A parameter based ANFIS model for crop yield prediction

Shastry, Aditya; Sanjay, H. A.; Hegde, Mithra N

doi:10.1109/iadcc.2015.7154708

Cited by 37 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 3 shows the best values obtained through both configurations. When compared this best values obtained to fuzzy models and ANFIS; MLR and ANN; and SVMs [1,25,26] the used methods (combined with an optimization technique) show similar or more accurate outcomes.…”

Section: Generic Quasi-newton Gradient Algorithmmentioning

confidence: 92%

“…To predict any natural event requires a logical analysis on the occurrence frequency and the nature of the event itself [1]. Therefore, to apply any process aimed to develop a prediction model, researchers must take advantage of those methods that handle uncertainty better than the traditional ones do [2]; this, allow them to create models that would improve the outcomes (depending on the context and goals establishment) and the model's significance and accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictions on wheat crop yielding through fuzzy set theory and optimization techniques

2020

View full text Add to dashboard Cite

Agricultural field's production is commonly measured through the performance of the crops in terms of sow amount, climatology, and the type of crop, among other. Therefore, prediction on the performance of the crops can aid cultivators to make better informed decisions and help the agricultural field. This research work presents a prediction on wheat crop using the fuzzy set theory and the use of optimization techniques, in both; traditional methods and evolutionary meta-heuristics. The performance prediction in this research has its core on the following parameters: biomass, solar radiation, rainfall, and infield's water extractions. Besides, the needed standards and the efficiency index (EFI) used come from already developed models; such standards include: the root-mean-square error (RMSE), the standard deviation, and the precision percentage. The application of a genetic algorithm on a Takagi-Sugeno system requires and highly precise prediction on wheat cropping; being, 0.005216 the error estimation, and 99.928 the performance percentage.

show abstract

Section: Generic Quasi-newton Gradient Algorithmmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Predictions on wheat crop yielding through fuzzy set theory and optimization techniques

2020

View full text Add to dashboard Cite

show abstract

“…The approach used ANN for prediction of yield. In [8] authors proposed three model for yield prediction. Fuzzy logic (FL), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) are used.…”

Section: Related Workmentioning

confidence: 99%

Yield Forecasting for Indian Crops with Ensemble Model

Lokesh¹,

Senthil²

2019

IJRTE

View full text Add to dashboard Cite

In fast developing economies like India, there is fast depletion of agricultural lands and many people migrate to cities leaving agriculture. In these conditions effective yield prediction of crops is a must for planning food policies and securing food for the people. Import only at time of huge price rise is not a solution and predictive import of crops based on yield predicted is a way to keep food inflation under control. Towards this end, an Ensemble model for prediction of yield of India Rabbi Crops is proposed in this work. An Ensemble machine learning model is built based on past histories and macro climatic and monsoon conditions to forecast the yield for crops in work.

show abstract

“…Difficulty of crop yield estimation using k-means clustering and feature extraction and classification procedure generating better salaries for farmers. Bunching is the procedure of combination the information into classes so that substances within a collection have high resemblance in arrangement to each other but are extremely different to matters in option collections [2], [3]. A gathering of data items can be preserved collectively throughout the time that is grouped and can be looked as data compression process.…”

Section: Introductionmentioning

confidence: 99%

Crop yield prediction using data mining: an efficient data modeling approach

Geetika¹,

Bajaj²

2018

IJET

View full text Add to dashboard Cite

Motivation for this research comes from the agricultural point of view that there is a lot of work for the farmers to be done manually. So this research deals with the automatic classification approach which provides help to the farmers and decrease their efforts. With the change of climate in the world, the mainstreams of the crops in the agricultural field are severely affected in performance in previous decades. The crop prediction yielding should be healthy to help the farmers for taking suitable procedures for selling and loading/storage. Such estimations on the yielding of the crops will also assist the industries based on the farming and production of the crops for development of the logistics of their commercials. Numerous approaches of estimating and crop yielding have been established in the previous times with variable success rates. This paper deals with the prediction of the wheat crop yielding using data mining approaches. The paper deals with the clustering approach using k-means clustering and feature extraction using independent component analysis and the classification is done using linear discriminant analysis and the simulation is done using MATLAB environment. From the result and discussions it can be noticed that our proposed approach is able to achieve less error rate probabilities and high signal to noise ratio. The proposed approach is able to achieve mean square error rate of 0.5 and peak signal to noise ratio of 33.18 db.

show abstract

A parameter based ANFIS model for crop yield prediction

Cited by 37 publications

References 3 publications

Predictions on wheat crop yielding through fuzzy set theory and optimization techniques

Predictions on wheat crop yielding through fuzzy set theory and optimization techniques

Yield Forecasting for Indian Crops with Ensemble Model

Crop yield prediction using data mining: an efficient data modeling approach

Contact Info

Product

Resources

About