Crop yield forecasting using fuzzy logic and regression model

Garg, Bindu; Aggarwal, Shubham; Sokhal, Jatin

doi:10.1016/j.compeleceng.2017.11.015

Cited by 40 publications

(6 citation statements)

References 8 publications

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“…[23,24,25,26]. Besides, novel regression approaches are still being developed and introduced, e.g., an interaction regression model, fuzzy regression, which were proved to be quite reliable and accurate in performing the task of yield analysis [27,28]. At this point, right choice of the approach applied to yielding data analysis became crucial, and the focus of researchers should also be changed to studying the best statistical method for yield prediction in terms of fitting quality and prediction accuracy [29,30].…”

Section: Discussionmentioning

confidence: 99%

Choosing the Best Model for Crop Yield Prediction by the Means of Regression Analysis on the Example of “Crop Yield – Water Use” Simulations Through Best Subsets Approach

Lykhovyd¹,

Вожегова²,

Piliarska³

et al. 2023

Preprint

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Crop yield prediction is relevant subject of current agricultural science. There are various mathematical approaches to crop yield prediction, and regression analysis, notwithstanding the fact that it is somewhat outdated, is still one of the most used ones in this purpose. The quality of predictive model is of great importance, and it is strongly dependent on the rational choice of the target function. The goal of this study is to find out the best regression model for winter wheat, soybeans, and grain corn yield prediction depending on the crops’ water use. The data on true crops’ yields and water use were collected within 1970-2020 at the experimental fields of the Institute of Climate-Smart Agriculture, Kherson region, Ukraine. In total, 145 data pairs were processed by the best subsets regression to find out the best model in terms of fitting quality (assessed by the Pearson’s coefficient of correlation), and prediction accuracy (assessed by the values of the minimum and maximum absolute errors and mean average percentage error). As a result, it was established that the best fitting quality for all the studied crops is attributed to cubic function, while the best accuracy is recorded for hyperbolic (reverse) function in soybeans (mean absolute percentage error is 12.27%), quadratic and hyperbolic functions in winter wheat (mean absolute percentage error is 20.54%), and cubic function in grain corn (mean absolute percentage error is 14.92%). To sum up the results of the study, it is recommended to apply cubic regression function for modeling crops’ yields in agricultural studies.

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Section: Discussionmentioning

confidence: 99%

Choosing the Best Model for Crop Yield Prediction by the Means of Regression Analysis on the Example of “Crop Yield – Water Use” Simulations Through Best Subsets Approach

Lykhovyd¹,

Вожегова²,

Piliarska³

et al. 2023

Preprint

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“…Garg et al [16] used historical wheat yield data from the Odisha University of Agriculture and Technology in India to forecast wheat yield from mathematical equations of degree from 1 to 4. They classified the wheat yield variable into 7 fuzzy intervals (very poor yield, poor yield, not so good yield, average yield, good yield, very good yield, excellent yield).…”

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

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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.

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“…A fuzzy logic and regression model [11] was introduced for crop yield forecasting. This model centered on the estimation of data values based on a large variety of fuzzy logic equations, relying on relationships between second and third degrees.…”

Section: Saranya S Sathappanmentioning

confidence: 99%

“…In addition, the gradient of the final prediction is computed with respect to the backpropagated derivatives of a predicator at every depth weighted by . The depth of the network is decided based on the following (11). (11) In (11), is the expected loss of the network at time and is the network depth.…”

Section: Multi-model Ensemble-depth Adaptive Deep Neural Networkmentioning

confidence: 99%

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Multi-Model Ensemble Depth Adaptive Deep Neural Network for Crop Yield Prediction

Saranaya*,

Sathappan

2020

IJRTE

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Accurate prediction of crop yield enables critical tasks such as identifying the optimum crop profile for planting, assigning government resources and decision-making on imports and exports in more commercialized systems. In past few years, Machine Learning (ML) techniques have been widely used for crop yield prediction. Deep Neural Network (DNN) was introduced for crop yield. The crop yield prediction accuracy based on DNN was further improved by Multi-Model DNN (MME-DNN). It predicted the crop yield by modeling climatic, weather and soil parameters through statistical model and DNN. The MME-DNN is not scalable when new data appears consecutively in a stream form. In order to solve this problem, an Online Learning (OL) is introduced for crop yield prediction. In OL, DNN is learned in an online setting which optimizes the objective function regarding shallow model. But, a fixed depth of the network is used in ODL and it cannot be changed during the training process. So, Multi-Model Ensemble Depth Adaptive Deep Neural Network (MME-DADNN) is proposed in this paper to adaptively decide the depth of the network for crop yield prediction. A training scheme for OL is designed through a hedge back propagation. It automatically decides the depth of the DNN using Online Gradient Descent (OGD) in an online manner. Also, a smoothing parameter is introduced in OL to set a minimum weight for every depth of DNN and it also contributes a balance between exploitation and exploration. The crop yield is predicted from the soil, weather and climate parameters and their variation over four years by applying the MME-DADNN. Thus, by adaptively changing the depth of the DNN the performance of crop yield prediction is enhanced.

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Crop yield forecasting using fuzzy logic and regression model

Cited by 40 publications

References 8 publications

Choosing the Best Model for Crop Yield Prediction by the Means of Regression Analysis on the Example of “Crop Yield – Water Use” Simulations Through Best Subsets Approach

Choosing the Best Model for Crop Yield Prediction by the Means of Regression Analysis on the Example of “Crop Yield – Water Use” Simulations Through Best Subsets Approach

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

Multi-Model Ensemble Depth Adaptive Deep Neural Network for Crop Yield Prediction

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