Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI

Aghighi, Hossein; Azadbakht, Mohsen; Ashourloo, Davoud; Shahrabi, Hamid Salehi; Radiom, Soheil

doi:10.1109/jstars.2018.2823361

Cited by 144 publications

(70 citation statements)

References 80 publications

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“…decision support system for agrotechnology transfer (DSSAT) [15], agricultural production systems sIMulator (APSIM) [16], model to capture the crop-weather relationship over a large area (MCWLA) [8,17], and world food studies (WOFOST) [18]. Although these models can simulate crop yields with higher accuracy, lots of inputs (e.g., climatic variables, fertilizers, irrigations, soil, and hydrological features) are required to run a model, which are time-consuming, cost-intensive, and difficult to popularize into a larger region or a developing country [14,19,20]. Climate variables are the primary inputs for the above two approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Although previous studies have greatly improved yield prediction accuracy from spatial and temporal domains, they only focused on partial regions due to the complicated data process [20,37]. Crop yield prediction at a larger-area scale generally requires a large amount of data and complex data processing, suggesting high costs for acquiring and processing large data sets.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

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Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

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“…The repeated five-fold cross-validation (CV) technique [80] was used to separate the targeted dataset into training and testing groups. In each iteration, the targeted dataset was first shuffled randomly and then split into five equal-sized subsets, of which four subsets of data were used for training the model and the remaining one subset was used for testing the model.…”

Section: Model Construction and Validationmentioning

confidence: 99%

The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA

et al. 2020

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Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data.

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“…Common difficulty that is present among the Indian farmers is that they don't select or opt for the proper crop based on their soil fertilization and their climatic factors. Hossein Aghighi, Hamid Salehi Shahrabi, Davoud Ashourloo, Mohsen Azadbakht, and Soheil Radiom [7] specifies about the machine learning which is used here for crop monitoring and also for calculation of yield of a crop. Main algorithm used are Gaussian process regression(GPR), NVDI, Support vector and Random forest regression.…”

Section: Literature Surveymentioning

confidence: 99%

Determining the Yield of the Crop using Artificial Neural Network Method

Rajan¹,

Nandi²,

Chauhan³

et al. 2019

IJEAT

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The agricultural system is complex and comprehend since it deals with large data that comes from several factors. Lot of techniques and have been used to identify any interactions between factors affecting yields with crop performance. The major objective of this paper is to help us predict the yield of a particular crop before even cultivating it for its production. We are using artificial neural networks for forwarding and implementing a system that will help the farmers in finding their crop yields according to their given data as input in the system and the system will give output based on previous data. The method used in this crop yield system is an artificial neural network and the algorithm used is feed forward and back propagation. Provide the input of data sets and the desired outcome of the system. Compute the error between the actual and desired outcome of the system. Amendment of the weight associated with different inputs and different functions. Compare the errors and the tolerance ratio of the output. Various machine learning techniques have been used in the past for calculating the crop yield using remote data. However, these methods are less useful and effective for predicting the yield of maize and for some other crops, which is cultivated at different times in various fields.The major application of this crop yield system is that it will help us to predict the yield before even cultivating it by studying the previous data collected such as soil fertility, pH level.

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Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI

Cited by 144 publications

References 80 publications

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA

Determining the Yield of the Crop using Artificial Neural Network Method

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