Wheat yield forecasting using fuzzy logic

ABSTRACT Fuzzy logic can simulate wheat productivity by assisting crop predictability. The objective of the study is the use of fuzzy logic to simulate wheat yield in the conditions of nitrogen use, together with the effects of air temperature and rainfall, in the main cereal succession systems in Southern Brazil. The study was conducted in the years 2014, 2015 and 2016, in Augusto Pestana, RS, Brazil. The experimental design was a randomized block design with four repetitions in a 4 x 3 factorial scheme for N-fertilizer doses (0, 30, 60, 120 kg ha-1) and nutrient supply forms [100% in phenological stage V3 (third expanded leaf); (70%/30%) in the phenological stage V3/V6 (third and sixth expanded leaf) and; fractionated (70%/30%) at the phenological stage V3/E (third expanded leaf and beginning of grain filling)], respectively, in the soybean/wheat and corn/wheat systems. The pertinence functions and the linguistic values established for the input and output variables are adequate for the use of fuzzy logic. Fuzzy logic simulates wheat grain yield efficiently in the conditions of nitrogen use with air temperature and rainfall in crop systems.

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Winter Wheat Yield Forecasting: a Comparative Analysis of Results of Regression and Biophysical Models

Journal of Automation and Information Sciences ◽

10.1615/jautomatinfscien.v45.i6.70 ◽

2013 ◽

Vol 45 (6) ◽

pp. 68-81 ◽

Cited By ~ 27

Author(s):

Felix Kogan ◽

Nataliya N. Kussul ◽

Tatyana I. Adamenko ◽

Sergey V. Skakun ◽

Alexey N. Kravchenko ◽

...

Keyword(s):

Comparative Analysis ◽

Winter Wheat ◽

Wheat Yield ◽

Biophysical Models ◽

Winter Wheat Yield ◽

Analysis Of Results ◽

Yield Forecasting

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Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-39-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 39-44 ◽

Cited By ~ 25

Author(s):

A. Kolotii ◽

N. Kussul ◽

A. Shelestov ◽

S. Skakun ◽

B. Yailymov ◽

...

Keyword(s):

Winter Wheat ◽

Crop Yield ◽

Wheat Yield ◽

Biophysical Model ◽

Wheat Crop ◽

Biophysical Parameters ◽

Model Based ◽

Winter Wheat Crop ◽

Yield Forecasting ◽

Crop Yield Forecasting

Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

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Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models

Mathematical Problems in Engineering ◽

10.1155/2014/857865 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 10

Author(s):

William W. Guo ◽

Heru Xue

Keyword(s):

Neural Network ◽

Neural Networks ◽

Crop Yield ◽

Wheat Yield ◽

Network Models ◽

High Accuracy ◽

Neural Network Models ◽

Yield Forecasting ◽

Crop Forecasting ◽

Crop Yield Forecasting

Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature has shown that incorporating statistics and artificial neural networks can produce highly satisfactory forecasting of wheat yield. However, no comparison has been made between the outcomes from the spatial neural network model and commonly used temporal neural network models in crop forecasting. This paper presents the latest research outcomes from using both the spatial and temporal neural network models in crop forecasting. Our simulation shows that the spatial NN model is able to predict the wheat yield with respect to a given plantation area with a high accuracy compared with the temporal NARNN and NARXNN models. However, the high accuracy of the spatial NN model in crop yield forecasting is limited to the forecasting of crop yield only within normal ranges. Users must be cautious when using either NARNN or NARXNN for crop yield forecasting due to their inconsistency between the results of training and forecasting.

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