Interpolation of greenhouse environment data using multilayer perceptron

Moon, Taewon; Hong, Seojung; Choi, Ha Young; Jung, Do Hyun; Chang, Se Hong; Son, Jung Eek

doi:10.1016/j.compag.2019.105023

Cited by 49 publications

(30 citation statements)

References 23 publications

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“…At Cloud level, collected data can be processed and fused with external information retrieved from other sources (e.g., meteorological and historical data) for different purposes such as, for example, to forecast greenhouse's future variables trend in order to avoid possible dangerous environmental conditions. Nowadays, this last task has been successfully accomplished with Machine Learning (ML) techniques, such as Artificial Neural Networks (ANNs) [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting

Codeluppi

Cilfone

Davoli

et al. 2020

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

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Section: Introductionmentioning

confidence: 99%

AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting

Codeluppi

Cilfone

Davoli

et al. 2020

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

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“…Employment of Big Data for smart agriculture is a completely new concept [114]. Although Big Data applications in smart agriculture are not that common, they are meant for cloud computing and IoT-based smart agriculture application [67].…”

Section: Discussionmentioning

confidence: 99%

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

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Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

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“…Meanwhile, an MLP model was established using Neuro Solutions 6.31 (Neuro Dimension, Gainesville, Florida, USA). MLP is one of the commonly used feed forward artificial neural network (ANN) types for non-linear function approximation task which learns the pattern of data by several layers with connected perceptrons [24][25]. In this study, the seven selected topographic factors were served as the input layer, and the output layer was the SOM content.…”

Section: Establishment Of the Soil-landscape Modelmentioning

confidence: 99%

Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm

Chen¹,

Liu²,

Yang³

et al. 2019

EARTH

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In this study, the spatial distribution pattern of the roads, historical samples, digital elevation data, and other available resources were incorporated into the design of a soil-sampling scheme to predict the soil organic matter (SOM) of the northern region of Zhongxiang City, Hubei Province, and simulated annealing (SA) was applied to optimize the sampling design. The sampling points determined after optimization were used to establish a multivariate linear regression model to adequately reproduce the intrinsic link between topographic factors and the SOM at 13 different sampling scales in areas nearby the existing roadways in the study area. The topographic factors included slope, plane curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), and sediment transport index (STI). A multilayer perceptron (MLP) model was also constructed. Comparison of the accuracy of the multivariate linear regression and MLP models demonstrated the feasibility of an optimized soil sampling design based on the road network. With the optimized sampling design, accurate soil-landscape information can be obtained, and its precision is greater than that of the original sampling scheme before optimization. The optimized sampling design obtained reduces sampling costs, increases sampling efficiency, and provides an effective method for obtaining the spatial distribution pattern of organic matter in soils.

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Interpolation of greenhouse environment data using multilayer perceptron

Cited by 49 publications

References 23 publications

AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting

AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm

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