Application of big data technology in agricultural Internet of Things

Chun-ling, Zhang; Liu, Zunfeng

doi:10.1177/1550147719881610

Cited by 25 publications

(16 citation statements)

References 27 publications

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“…Some relevant applications of IoT in agriculture are described in ref. [ 132 , 133 , 134 , 135 ]. In particular, a system based on wireless sensors was reported in ref.…”

Section: Innovative Technologies For Plant Pathologymentioning

confidence: 99%

Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics

Buja

Sabella

Monteduro

et al. 2021

Sensors

136

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Human activities significantly contribute to worldwide spread of phytopathological adversities. Pathogen-related food losses are today responsible for a reduction in quantity and quality of yield and decrease value and financial returns. As a result, “early detection” in combination with “fast, accurate, and cheap” diagnostics have also become the new mantra in plant pathology, especially for emerging diseases or challenging pathogens that spread thanks to asymptomatic individuals with subtle initial symptoms but are then difficult to face. Furthermore, in a globalized market sensitive to epidemics, innovative tools suitable for field-use represent the new frontier with respect to diagnostic laboratories, ensuring that the instruments and techniques used are suitable for the operational contexts. In this framework, portable systems and interconnection with Internet of Things (IoT) play a pivotal role. Here we review innovative diagnostic methods based on nanotechnologies and new perspectives concerning information and communication technology (ICT) in agriculture, resulting in an improvement in agricultural and rural development and in the ability to revolutionize the concept of “preventive actions”, making the difference in fighting against phytopathogens, all over the world.

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“…Some relevant applications of IoT in agriculture are described in ref. [ 132 , 133 , 134 , 135 ]. In particular, a system based on wireless sensors was reported in ref.…”

Section: Innovative Technologies For Plant Pathologymentioning

confidence: 99%

Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics

Buja

Sabella

Monteduro

et al. 2021

Sensors

136

View full text Add to dashboard Cite

show abstract

“…For example, the integration of sensors (Basnet and Bang 2018), artificial intelligence (Patrício and Rieder 2018), robotics, and remote sensing technologies ) into all aspects of agricultural development as a focus for future technological development can improve overall efficiency and sustainability (Dlodlo and Kalwzhi 2015). Contemporary agriculture is increasingly inclined to synergistically adopt technologies such as the Internet of Things (Ayaz et al 2019), the Internet of Agriculture, big data analytics (Zhang and Liu 2019) and blockchain (Sun 2017)to integrate resource conservation with economic, social and environmental sustainability (Batie 1989).…”

Section: The Development Of Digital Agriculturementioning

confidence: 99%

The Carbon Emissions Reduction Effect of Digital Agriculture in China

Wei

2022

Preprint

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Carbon emissions reduction is gaining increasing attention worldwide. Digital agriculture has a carbon emissions reduction effect. This paper focuses on how the development of digital agriculture contributes to agricultural carbon emissions reduction.To this end, the spatial characteristics, spillover effects and driving factors of digital agriculture on agricultural carbon emissions are explored using panel data of 31 regions in China from 2011 to 2019 using a spatial econometric model. The results show that digital agriculture development reduces agricultural carbon emissions.The results remain robust after estimation using the replacement weight method and the explanatory variable substitution method. Agricultural technological progress, agricultural industry structure, and rural education level all contribute to the reduction of agricultural carbon emissions in a region, while agricultural carbon emissions in the neighboring regions have a negative relationship with the agricultural industry structure in the region and a positive relationship with rural education level and agricultural technological level. Furthermore, strengthening the exchange of digital agriculture between regions and leveraging the intermediary effect of digital inclusive finance can effectively enhance the carbon emissions reduction effect.

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“…To provide healthy food to feed the population worldwide, smart agricultural greenhouses (SAG) utilize advanced intelligent information technologies to break through the limits of natural conditions by providing a stable and controllable environment for crop growing and planting management, which has recently become the significant solution to produce more with minimal socioeconomic and ecological loss from limited tillage and labor [1]. On basis of artificial intelligence [2], Internet of Things (IoT) [3], big data computing [4], 5G communication [5], blockchain [6], etc., the SAG systems record various environmental factors for real-time monitoring of crop growth status and soil/climate changes, which aims to optimize the data is strongly random and the model is not learnable. This has resulted in poor prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse

et al. 2021

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Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic temporal data, this study proposes a bidirectional self-attentive encoder–decoder framework (BEDA) to construct the long-time predictor for multiple environmental factors with high nonlinearity and noise in a smart greenhouse. Firstly, the original data are denoised by wavelet threshold filter and pretreatment operations. Secondly, the bidirectional long short-term-memory is selected as the fundamental unit to extract time-serial features. Then, the multi-head self-attention mechanism is incorporated into the encoder–decoder framework to improve the prediction performance. Experimental investigations are conducted in a practical greenhouse to accurately predict indoor environmental factors (temperature, humidity, and CO2) from noisy IoT-based sensors. The best model for all datasets was the proposed BEDA method, with the root mean square error of three factors’ prediction reduced to 2.726, 3.621, and 49.817, and with an R of 0.749 for temperature, 0.848 for humidity, and 0.8711 for CO2 concentration, respectively. The experimental results show that the favorable prediction accuracy, robustness, and generalization of the proposed method make it suitable to more precisely manage greenhouses.

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Application of big data technology in agricultural Internet of Things

Cited by 25 publications

References 27 publications

Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics

Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics

The Carbon Emissions Reduction Effect of Digital Agriculture in China

Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse

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