Estimation of grain yield with remote sensing method
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In this paper,we make a study on the grain-yield forecasting model based on time series Normalized Difference Vegetation Index(NDVI) derived from NOAA-AVHRR.We build the grain yield-forecasting model by three different modeling patterns,including Parameter-yield mode,Decomposed yield mode and Difference-yield mode.By comparing the significances of three models,we pick out the final forecasting model for different province.The models were used to predict the autumn grain yield of different province in China and the differences are-4.9% to 11.59% by comparing with the yield data of the National Statistic Bureau,the R Square is 0.947.Keywords:
Statistic
Mode (computer interface)
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The spatial variability of cereal yield is analyzed based on the practices of precision agriculture. Some error data were eliminated using the Interval Estimation and the original cereal yield data sets were modified and transferred using the Moving Weighted Average Method by plotting out the field. The more precise Yield Spatial Distribution Map was obtained through comparing the Yield Spatial Distribution Map under various Grid Size.
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Empirical modelling
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A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.
Growing season
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The use of satellite remote sensing could effectively predict maize yield. However, many statistical prediction models using remote sensing data cannot extend to the regional scale without considering the regional climate. This paper first introduced the hierarchical linear modeling (HLM) method to solve maize-yield prediction problems over years and regions. The normalized difference vegetation index (NDVI), calculated by the spectrum of the Landsat 8 operational land imager (OLI), and meteorological data were introduced as input parameters in the maize-yield prediction model proposed in this paper. We built models using 100 samples from 10 areas, and used 101 other samples from 34 areas to evaluate the model’s performance in Jilin province. HLM provided higher accuracy with an adjusted determination coefficient equal to 0.75, root mean square error (RMSEV) equal to 0.94 t/ha, and normalized RMSEV equal to 9.79%. Results showed that the HLM approach outperformed linear regression (LR) and multiple LR (MLR) methods. The HLM method based on the Landsat 8 OLI NDVI and meteorological data could flexibly adjust in different regional climatic conditions. They had higher spatiotemporal expansibility than that of widely used yield estimation models (e.g., LR and MLR). This is helpful for the accurate management of maize fields.
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Precision Agriculture
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Traditional plant breeding based on selection for grain yield is time-consuming and costly; therefore, new innovative methods are in high demand to reduce costs and accelerate genetic gains. Remote sensing-based platforms such as unmanned aerial vehicles (UAV) show promise to predict different traits including grain yield. Attention is currently being devoted to machine learning methods in order to extract the most meaningful information from the massive amounts of data generated by UAV images. These methods have shown a promising capability to come up with nonlinearity and explore patterns beyond the human ability. This study investigates the application of two different machine learning based regressor methods to predict wheat grain yield using extracted vegetation indices from UAV images. The goal of the study was to investigate the strength of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for grain yield prediction and compare the results with LASSO regressor with an internal feature selector. Models were tested on grain yield data from 600 plots of spring wheat planted in South-Eastern Norway in 2018. Five spectral bands along with three different vegetation indices; the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and MERIS Terrestrial Chlorophyll Index (MTCI) were extracted from multispectral images at three dates between heading and maturity of the plants. These features for each field trial plot at each date were used as input data for the SVR model. The best model hyperparameters were estimated using grid search. Based on feature selection results from both methods, NDVI showed the highest prediction ability for grain yield at all dates and its explanatory power increased toward maturity, while adding MTCI and EVI at earlier stages of grain filling improved model performance. Combined models based on all indices and dates explained up to 90% of the variation in grain yield on the test set. Inclusion of individual bands added collinearity to the models and did not improve the predictions. Although both regression methods showed a good capability for grain yield prediction, LASSO regressor proved to be more affordable and economical in terms of time.
Lasso
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The use of yield prediction maps is an important tool for the delineation of within-field management zones. Vegetation indices based on crop reflectance are of potential use in the attainment of this objective. There are different types of vegetation indices based on crop reflectance, the most commonly used of which is the NDVI (normalized difference vegetation index). NDVI values are reported to have good correlation with several vegetation parameters including the ability to predict yield. The field research was conducted in two commercial farms of processing tomato crop, Cantillana and Enviciados. An NDVI prediction map developed through ordinary kriging technique was used for guided sampling of processing tomato yield. Yield was studied and related with NDVI, and finally a prediction map of crop yield for the entire plot was generated using two geostatistical methodologies (ordinary and regression kriging). Finally, a comparison was made between the yield obtained at validation points and the yield values according to the prediction maps. The most precise yield maps were obtained with the regression kriging methodology with RRMSE values of 14% and 17% in Cantillana and Enviciados, respectively, using the NDVI as predictor. The coefficient of correlation between NDVI and yield was correlated in the point samples taken in the two locations, with values of 0.71 and 0.67 in Cantillana and Enviciados, respectively. The results suggest that the use of a massive sampling parameter such as NDVI is a good indicator of the distribution of within-field yield variation.
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Assimilation (phonology)
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Grain Yield Estimating for Hubei Province Using Remote Sensing Data Take Semilate Rice as an Example
Crop yield estimation models using remote sensing data were developed to forecast crop yield for Hubei province. Firstly, simulated counties were chosen using productivity zoning method, and the fluctuated yield was obtained by analyzing history trend. Secondly, the correlation coefficient between fluctuated yield and remote sensing index was calculated. Then, the index with the highest correlation coefficient was selected as key factor to build simple linear regression models to estimate the crop yield. Finally, the error analysis was processed by comparing the actual crop yield from statistic data with that from modeling results. The results indicate that the precision error ranges from -14.38% to 11.31% compared with statistics data, and the coefficient of determination R 2 is 0.872.The results calculated by this method meet the accuracy requirements for the crop yield estimation in most part of Hubei province, and can support the decision making of the government and corporations.
Statistic
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Background and objectives: Food security has been most important concern of the mankind on the earth. On the other hand, agricultural productions have always been face by risk probability in the case of weather and changes in international markets, however this risk probability never undeleted completely, but could be minimized that by pre-harvesting yield estimation. In this study, different methods of vegetation maps provision were involved toprovide a suitable pre-harvesting map for wheat yield. Materials and methods: For comparison of remote sensing and geostatistics-based methods capabilities in wheat yield predication in wheat fields, a survey was conducted in 2011-12 growing season. 101 plant samples were taken from 2500 hectare wheat fields in tillering, booting, seed filling and maturity stages (three times for leaf area index and dry weight and one sampling for yield) and related measurements were done. Ordinary, Universal and Disjunctive Kriging methods were applied and semivariograms were provided, then proper models were fitted. Different statistical indices were used to test the accuracy. Also, three +ETM images acquired by Landsat satellite were used which were matched by sampling dates. Four images for previous years also were used as needed. Eight plant indices were provided from aforementioned images and were compared with plant variables which were recorded or measured simultaneously, then related relations were determined and maps were provided. By fitting the logistic model between yield and plant variables, yield prediction maps were evaluated by remote sensing and, the obtained maps were compared using different statistical indices. Results: Evaluation results of interpolation methods revealed that spherical, exponential, Gaussian and circular models were superior models in this study. Also, results on the survey indices derived from satellite images showed a significant relationship between the variables and indices derived from satellite images in the end of tillering stage. Assessment of generated yield maps, demonstrated pronounced superiority of remote sensing techniques compared with geostatistical-based analysis methods. The results demonstrated the capability of satellite images in regional scale to predict wheat yield (with 715 kg.ha-1 biass in tillering stage).Conclusion: According to the acceptable accuracy of the remote sensing compared with the Geostatistics- based method along with ease of and low cost of this method, use of the remote sensing and satellite images – derived vegetation indices could be a new horizon in regional yield estimation. Since satellite images provide an actual representation from the crop status, could involve significantly to the growth modeling.
Geostatistics
Hectare
Interpolation
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