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Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

Sudhanshu Sekhar Panda, Daniel P. Ames, Suranjan Panigrahi
2010 Remote Sensing  
The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques.  ...  four vegetation indices in corn crop yield prediction.  ...  Acknowledgment The authors express their sincere thanks to NASA for financial support of this study.  ... 
doi:10.3390/rs2030673 fatcat:vgt5fdrsmnbzzdhxttra7ib6s4

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Patryk Hara, Magdalena Piekutowska, Gniewko Niedbała
2021 Land  
The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs).  ...  The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail.  ...  The application of artificial neural networks (ANNs) in agriculture solved the problem of the lack of linearity between the crop yield and independent variables.  ... 
doi:10.3390/land10060609 fatcat:vjxlulqnlrbbdpn7l4wbg4k5zq


O. G. Narin, A. Sekertekin, A. Saygin, F. Balik Sanli, M. Gullu
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In addition, the efficiency of linear regression, Convolutional Neural Network (CNN), and Artificial Neural Network (ANN) techniques are examined with the use of indices in yield estimation.  ...  Due to food security and agricultural land management, it is crucial for decision makers and farmers to predict crop yields.  ...  In addition, the efficiency of linear regression, Convolutional Neural Network (CNN), and Artificial Neural Network (ANN) techniques are examined with the use of indices in yield estimation.  ... 
doi:10.5194/isprs-archives-xlvi-4-w5-2021-385-2021 fatcat:ibwurgnhqfautiyqcawkvkdwe4

Spatial Data Mining- A tool for Spatial Decision Support System in Agriculture Management

Vidya Kumbhar, Akhil Maru, Sneha Kumari
2022 Journal of Engineering Science and Technology Review  
The study also summarizes the application of geospatial data mining techniques and algorithms in agriculture.  ...  Data Mining has left a vast scope for decision making in government and enterprises. The gap has been bridged by several techniques. Data mining is one of the such technique.  ...  This is an Open Access article distributed under the terms of the Creative Commons Attribution License. ______________________________ References  ... 
doi:10.25103/jestr.151.16 fatcat:hn5ftvppzzgfjlyv553gzqzili

Crop Yield Prediction Techniques using Remote Sensing Data

2020 International Journal of Engineering and Advanced Technology  
Crop yield prediction is an art of forecasting the yield of crop before harvesting.  ...  Prediction of crop yield will be very useful for the government to make food policies, market price, import and export policies and proper warehousing well in time.  ...  RELATED WORK 4.1.Aback-propagation neural network is proposed and conjugate gradient algorithm was used for training to predict the corn yield in Ottawa by using vegetation indices (VI) & textural indices  ... 
doi:10.35940/ijeat.c6217.029320 fatcat:omohesb5p5aido2rxp4c3chicm

A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing

Priyanga Muruganantham, Santoso Wibowo, Srimannarayana Grandhi, Nahidul Hoque Samrat, Nahina Islam
2022 Remote Sensing  
Findings show that vegetation indices are the most used feature for crop yield prediction.  ...  This study finds that Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) are the most widely used deep learning approaches for crop yield prediction.  ...  Informed Consent Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs14091990 fatcat:pnexuzafv5e27h4spgvvk6ywb4

Automatic Estimation of Nitrogen content in Cotton (Gossypium Hirsutum L) Plant by using Image Processing Techniques: A Review

Asaram PandurangJanwale, Santosh S. Lomte
2015 International Journal of Computer Applications  
There are different methods for Nitrogen detection like color analysis using deferent color analysis model, remote sensing, and neural network etc.  ...  Cotton is an important crop in India. Yield depends on many factors like nutrients, water etc. Nitrogen plays important role to increase yield.  ...  Artificial Neural Network In this paper [4] , a three-layer multilayer perception (MLP) artificial neural network (ANN) based prediction system was presented for predicting the leaf chlorophyll content  ... 
doi:10.5120/21343-4355 fatcat:xtm2dapq75gttj6p3mcifs7vze

Agriculture Crop Enhancing Identification and Classification using Machine Learning Techniques

Prayagkumar Patel, Dr. Anilkumar Suthar
2022 IJARCCE  
Many Techniques are used for crop yield prediction, including supportive decisions on what crops to grow and what to do during the growing period of the crops.  ...  Crop yield prediction has been a topic of interest for producers, consultants, and agricultural related organizations.  ...  Another research for Yield prediction is Wheat yield prediction using machine learning and advanced sensing techniques [10] .  ... 
doi:10.17148/ijarcce.2022.114103 fatcat:meccfn7rmjhstfejmjmjvivmvi

Using artificial neural network and satellite data to predict rice yield in Bangladesh

Kawsar Akhand, Mohammad Nizamuddin, Leonid Roytman, Felix Kogan, Mitch Goldberg, Wei Gao, Ni-Bin Chang, Jinnian Wang
2015 Remote Sensing and Modeling of Ecosystems for Sustainability XII  
This study demonstrates the successful application of Artificial Neural Network (ANN) and remote sensing satellite data in developing a reliable prediction model using Advanced Very High Resolution Radiometer  ...  (AVHRR) sensor-based vegetation health indices (the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)) and statistical yield data to predict Boro rice yield, the main rice variety  ...  Neural Network-Based Model for Predicting Boro Rice Yield in Bangladesh Using AVHRR-Based Satellite Data  ... 
doi:10.1117/12.2186261 fatcat:5xcf6pq2xjghfbt4ueacp6vt6m

A comprehensive review on machine learning in agriculture domain

Kavita Jhajharia, Pratistha Mathur
2022 IAES International Journal of Artificial Intelligence (IJ-AI)  
The observed sub-categories of the agriculture domain are crop yield prediction, soil management, pest management, weed management, and crop disease.  ...  Machine learning is a vitally used technology in agriculture to protect food security and sustainability.  ...  the model to satellite imagery soybean data of predict the crop Argentina and yield of one predicts fine for region.  ... 
doi:10.11591/ijai.v11.i2.pp753-763 fatcat:fwa7ljqb4fgehok44s6rijbtbe

Monitoring nitrogen concentration of oilseed rape from hyperspectral data using radial basis function

Fumin Wang, Jingfeng Huang, Yuan Wang, Zhuanyu Liu, Dailiang Peng, Feifeng Cao
2013 International Journal of Digital Earth  
The study showed that nitrogen concentrations of oilseed rape canopy could be monitored using remotely sensed data and the RBF method, especially the GRNN method, is a useful explorative tool for oilseed  ...  His primary research areas include the applications of remote sensing technologies to estimating the planting area of crop, monitoring their growth, and predicting grain yields at variable spatial and  ...  His primary research area is the application of hyperspectral remote sensing technology in crop disease monitoring.  ... 
doi:10.1080/17538947.2011.628414 fatcat:c4mqoc4dpfdd5i3t44nf5wcy2u

A survey on plant disease prediction using machine learning and deep learning techniques

UshaDevi G, Gokulnath BV
2020 Inteligencia Artificial  
A survey of different existing machine learning techniques used for plant disease prediction was presented in this paper.  ...  Analysis of this data helps in predicting the crop yield, analyzing soil quality, predicting disease in a plant, and how meteorological factor affects crop productivity.  ...  Acknowledgements This is the place for acknowledgements. Referencias  ... 
doi:10.4114/intartif.vol23iss65pp136-154 fatcat:lc3qvowvbjhvvp5o5k32bqoxka

Machine Learning in Agriculture Application: Algorithms and Techniques

This paper is categorized into three sections a) Yield prediction using machine learning technique b) Price prediction c) Leaf disease detection using neural networks.  ...  In this paper we study the comparison of neural network models with existing models.  ...  Yield performance is calculated using yield and check yield using deep neural networks. Neural network in his paper is also used for weather prediction.  ... 
doi:10.35940/ijitee.f3713.049620 fatcat:wfmtzxcwhbaatm3mlmmatfeuqy

Applications of Machine Learning Techniques in Agricultural Crop Production: A Review Paper

Subhadra Mishra, Debahuti Mishra, Gour Hari Santra
2016 Indian Journal of Science and Technology  
Application / Improvement: The few techniques like artificial neural networks, Information Fuzzy Network, Decision Tree, Regression Analysis, Bayesian belief network.  ...  of the data with crop yield evaluation.  ...  techniques in agriculture Fuzzy Cognitive Map learning approach 21 Yield prediction in apples Regression and Neural Networks Models 14 for Prediction of Crop Production Fusion type Application area  ... 
doi:10.17485/ijst/2016/v9i38/95032 fatcat:wssmaerlavhk7eiymp23uzxvge

Artificial Neural Networks in Agriculture

Sebastian Kujawa, Gniewko Niedbała
2021 Agriculture  
The spectrum of neural networks application is very wide, and it also includes agriculture.  ...  Artificial neural networks are increasingly used by food producers at every stage of agricultural production and in efficient farm management.  ...  Acknowledgments: We thank the authors for submitting manuscripts of high quality and their willingness to further improve them after peer review, the reviewers for their careful evaluations aimed at eliminating  ... 
doi:10.3390/agriculture11060497 fatcat:7yofwdsc3jg7pfft5plp2ilmzy
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