An IoT based smart irrigation management system using Machine learning and open source technologies

Goap, Amarendra; Sharma, D. K.; Shukla, Alok Kumar; Krishna, C. Rama

doi:10.1016/j.compag.2018.09.040

Cited by 461 publications

(202 citation statements)

References 20 publications

Supporting

Mentioning

191

Contrasting

Unclassified

Order By: Relevance

“…Growers of the future will be able to take advantage of precise irrigation recommendations using information sourced from a fleet of UAS that map large farm blocks on a daily schedule, continuous ground-based proximal and direct sensors, and weather stations. This data can be stored on and accessed from the cloud almost instantaneously, used in conjunction with post-processing algorithms for decision-making on optimised irrigation applications [311,314].…”

Section: Future Prospective and Gaps In The Knowledgementioning

confidence: 99%

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

Gautam

Pagay

2020

Agronomy

View full text Add to dashboard Cite

With increasingly advanced remote sensing systems, more accurate retrievals of crop water status are being made at the individual crop level to aid in precision irrigation. This paper summarises the use of remote sensing for the estimation of water status in horticultural crops. The remote measurements of the water potential, soil moisture, evapotranspiration, canopy 3D structure, and vigour for water status estimation are presented in this comprehensive review. These parameters directly or indirectly provide estimates of crop water status, which is critically important for irrigation management in farms. The review is organised into four main sections: (i) remote sensing platforms; (ii) the remote sensor suite; (iii) techniques adopted for horticultural applications and indicators of water status; and, (iv) case studies of the use of remote sensing in horticultural crops. Finally, the authors' view is presented with regard to future prospects and research gaps in the estimation of the crop water status for precision irrigation.

show abstract

Section: Future Prospective and Gaps In The Knowledgementioning

confidence: 99%

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

Gautam

Pagay

2020

Agronomy

View full text Add to dashboard Cite

show abstract

“…The GBRT model outperformed the others and, so, the decision support service incorporated it as a module of the system. A model based on support vector regression (SVR) and k-means ML techniques which used weather data and forecasts, as well the soil data (temperature and moisture), to forecast the soil moisture has also been investigated [44].…”

Section: Soil Moisture Predictionmentioning

confidence: 99%

Smart & Green: An Internet-of-Things Framework for Smart Irrigation

Campos

Rocha²,

Gondim

et al. 2019

Sensors

View full text Add to dashboard Cite

Irrigation is one of the most water-intensive agricultural activities in the world, which has been increasing over time. Choosing an optimal irrigation management plan depends on having available data in the monitoring field. A smart agriculture system gathers data from several sources; however, the data are not guaranteed to be free of discrepant values (i.e., outliers), which can damage the precision of irrigation management. Furthermore, data from different sources must fit into the same temporal window required for irrigation management and the data preprocessing must be dynamic and automatic to benefit users of the irrigation management plan. In this paper, we propose the Smart&Green framework to offer services for smart irrigation, such as data monitoring, preprocessing, fusion, synchronization, storage, and irrigation management enriched by the prediction of soil moisture. Outlier removal techniques allow for more precise irrigation management. For fields without soil moisture sensors, the prediction model estimates the matric potential using weather, crop, and irrigation information. We apply the predicted matric potential approach to the Van Genutchen model to determine the moisture used in an irrigation management scheme. We can save, on average, between 56.4% and 90% of the irrigation water needed by applying the Zscore, MZscore and Chauvenet outlier removal techniques to the predicted data.

show abstract

“…[Treboux and Genoud 2018] apresentaram o impacto do aprendizado de máquina junto a agricultura de precisão na segregação de cores em imagens de satélites. [Goap et al 2018] propuseram um sistema inteligente programado em código aberto para prever os requisitos de irrigação do campo com utilização de vários sensores de medições físicas do solo e ambientais. [Santos et al 2018] apresentaram um modelo de arquitetura de redes de sensores sem fio para realizar predição de umidade e temperatura do solo.…”

Section: Trabalhos Relacionadosunclassified

Tellus: um modelo computacional para a predição da fertilidade do solo na agricultura de precisão

Helfer

Martini

Santos

et al. 2019

Anais Do Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP)

View full text Add to dashboard Cite

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing and its integration with the real world. One of the challenges in this area is the use of context awareness. In agriculture, the context can be related to the environment, for example, the chemical and physical aspects that characterize different types of soil over time. This paper proposes a computational model applied in precision agriculture that uses the contexts history to predict soil fertility. The best results were obtained in the prediction of organic matter, with a coefficient of determination (R 2 ) of 0.9102 for root mean square error (RMSE) of 0.49%. Resumo.A aplicação da computação ubíqua tem aumentado nosúltimos anos, especialmente devido ao desenvolvimento de tecnologias como a computação móvel e sua integração com o mundo real. Um dos desafios nessaáreaé o uso da sensibilidade ao contexto. Na agricultura, pode-se considerar o contexto relacionado ao meio ambiente, por exemplo, os aspectos químicos e físicos que caracterizam os diferentes tipos de solo ao longo do tempo. Este artigo propõe um modelo computacional aplicado na agricultura de precisão que usa os históricos de contextos para predição da fertilidade do solo. Os melhores resultados foram obtidos na predição de matéria orgânica, com um coeficiente de determinação (R 2 ) de 0.9102 para um erro quadrático médio (RMSE) de 0.49%. IntroduçãoA aplicação da agricultura de precisão tem se mostrado uma estratégia adequada para aumentar a produtividade, permitindo o uso racional de insumos e reduzindo os impactos ambientais causados pelas práticas agrícolas. Atualmente, os insumos são utilizados de forma variável, visando atender as necessidades específicas de cada localidade, otimizando o processo produtivo. Entretanto,é necessário caracterizar a variabilidade dos

show abstract

An IoT based smart irrigation management system using Machine learning and open source technologies

Cited by 461 publications

References 20 publications

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

Smart & Green: An Internet-of-Things Framework for Smart Irrigation

Tellus: um modelo computacional para a predição da fertilidade do solo na agricultura de precisão

Contact Info

Product

Resources

About