Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse

Park, Soo Hyun; Kim, Hyoung Seok; Jhin, Changho; Kim, Hak-Jin

doi:10.1016/j.compag.2020.105402

Cited by 121 publications

(66 citation statements)

References 24 publications

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“…In other words, this means that the air temperature can be forecast with a standard deviation of 1.50°C with respect to the actual value. If compared with the models proposed in the literature (recalled in Section II), which allow to predict air temperature values with a RMSE < 1°C, then the RMSE of our proposed model is slightly higher (in the range of 0.5-1.0°C [7], [16]) or similar [15]. Considering the R 2 metric, the obtained value (i.e., 0.965) is similar to the results outlined in [16], but higher than the score in [15].…”

Section: Resultsmentioning

confidence: 94%

“…If compared with the models proposed in the literature (recalled in Section II), which allow to predict air temperature values with a RMSE < 1°C, then the RMSE of our proposed model is slightly higher (in the range of 0.5-1.0°C [7], [16]) or similar [15]. Considering the R 2 metric, the obtained value (i.e., 0.965) is similar to the results outlined in [16], but higher than the score in [15]. Nevertheless, the obtained prediction performance is satisfactory, taking into account the accuracy of the environmental sensor adopted to measure air temperature (i.e., ±1°C) and the type of application (namely, greenhouse's internal air temperature monitoring).…”

Section: Resultsmentioning

confidence: 94%

“…From a technological point of view, several efforts have been undertaken in designing approaches and defining systems aiming at simplifying, enhancing and making the regulation and supervision of greenhouse inner variables automatic. More precisely, as discussed in Section I, automatic management of the internal climate of a greenhouse usually includes: (i) a WSN for collecting and monitoring internal variables, based on IoT technologies [10]- [12]; (ii) several control systems, which automatically perform operations to maintain the internal conditions within desirable ranges [13], [14]; and (iii) internal variables' prediction techniques, in order to forecast their future trends, based on ML-based algorithms, such as ANNs [15], [16].…”

Section: Related Workmentioning

confidence: 99%

“…Concerning the first class of models, they take advantage of features typical of time series (i.e., data which are sampled periodically and have a time reference, as sensor data), which include trends, seasonality, and correlation between samples which are closest in time. The best results in this field have been obtained by architectures able to discover deep relations between temporally-close data, such as Recurrent Neural Networks (RNNs) [15] and Long-Short Term Memory (LSTM) Networks [16]. Unfortunately, a drawback of RNNs and LSTMs is the required significant amount of memory and computational power, with respect to other models, such as Convolutional Neural Networks (CNNs) [17].…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

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

Codeluppi

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Davoli

et al. 2020

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

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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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“…In recent years, machine learning has driven advances in many different fields [ 12 , 13 , 14 ]. The process of predicting the sensory data is used not only for securing reliability of the existing data and defining the cause of faults that had already occurred also in forecasting future to detect the user’s risk in advance.…”

Section: Methodsmentioning

confidence: 99%

Optimization of Machine Learning in Various Situations Using ICT-Based TVOC Sensors

Cho

Lee

2020

Micromachines

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A computational framework using artificial intelligence (AI) has been suggested in numerous fields, such as medicine, robotics, meteorology, and chemistry. The specificity of each AI model and the relationship between data characteristics and ground truth, allowing their guidance according to each situation, has not been given. Since TVOCs (total volatile organic compounds) cause serious harm to human health and plants, the prevention of such damages with a reduction in their occurrence frequency becomes not an optional process but an essential one in manufacturing, as well as for chemical industries and laboratories. In this study, with consideration of the characteristics of the machine learning technique and ICT (information and communications technology), TVOC sensors are explored as a function of grounded data analysis and the selection of machine learning models, determining their performance in real situations. For representative scenarios, considering features from an ICT semiconductor sensor and one targeting TVOC gas, we investigated suitable analysis methods and machine learning models such as LSTM (long short-term memory), GRU (gated recurrent unit), and RNN (recurrent neural network). Detailed factors for these machine learning models with respect to the concentration of TVOC gas in the atmosphere are compared with original sensory data to obtain their accuracy. From this work, we expect to significantly minimize risk in empirical applications, i.e., maintaining homeostasis or predicting abnormal situations to construct an opportune response.

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A long short‐term memory‐based model for greenhouse climate prediction

et al. 2021

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Greenhouses can grow many off‐season vegetables and fruits, which improves people's quality of life. Greenhouses can also help crops resist natural disasters and ensure the stable growth of crops. However, it is highly challenging to carefully control the greenhouse climate. Therefore, the proposal of a greenhouse climate prediction model provides a way to solve this challenge. We focus on the six climatic factors that affect crops growth, including temperature, humidity, illumination, carbon dioxide concentration, soil temperature and soil humidity, and propose a GCP_lstm model for greenhouse climate prediction. The climate change in greenhouse is nonlinear, so we use long short‐term memory (LSTM) model to capture the dependence between historical climate data. Moreover, the short‐term climate has a greater impact on the future trend of greenhouse climate change. Therefore, we added a 5‐min time sliding window through the analysis experiment. In addition, sensors sometimes collect wrong climate data. Based on the existence of abnormal data, our model still has good robustness. We experienced our method on the data sets of three vegetables: tomato, cucumber and pepper. The comparison shows that our method is better than other comparison models.

show abstract

Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse

Cited by 121 publications

References 24 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

Optimization of Machine Learning in Various Situations Using ICT-Based TVOC Sensors

A long short‐term memory‐based model for greenhouse climate prediction

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