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

Linghua Meng, Huanjun Liu, Susan L. Ustin, Xinle Zhang
2021 Remote Sensing  
prediction at the plot scale and develops timely and robust methods for maize yield prediction grown under different fertilizing systems.  ...  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  ...  Conclusions We predicted maize yield at the plot scale based on multi-source data and multiple machine learning models at the plot scale in RRSAF.  ... 
doi:10.3390/rs13183760 fatcat:sl5pz4rytrhkxga5ddvxblk3ni

Random Forests for Global and Regional Crop Yield Predictions

Jig Han Jeong, Jonathan P. Resop, Nathaniel D. Mueller, David H. Fleisher, Kyungdahm Yun, Ethan E. Butler, Dennis J. Timlin, Kyo-Moon Shim, James S. Gerber, Vangimalla R. Reddy, Soo-Hyung Kim, Jose Luis Gonzalez-Andujar
2016 PLoS ONE  
We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato  ...  Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in  ...  This study was supported by a Cooperative Research Program for Agricultural Science and Technology Devel- Author Contributions  ... 
doi:10.1371/journal.pone.0156571 pmid:27257967 pmcid:PMC4892571 fatcat:33kuo57jtjh3zlmgjxe6uipwau

Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales

Zhu, Sun, Peng, Huang, Li, Zhang, Yang, Liao
2019 Remote Sensing  
We investigated the performances of the multi-source UAV-based 3D point clouds at multi-spatial scales using the traditional multi-variable linear regression model (OLS), random forest (RF), backpropagation  ...  Estimating the crop AGB at a field scale through the use of unmanned aerial vehicles (UAVs) is promising for agronomic application, but the robustness of the methods used for estimation needs to be balanced  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs11222678 fatcat:letr42jvenapllkanl6xq445ey

Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments

Qian Cheng, Honggang Xu, Shuaipeng Fei, Zongpeng Li, Zhen Chen
2022 Agriculture  
For this, multispectral imagery of the field was conducted at different growth stages (jointing, trumpet, silking and flowering) of maize under three water treatments and five fertilizer treatments.  ...  The objective of this study is to evaluate the unmanned aerial vehicle (UAV)-based multispectral imaging to estimate the LAI of maize under different water and fertilizer stress conditions.  ...  Acknowledgments: The authors would like to thank the anonymous reviewers for their kind suggestions and constructive comments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/agriculture12081267 fatcat:xgiiavj7djh7vcknsqb2irjkpa

Crop Yield Estimation of Teff (Eragrostis tef Zuccagni) Using Geospatial Technology and Machine Learning Algorithm in the Central Highlands of Ethiopia

Hailu Shiferaw, Getachew Tesfaye, Habtamu Sewnet, Leulseged Tamene
2022 Sustainable Agriculture Research  
For this, ground truth sample plots were used for nine zones, and geospatial technology and machine learning were applied for upscaling to the whole study area’s scale.  ...  This study is conducted at nine Teff-dominated zones of the country to examine whether geospatial technology can serve to estimate the productivity of crop yield.  ...  Acknowledgment The authors acknowledge the initiations and encouragement of coalition of the willing (CoW), particularly Dr.  ... 
doi:10.5539/sar.v11n1p34 fatcat:jv2azqkoong7xesysiwn77k6se

The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing

Bin Yang, Wanxue Zhu, Ehsan Eyshi Rezaei, Jing Li, Zhigang Sun, Junqiang Zhang
2022 Remote Sensing  
Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (R2 increased by 8.1% and RMSE decreased by 27.4%).  ...  For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (R2 = 0.93, RMSE = 0.77 t·ha−1).  ...  Acknowledgments: Thanks to Guicang Ma, Danyang Yu, and Jiang Bian for UAV flight missions; colleagues of YCES for field measurement.  ... 
doi:10.3390/rs14071559 fatcat:24bbofq4znaphcisea73g44z4i

Maize yield in smallholder agriculture system—An approach integrating socio-economic and crop management factors

Sudarshan Dutta, Somsubhra Chakraborty, Rupak Goswami, Hirak Banerjee, Kaushik Majumdar, Bin Li, M. L. Jat, Umair Ashraf
2020 PLoS ONE  
Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management.  ...  Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential  ...  Acknowledgments We acknowledge the participation of maize growers in Malda and Bankura districts in India in the farm surveys and farm visits. We are thankful to Dr.  ... 
doi:10.1371/journal.pone.0229100 pmid:32092077 pmcid:PMC7039445 fatcat:tbcymxf4sfgcpgro7dx43zuxii

A review of hyperspectral remote sensing of crops

Liheng Xia, Xueying Wu, L. Li, S. Zhu
2022 E3S Web of Conferences  
This paper has the following aspects to introduce the current situation of application of high-resolution and hyperspectral remote sensing data.  ...  With the development of space science and technology, various resource monitoring environmental satellites provide multi-platform, multi-spectral, multi-temporal and wide-range real-time information for  ...  and reliably predict the seed yield of different N applications and varieties in different years.  ... 
doi:10.1051/e3sconf/202233801029 fatcat:z3aa4mrymbcgdcwtg5nw7cgoqi

A Review on Drone-Based Data Solutions for Cereal Crops

Uma Shankar Panday, Arun Kumar Pratihast, Jagannath Aryal, Rijan Bhakta Kayastha
2020 Drones  
More specifically, the review covers sensing capabilities, promising algorithms, and methods, and added-value of novel machine learning algorithms for local-scale monitoring, biomass and yield estimation  ...  cereal crop productivity of small-scale farming systems in low-income countries.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/drones4030041 fatcat:ru4ourvzmnctfeky3uodsnynre

Benchmarking statistical modelling approaches with multi-source remote sensing data for millet yield monitoring: a case study of the groundnut basin in central Senegal

Yawogan Jean Eudes Gbodjo, Dino Ienco, Louise Leroux
2021 International Journal of Remote Sensing  
This study intends to estimate and forecast millet yields in central Senegal, making the use of multisource (synthetic-aperture radar (SAR) and optical) image time series and state-of-the-art machine learning  ...  A Random Forest (RF) model explained up to 50% of the millet yield variability, while deep learning models such as Convolutional Neural Network (CNN) showed promise results but performed lower.  ...  Disclosure statement No potential conflict of interest was reported by the author(s).  ... 
doi:10.1080/01431161.2021.1993465 fatcat:w5jtkhkiefeu5meptpptlpipmm

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

Xinxin Chen, Lan Feng, Rui Yao, Xiaojun Wu, Jia Sun, Wei Gong
2021 Remote Sensing  
This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.  ...  Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13010146 fatcat:5e77zdsfsbf4zidaaoctyvy6wu

Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels

Baiyu Qiao, Xiongkui He, Yajia Liu, Hao Zhang, Lanting Zhang, Limin Liu, Alice-Jacqueline Reineke, Wenxin Liu, Joachim Müller
2022 Remote Sensing  
The main purpose of this paper is to monitor the maize status under two phosphorus levels in soil by a nondestructive testing method and identify different phosphorus treatments by spectral data.  ...  The regression coefficients of 0.75 and 0.5 for the prediction models of LAI and yield were found by combining the spectral data under two P levels.  ...  We sincerely thank the editors and anonymous reviewers for their constructive comments and suggestions on the manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs14030493 fatcat:jsrui3o4gbeljorfe6knjtnepe

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

Sotirios V. Archontoulis, Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez‐Feria, Laila Puntel, Raziel A. Ordóñez, Javed Iqbal, Emily E. Wright, Ranae N. Dietzel (+13 others)
2020 Crop science  
We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA  ...  Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA.  ...  ACKNOWLEDGEMENTS This work was sponsored by Iowa Soybean Association, Foundation for Food and Agricultural Research (Grant #534264), Iowa Crop Improvement Association, NSF  ... 
doi:10.1002/csc2.20039 fatcat:qgyxj3qy5nbtblqynq6m6xsbwy

Maize Kernel Abortion Recognition and Classification Using Binary Classification Machine Learning Algorithms and Deep Convolutional Neural Networks

Lovemore Chipindu, Walter Mupangwa, Jihad Mtsilizah, Isaiah Nyagumbo, Mainassara Zaman-Allah
2020 AI  
By using a convolution neural network (CNN) method, more data (big data) can be collected and processed for hundreds of maize ears, accelerating the phenotyping process.  ...  Therefore, the measurement of kernel traits is important for maize breeding and the evaluation of maize yield.  ...  Training, Testing, and 10-Fold Cross-Validation The study and construction of algorithms that can learn from and make predictions on data is the major task of deep learning and machine learning.  ... 
doi:10.3390/ai1030024 fatcat:va76lwjyevdrpjamoor5s44goq

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Esti-mation Under Variety Performance Trials

Kai-Yun Li, Niall Burnside, Raul Sampaio de Lima, Miguel Villoslada Peciña, Karli Sepp, Ming-Der Yang, Janar Raet, Ants Vain, Are Selge, Kalev Sepp
2021 Remote Sensing  
cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and  ...  However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA.  ...  School of Earth Sciences and Ecology, financed by the European Union, European Regional Development Fund (Estonian University of Life Sciences ASTRA project "Value-chain based bio-economy").  ... 
doi:10.3390/rs13101994 fatcat:47kjcon6rncipkdoftvautiehi
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