agricultural data. The main aim of this research is to pinpoint the accuracy of Decision Tree Algorithm and C 5.0 algorithm which is used to predict the crop yield. Result: This paper presents a brief analysis of Crop yield prediction using data mining technique based decision tree algorithm and C5.0 algorithm for the selected region (Krishnagiri) district of Tamil Nadu in India. The experimental result shows that the proposed work efficiently to determine the accuracy of decision tree algorithm and also to predict the crop yield production using R- Tool.

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Rice Yield Prediction for Cauvery Delta Zone of Tamil Nadu using Weather Based Statistical Model

International Journal of Current Microbiology and Applied Sciences ◽

10.20546/ijcmas.2020.906.052 ◽

2020 ◽

Vol 9 (6) ◽

pp. 400-406

Author(s):

G. Senthil Kumar ◽

K. Subrahmaniyan

Keyword(s):

Statistical Model ◽

Tamil Nadu ◽

Rice Yield ◽

Yield Prediction ◽

Cauvery Delta

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Selection of Important Features for Optimizing Crop Yield Prediction

International Journal of Agricultural and Environmental Information Systems ◽

10.4018/ijaeis.2019070104 ◽

2019 ◽

Vol 10 (3) ◽

pp. 54-71

Author(s):

Maya Gopal P S ◽

Bhargavi R

Keyword(s):

Feature Selection ◽

Crop Yield ◽

Tamil Nadu ◽

Feature Subset ◽

Yield Prediction ◽

Multilinear Regression ◽

Secondary Sources ◽

Backward Elimination ◽

Tamil Nadu State ◽

Selection Algorithms

In agriculture, crop yield prediction is critical. Crop yield depends on various features including geographic, climate and biological. This research article discusses five Feature Selection (FS) algorithms namely Sequential Forward FS, Sequential Backward Elimination FS, Correlation based FS, Random Forest Variable Importance and the Variance Inflation Factor algorithm for feature selection. Data used for the analysis was drawn from secondary sources of the Tamil Nadu state Agriculture Department for a period of 30 years. 75% of data was used for training and 25% data was used for testing. The performance of the feature selection algorithms are evaluated by Multiple Linear Regression. RMSE, MAE, R and RRMSE metrics are calculated for the feature selection algorithms. The adjusted R2 was used to find the optimum feature subset. Also, the time complexity of the algorithms was considered for the computation. The selected features are applied to Multilinear regression, Artificial Neural Network and M5Prime. MLR gives 85% of accuracy by using the features which are selected by SFFS algorithm.

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Design and Implementation of Various Regression Models for Yield Prediction

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2766.039520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 1280-1284

Keyword(s):

Random Forest ◽

Predictive Models ◽

Regression Models ◽

Tamil Nadu ◽

Research Work ◽

Uttar Pradesh ◽

Yield Prediction ◽

Indian States ◽

Design And Implementation ◽

Yield Value

Agriculture is the backbone of India. In order to support farmers in India, this research is focused on the design of various predictive models that are used to predict the yield value for a specific crop in Indian states. This research work considers Rice, Wheat, and Bajra crops in Tamil-Nadu, Rajasthan, Uttar Pradesh states respectively. The various regression models such as Linear, Multiple, C4.5 and Random Forest are considered in this work. R squared value is used to evaluate the performance of the regression models. The result of this work shows that Random Forest model is better in performance.

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