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Improved Accuracy of Phenological Detection in Rice Breeding by Using Ensemble Models of Machine Learning Based on UAV-RGB Imagery

Haixiao Ge, Fei Ma, Zhenwang Li, Zhengzheng Tan, Changwen Du
2021 Remote Sensing  
Bayes; support vector machine and logistic regression) as base models to estimate phenological stages in rice breeding.  ...  of rice based on unmanned aerial vehicle (UAV) RGB imagery.  ...  In this study, the performance of five machine learning algorithms and three ensemble models was compared to detect phenology in rice breeding.  ... 
doi:10.3390/rs13142678 fatcat:psjwwa7rtnafblgn6taeqde65m

Analysis of Plant Breeding on Hadoop and Spark

Shuangxi Chen, Chunming Wu, Yongmao Yu
2016 Advances in Agriculture  
Analysis of crop breeding technology is one of the important means of computer-assisted breeding techniques which have huge data, high dimensions, and a lot of unstructured data.  ...  By experiments and tests of Indica and Japonica rice traits, plant breeding analysis platform can significantly improve the breeding of big data analysis speed, reducing the workload of concurrent programming  ...  MLlib as the achievement of machine learning algorithm on Spark platform supports common machine learning problems such as classification, regression, clustering, and collaborative filtering.  ... 
doi:10.1155/2016/7081491 fatcat:m67r6na6lfc4bloxaf6ywvvvqq

A Review on the Role of Machine Learning in Agriculture

Syamasudha Veeragandham, Santhi H
2020 Scalable Computing : Practice and Experience  
The machine learning models facilitate very fast and optimal decisions. The model of machine learning involves with training and testing to predict the accuracy of the result.  ...  Machine learning is a promising domain which is widely used now a days in the field of agriculture. The availability of manpower for agriculture is not enough and skill full farmers are less.  ...  Gaussian kernel-based Support Vector Machines (SVM) based method performs better than the k-Nearest Neighbour (k-NN), and Bagged Trees.  ... 
doi:10.12694/scpe.v21i4.1699 fatcat:sq3cn6hsr5expflmpdvqwxvzqm

Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning

Yali Zhang, Luchao Bai, Yuan Qi, Huasheng Huang, Xiaoyang Lu, Junqi Xiao, Yubin Lan, Muhua Lin, Jizhong Deng
2022 Agriculture  
The accuracy of the model reaches up to 96–100%. Hyperspectral technology and machine learning algorithm are capable of effective detection of rice spikelet flowering.  ...  This study attempts to acquire rice spikelet flowering information using a hyperspectral technique and machine learning in order to meet the needs of hybrid rice seed pollination rapidly and automatically  ...  based on machine learning algorithms.  ... 
doi:10.3390/agriculture12060755 fatcat:5fufkjoc5fcszesaowffs365gu

Fast-forward breeding for a food-secure world

Rajeev K. Varshney, Abhishek Bohra, Manish Roorkiwal, Rutwik Barmukh, Wallace A. Cowling, Annapurna Chitikineni, Hon-Ming Lam, Lee T. Hickey, Janine S. Croser, Philipp E. Bayer, David Edwards, José Crossa (+5 others)
2021 Trends in Genetics  
Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research.  ...  Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future.  ...  of Science and  ... 
doi:10.1016/j.tig.2021.08.002 pmid:34531040 fatcat:azivifn6fnfzxdkauiwk2z3lji

High-throughput field crop phenotyping: current status and challenges

Seishi Ninomiya
2022 Breeding Science  
Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image  ...  This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance  ...  under climatic change" (JPMJSC16H2), and the aXis B type project "Development and demonstration of highperformance rice breeding support pipeline for semiarid area" of the Japan Science and Technology  ... 
doi:10.1270/jsbbs.21069 pmid:36045897 pmcid:PMC8987842 fatcat:qyabvcv6xngbhc6epjtocob5tq

Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

Jinnuo Zhang, Yong Yang, Xuping Feng, Hongxia Xu, Jianping Chen, Yong He
2020 Frontiers in Plant Science  
network (CNN), and traditional machine learning methods, support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA).  ...  The feasibility of using terahertz imaging technology and near-infrared hyperspectral imaging technology to identify BB resistant seeds has therefore been studied.  ...  A deep learning method (CNN) and traditional machine learning methods (SVM, PLS-DA, and RF) were applied to build discriminant models based on either the 2D spectral images or the 1D spectra.  ... 
doi:10.3389/fpls.2020.00821 pmid:32670316 pmcid:PMC7326944 fatcat:myiqpw2y6zbgfntg7adcyxinv4

Decision Support System Using Artificial Neural Network to Predict Rice Production in Phimai District, Thailand

Saisunee Jabjone, Sura Wannasang
2014 International Journal of Computer and Electrical Engineering  
This study aims to develop the decision support system using Artificial Neural Networks (ANN) by adjust the value of parameters and study about 9 Algorithms training.  ...  CGB Algorithm has coefficient decision higher than using regression variable technique by Stepwise multiple method curve of ANN and stepwise multiple regression method was 4,293.70 and 40,160.00, respectively  ...  Thank you to officer of Ministry of Agriculture and Cooperatives, Thai Meteorological Department, Land Development Department and Royal Irrigation Department, who supported the data, gave the advice and  ... 
doi:10.7763/ijcee.2014.v6.814 fatcat:2byhlak37vasbkf7btcikapgs4

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  
Objective: This paper has been prepared as an effort to reassess the research studies on the relevance of machine learning techniques in the domain of agricultural crop production.  ...  Time series analysis, Markov chain model, k-means clustering, k nearest neighbor, and support vector machine are applied in the domain of agriculture were presented.  ...  Neural Networks 12 Forecasting Thailands Rice Export Building a fuzzy logic information network and a decision-support system 8 for olive cultivation in Andalusia List of important fusion of machine learning  ... 
doi:10.17485/ijst/2016/v9i38/95032 fatcat:wssmaerlavhk7eiymp23uzxvge

Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

Jian Wang, Bizhi Wu, Markus V. Kohnen, Daqi Lin, Changcai Yang, Xiaowei Wang, Ailing Qiang, Wei Liu, Jianbin Kang, Hua Li, Jing Shen, Tianhao Yao (+3 others)
2021 Plant Phenomics  
Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice  ...  In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding  ...  Acknowledgments The authors acknowledge the help from Pengfei Gao, Kaiqiang Hu, Kai Chen, and Yubang Gao from Lianfeng Gu's group during data collection.  ... 
doi:10.34133/2021/9765952 pmid:33851136 pmcid:PMC8028843 fatcat:fb2alxovr5grtm2nt2sjczdiqy

Feature Based Qualitative Classification of Rice Varieties: A Review

Komal, Ganesh Kumar Sethi, Rajesh Kumar Bawa
2020 Journal of scientific research  
In this paper, Different techniques of machine learning, deep learning and image processing considering morphological, color, shape, textural as well as other features of rice are analyzed to review the  ...  Various procedures and methods are considered for the review purpose to analyze the quality of rice grains on the basis of different features of rice.  ...  In future, new systems with higher accuracy can be developed with the help of ever advancing new technologies of machine learning, deep learning and image processing using conjunction of different combined  ... 
doi:10.37398/jsr.2020.640242 fatcat:ukqojvqiovciznx3kp3ijmjcbe

Artificial Neural Networks in Agriculture

Sebastian Kujawa, Gniewko Niedbała
2021 Agriculture  
Artificial neural networks are one of the most important elements of machine learning and artificial intelligence.  ...  The inclusion of machine learning methods in the "life cycle of a farm" requires handling large amounts of data collected during the entire growing season and having the appropriate software.  ...  weaknesses and their suggestions to optimize the manuscripts and the editorial staff of MDPI for the professional support and the rapid actions taken when necessary throughout the editorial process.  ... 
doi:10.3390/agriculture11060497 fatcat:7yofwdsc3jg7pfft5plp2ilmzy

Big Data and Climate Smart Agriculture-Status and Implications for Agricultural Research and Innovation in India

N. H. Rao
2018 Proceedings of the Indian National Science Academy  
It is a multi-stage, multi-objective, data-driven, and knowledge based approach to agriculture, with the farm as the most fundamental unit for both strategic and tactical decisions.  ...  The state-of-art on big data based approaches at each of the three levels is assessed.  ...  Similarly, to deal with high volume, variety and velocity aspects of big data, machine learning technologies are used to rapidly fit, optimize and predict data.  ... 
doi:10.16943/ptinsa/2018/49342 fatcat:kvvmdqqkhvbthb7gdktnxbx4ha


Anton Feriady, Maheran Mulyadi, Elni Mutmainnah
2022 Jurnal AGRISEP  
breeding business in Bengkulu Tengah Regency was profitable, amounting to Rp.23,719,248/planting season, but based on the study of its characteristics it was necessary to improve and evaluate the role  ...  as a certified rice seed breeder from the Center for Seed Supervision and Certification of the Horticulture and Plantation Office of the Bengkulu Province.The results showed that the certified rice seed  ...  Thus, farmers in Bengkulu Tengah Regency are very experienced and master the technology of breeding certified rice seeds.  ... 
doi:10.31186/jagrisep.21.2.409-424 fatcat:ieyb4vq3qbgyvjrfpsatoc6kiu

Predicting Rice Heading Date Using an Integrated Approach Combining a Machine Learning Method and a Crop Growth Model

Tai-Shen Chen, Toru Aoike, Masanori Yamasaki, Hiromi Kajiya-Kanegae, Hiroyoshi Iwata
2020 Frontiers in Genetics  
In this study, the integration of a machine learning model and a CGM was better able to predict the heading date of a new rice cultivar in an untested potential environment.  ...  The proposed approach outperformed the machine learning method in the prediction of an untested genotype in an untested location.  ...  ACKNOWLEDGMENTS We sincere thanks to the following members: Osamu Ideta, Tomomori Kataoka, Narifumi Yokogami, Ryota Kaji, Hideo Maeda, Kazumasa Murata, and Hiroshi Nakagawa for helping with the phenotyping  ... 
doi:10.3389/fgene.2020.599510 pmid:33391352 pmcid:PMC7775545 fatcat:k4cckgk6h5f4verbbvpquu5hvu
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