Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil

2020 ◽  
Vol 284 ◽  
pp. 107886 ◽  
Author(s):  
Raí A. Schwalbert ◽  
Telmo Amado ◽  
Geomar Corassa ◽  
Luan Pierre Pott ◽  
P.V.Vara Prasad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Weather Data ◽  
Yield Prediction ◽  
Southern Brazil ◽  
Soybean Yield ◽  
Yield Forecast

scholarly journals An interaction regression model for crop yield prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Javad Ansarifar ◽  
Lizhi Wang ◽  
Sotirios V. Archontoulis
Keyword(s):  
Machine Learning ◽  
Regression Model ◽  
Crop Yield ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Yield Prediction ◽  
Soybean Yield ◽  
Global Food Security ◽  
Management Scenario ◽  
Complex Interactions

AbstractCrop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the power of optimization, machine learning, and agronomic insight, we present a new predictive model (referred to as the interaction regression model) for crop yield prediction, which has three salient properties. First, it achieved a relative root mean square error of 8% or less in three Midwest states (Illinois, Indiana, and Iowa) in the US for both corn and soybean yield prediction, outperforming state-of-the-art machine learning algorithms. Second, it identified about a dozen environment by management interactions for corn and soybean yield, some of which are consistent with conventional agronomic knowledge whereas some others interactions require additional analysis or experiment to prove or disprove. Third, it quantitatively dissected crop yield into contributions from weather, soil, management, and their interactions, allowing agronomists to pinpoint the factors that favorably or unfavorably affect the yield of a given location under a given weather and management scenario. The most significant contribution of the new prediction model is its capability to produce accurate prediction and explainable insights simultaneously. This was achieved by training the algorithm to select features and interactions that are spatially and temporally robust to balance prediction accuracy for the training data and generalizability to the test data.

scholarly journals County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4363 ◽  
Author(s):  
Jie Sun ◽  
Liping Di ◽  
Ziheng Sun ◽  
Yonglin Shen ◽  
Zulong Lai
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Crop Yield ◽  
Google Earth ◽  
Training Data ◽  
Weather Data ◽  
Yield Prediction ◽  
County Level ◽  
Soybean Yield ◽  
Deep Cnn

Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future.

Analysis of agricultural crop yield prediction using statistical techniques of machine learning

2021 ◽  
Author(s):  
Janmejay Pant ◽  
R.P. Pant ◽  
Manoj Kumar Singh ◽  
Devesh Pratap Singh ◽  
Himanshu Pant
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Statistical Techniques ◽  
Yield Prediction ◽  
Agricultural Crop

scholarly journals Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

2019 ◽  
Vol 12 (1) ◽  
pp. 21 ◽  
Author(s):  
Liangliang Zhang ◽  
Zhao Zhang ◽  
Yuchuan Luo ◽  
Juan Cao ◽  
Fulu Tao
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Satellite Data ◽  
Maize Yield ◽  
Environmental Data ◽  
Yield Prediction ◽  
County Level ◽  
Climate Data ◽  
Source Data ◽  
Optical Fluorescence

Maize is an extremely important grain crop, and the demand has increased sharply throughout the world. China contributes nearly one-fifth of the total production alone with its decreasing arable land. Timely and accurate prediction of maize yield in China is critical for ensuring global food security. Previous studies primarily used either visible or near-infrared (NIR) based vegetation indices (VIs), or climate data, or both to predict crop yield. However, other satellite data from different spectral bands have been underutilized, which contain unique information on crop growth and yield. In addition, although a joint application of multi-source data significantly improves crop yield prediction, the combinations of input variables that could achieve the best results have not been well investigated. Here we integrated optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield across four agro-ecological zones (AEZs) in China using a regression-based method (LASSO), two machine learning (ML) methods (RF and XGBoost), and deep learning (DL) network (LSTM). The results showed that combining multi-source data explained more than 75% of yield variation. Satellite data at the silking stage contributed more information than other variables, and solar-induced chlorophyll fluorescence (SIF) had an almost equivalent performance with the enhanced vegetation index (EVI) largely due to the low signal to noise ratio and coarse spatial resolution. The extremely high temperature and vapor pressure deficit during the reproductive period were the most important climate variables affecting maize production in China. Soil properties and management factors contained extra information on crop growth conditions that cannot be fully captured by satellite and climate data. We found that ML and DL approaches definitely outperformed regression-based methods, and ML had more computational efficiency and easier generalizations relative to DL. Our study is an important effort to combine multi-source remote sensed and environmental data for large-scale yield prediction. The proposed methodology provides a paradigm for other crop yield predictions and in other regions.

scholarly journals Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

2021 ◽  
Author(s):  
Amit Kumar Srivast ◽  
Nima Safaei ◽  
Saeed Khaki ◽  
Gina Lopez ◽  
Wenzhi Zeng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Yield Prediction ◽  
Learning Models ◽  
Winter Wheat Yield ◽  
Machine Learning Models

Abstract Crop yield forecasting depends on many interactive factors including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. We propose a convolutional neural network (CNN) which uses the 1-dimentional convolution operation to capture the time dependencies of environmental variables. The proposed CNN, evaluated along with other machine learning models for winter wheat yield prediction in Germany, outperformed all other models tested. To address the seasonality, weekly features were used that explicitly take soil moisture and meteorological events into account. Our results indicated that nonlinear models such as deep learning models and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models and deep neural networks had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. Therefore, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). As such, our study indicates which variables have the most significant effect on winter wheat yield.

scholarly journals Forecasting of Kharif Rice and Jute Yield in North Bengal through Statistical Model

2021 ◽  
pp. 60-65
Author(s):  
Kousik Nandi ◽  
Anwesh Rai ◽  
Soumen Mondal ◽  
Subhendu Bandyopadhyay ◽  
Deb Sankar Gupta
Keyword(s):  
Statistical Model ◽  
Crop Yield ◽  
Climate Change Scenario ◽  
Rice Yield ◽  
State Level ◽  
Change Scenario ◽  
Yield Prediction ◽  
Yield Forecast ◽  
Crop Yield Forecasting ◽  
North Bengal

Crop yield forecasting under the present climate change scenario needs an effective model and its parameter that how crop respond to the weather variable. A number of weather based models have been developed to estimate the crop yield for the various crops at block, district and state level. Among the different model statistical model is more popular and commonly used. The current study was undertaken to evaluate the performance of statistical model for rice and jute yield forecast of four different district viz. Cooch Behar, Jalpaiguri, Uttar Dinajpurand and Dakhin Dinajpur. Among the four districts Cooch Behar district found superior for kharif rice yield prediction (1.46% error with RMSE 177.68 kg/ha) whereas in case of jute crop its performance was the best in the Jalpaiguri district (-0.44% error with RMSE 217.50 kg/ha).

scholarly journals Predicting Maize Yield at the Plot Scale of Different Fertilizer Systems by Multi-Source Data and Machine Learning Methods

2021 ◽  
Vol 13 (18) ◽  
pp. 3760
Author(s):  
Linghua Meng ◽  
Huanjun Liu ◽  
Susan L. Ustin ◽  
Xinle Zhang
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Satellite Data ◽  
Maize Yield ◽  
Environmental Data ◽  
Yield Prediction ◽  
Climate Data ◽  
Multiple Sources ◽  
Adaptive Boosting ◽  
Source Data

Timely and reliable maize yield prediction is essential for the agricultural supply chain and food security. Previous studies using either climate or satellite data or both to build empirical or statistical models have prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, fertilizer information may also improve crop yield prediction, especially in regions with different fertilizer systems, such as cover crop, mineral fertilizer, or compost. Machine learning (ML) has been widely and successfully applied in crop yield prediction. Here, we attempted to predict maize yield from 1994 to 2007 at the plot scale by integrating multi-source data, including monthly climate data, satellite data (i.e., vegetation indices (VIs)), fertilizer data, and soil data to explore the accuracy of different inputs to yield prediction. The results show that incorporating all of the datasets using random forests (RF) and AB (adaptive boosting) can achieve better performances in yield prediction (R2: 0.85~0.98). In addition, the combination of VIs, climate data, and soil data (VCS) can predict maize yield more effectively than other combinations (e.g., combinations of all data and combinations of VIs and soil data). Furthermore, we also found that including different fertilizer systems had different prediction accuracies. This paper aggregates data from multiple sources and distinguishes the effects of different fertilization scenarios on crop yield predictions. In addition, the effects of different data on crop yield were analyzed in this study. Our study provides a paradigm that can be used to improve yield predictions for other crops and is an important effort that combines multi-source remotely sensed and environmental data for maize yield prediction at the plot scale and develops timely and robust methods for maize yield prediction grown under different fertilizing systems.

scholarly journals Knn based Crop and Fertilizer Prediction

2020 ◽  
Vol 9 (4) ◽  
pp. 1453-1456
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Crop Yield ◽  
Accurate Result ◽  
Machine Learning Algorithms ◽  
Yield Prediction ◽  
Prediction Problem ◽  
Environmental Statistics ◽  
Classification And Regression ◽  
Using Data

India has always been active in agriculture, in fact even in this age of industrialization agriculture and agriculturebased industries continue to be a main source of income for a large percentage of the population. Machine learning and data mining have become, in the present day, are very important mediums when it comes to research in the crop yielding domain. Many a times we come across news on the paper about farmers committing suicide because of crop failures and increase in loans. In preventing such situations, crop yield prediction software can play a very important role. This research is an attempt in proposing a method to predict the success of crop for a particular area by using data on amounts and ratios of different components of soil like nitrogen, potassium, phosphorus and environmental statistics on temperature and weather. Various machine learning algorithms are used to get an accurate result. KNN is used for classification and regression prediction problem. It also attempts in providing a precise output on what fertilizers can be used to better the yield. Through this, therefore, farmers will also be able to predict their profits and final revenues.

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