Machine learning-based canola yield prediction for site-specific nitrogen recommendations

2021 ◽  
Author(s):  
Guoqi Wen ◽  
Bao-Luo Ma ◽  
Anne Vanasse ◽  
Claude D. Caldwell ◽  
Hugh J. Earl ◽  
...  
Keyword(s):  
Machine Learning ◽  
Yield Prediction ◽  
Site Specific

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 UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 202
Author(s):  
Zhen Chen ◽  
Qian Cheng ◽  
Fuyi Duan ◽  
Xiuqiao Huang ◽  
Honggang Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Winter Wheat ◽  
Prediction Model ◽  
Grain Filling ◽  
Yield Prediction ◽  
Spectral Indices ◽  
Learner Model ◽  
Ensemble Machine Learning ◽  
Base Learner

Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

scholarly journals Are we there yet? A machine learning architecture to predict organotropic metastases

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Michael Skaro ◽  
Marcus Hill ◽  
Yi Zhou ◽  
Shannon Quinn ◽  
Melissa B. Davis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Metastasis ◽  
Driving Forces ◽  
The Cancer Genome Atlas ◽  
Tumor Type ◽  
Metastatic Progression ◽  
Statistical Machine Learning ◽  
Primary Tumors ◽  
Site Specific ◽  
Tissue Specific

Abstract Background & Aims Cancer metastasis into distant organs is an evolutionarily selective process. A better understanding of the driving forces endowing proliferative plasticity of tumor seeds in distant soils is required to develop and adapt better treatment systems for this lethal stage of the disease. To this end, we aimed to utilize transcript expression profiling features to predict the site-specific metastases of primary tumors and second, to identify the determinants of tissue specific progression. Methods We used statistical machine learning for transcript feature selection to optimize classification and built tree-based classifiers to predict tissue specific sites of metastatic progression. Results We developed a novel machine learning architecture that analyzes 33 types of RNA transcriptome profiles from The Cancer Genome Atlas (TCGA) database. Our classifier identifies the tumor type, derives synthetic instances of primary tumors metastasizing to distant organs and classifies the site-specific metastases in 16 types of cancers metastasizing to 12 locations. Conclusions We have demonstrated that site specific metastatic progression is predictable using transcriptomic profiling data from primary tumors and that the overrepresented biological processes in tumors metastasizing to congruent distant loci are highly overlapping. These results indicate site-specific progression was organotropic and core features of biological signaling pathways are identifiable that may describe proliferative plasticity in distant soils.

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 Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

2020 ◽  
Vol 15 (6) ◽  
pp. 064005
Author(s):  
Yanghui Kang ◽  
Mutlu Ozdogan ◽  
Xiaojin Zhu ◽  
Zhiwei Ye ◽  
Christopher Hain ◽  
...  
Keyword(s):  
Machine Learning ◽  
Environmental Variables ◽  
Learning Algorithms ◽  
Maize Yield ◽  
Comparative Assessment ◽  
Machine Learning Algorithms ◽  
Yield Prediction ◽  
The Us ◽  
Us Midwest

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.

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
Sign in / Sign up

Export Citation Format

Share Document