A low footprint olive grove weather forecasting using a single-layered seasonal attention encoder-decoder model
Weather forecasting is essential in various applications such as olive smart farming. Farmers use the predicted weather data to take appropriate actions with the aim of increasing the crop production. Many deep learning models have been developed for tackling such a problem. However, olive groves ar...
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| Vydáno v: | Ecological informatics Ročník 75; s. 102113 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.07.2023
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| Témata: | |
| ISSN: | 1574-9541 |
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| Abstract | Weather forecasting is essential in various applications such as olive smart farming. Farmers use the predicted weather data to take appropriate actions with the aim of increasing the crop production. Many deep learning models have been developed for tackling such a problem. However, olive groves are located in remote areas with no Internet connectivity, therefore these models are not applicable as they require either powerful processors or communication with cloud servers for inference. In this work, we propose a deep learning encoder-decoder model that uses a seasonal attention mechanism for time series forecasting of weather variables. The proposed model is non-complex, yet more powerful, compared to the more complex models in the literature. We use this model as the core of a framework that preprocess the training and testing data, train the model, and deploy the model on a resource-constrained microcontroller. Using real-life weather datasets of Spanish, Greek, and Chinese weather stations, we prove that the proposed model achieves a higher prediction accuracy compared to the existing literature. More specifically, the achieved prediction mean absolute error (MAE) is 2.13 °C and root mean squared error (RMSE) is 2.64 °C. This outstanding accuracy performance is achieved with the model requiring only 37.6 kB of memory for storing the model parameters with a total memory requirement of 50.1 kB. Since the model is relatively non-complex, we implement it on the Raspberry Pi Pico platform which has a very low cost with minimal power consumption compared to other embedded platforms. We also build a prototype and test it to verify the model's ability to achieve the target objective in real-life scenarios.
•Deep learning-based framework for predicting weather variables for olive farming.•A low complexity encoder-decoder model with a single LSTM/GRU layer.•Seasonal attention exploits seasonality to improve accuracy and reduce footprint.•Up to 29.7% improvement in MAE and 27.4% in RMSE with only 37.6 kB of memory.•Realized on a resource-constrained microcontroller (Raspberry Pi Pico). |
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| AbstractList | Weather forecasting is essential in various applications such as olive smart farming. Farmers use the predicted weather data to take appropriate actions with the aim of increasing the crop production. Many deep learning models have been developed for tackling such a problem. However, olive groves are located in remote areas with no Internet connectivity, therefore these models are not applicable as they require either powerful processors or communication with cloud servers for inference. In this work, we propose a deep learning encoder-decoder model that uses a seasonal attention mechanism for time series forecasting of weather variables. The proposed model is non-complex, yet more powerful, compared to the more complex models in the literature. We use this model as the core of a framework that preprocess the training and testing data, train the model, and deploy the model on a resource-constrained microcontroller. Using real-life weather datasets of Spanish, Greek, and Chinese weather stations, we prove that the proposed model achieves a higher prediction accuracy compared to the existing literature. More specifically, the achieved prediction mean absolute error (MAE) is 2.13 °C and root mean squared error (RMSE) is 2.64 °C. This outstanding accuracy performance is achieved with the model requiring only 37.6 kB of memory for storing the model parameters with a total memory requirement of 50.1 kB. Since the model is relatively non-complex, we implement it on the Raspberry Pi Pico platform which has a very low cost with minimal power consumption compared to other embedded platforms. We also build a prototype and test it to verify the model's ability to achieve the target objective in real-life scenarios.
•Deep learning-based framework for predicting weather variables for olive farming.•A low complexity encoder-decoder model with a single LSTM/GRU layer.•Seasonal attention exploits seasonality to improve accuracy and reduce footprint.•Up to 29.7% improvement in MAE and 27.4% in RMSE with only 37.6 kB of memory.•Realized on a resource-constrained microcontroller (Raspberry Pi Pico). |
| ArticleNumber | 102113 |
| Author | Khattab, Ahmed Mostafa, Hassan Abdelwahab, Mohamed H. |
| Author_xml | – sequence: 1 givenname: Mohamed H. surname: Abdelwahab fullname: Abdelwahab, Mohamed H. organization: Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt – sequence: 2 givenname: Hassan surname: Mostafa fullname: Mostafa, Hassan organization: Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt – sequence: 3 givenname: Ahmed surname: Khattab fullname: Khattab, Ahmed email: akhattab@eng.cu.edu.eg organization: Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt |
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| CitedBy_id | crossref_primary_10_1016_j_ipm_2024_103940 crossref_primary_10_3390_agriculture15030227 |
| Cites_doi | 10.1145/3524070 10.1007/s11265-020-01554-x 10.1016/j.ins.2022.02.007 10.1038/s41598-019-55320-6 10.1016/j.engappai.2019.07.011 10.1109/ACCESS.2021.3088075 10.1007/s10994-019-05815-0 10.1016/j.energy.2019.116187 10.1007/s10044-020-00898-1 10.1016/j.chaos.2020.110227 10.3390/agronomy10111755 10.3390/s21123973 10.1016/j.energy.2018.01.177 10.1007/s11042-019-08453-9 10.1016/j.procs.2020.03.036 10.1016/j.neucom.2019.12.118 |
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| Keywords | Deep learning Precision agriculture Long short-term memory Time series forecasting Encoder-decoder |
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| References | Raspberry Pi (bb0160) 2023 Sagheer, Kotb (bb0175) Dec. 2019; 9 Atef, Khattab, Agamy, Khairy (bb0010) Aug. 2021 Valenčič, Butinar, Podgornik, Bučar-Miklavčič (bb0220) 2021; 26 Kalamatianos, Avlonitis (bb0085) 2017 Raspberry Pi (bb0165) 2023 UCI Machine Learning Repository (bb0210) 2023 Laubscher (bb0090) 2019; 189 Fang, Yuan (bb0065) 2019; 85 STMicroelectronics (bb0205) 2023 Novac, Hacene, Pegatoquet, Miramond, Gripon (bb0125) 2021 Hewage, Trovati, Pereira, Behera (bb0075) Feb. 2021; 24 Coral (bb0050) 2023 Zhang, Suda, Lai, Chandra (bb0230) 2017 Zhang, Bi, Dong, Liu (bb0235) 2018 Nvidia Developer (bb0130) 2023 Observatory of Economic Complexity (OEC) (bb0135) 2023 Shih, Sun, Lee (bb0190) 2019; 108 Adams (bb0005) 2023 Qing, Niu (bb0155) 2018; 148 Mungarwal, Mehta (bb0115) 2023 Banbury, Zhou, Fedorov, Navarro, Thakker, Gope, Reddi, Mattina, Whatmough (bb0025) 2021 NanoPi (bb0120) 2023 Bahdanau, Cho, Bengio (bb0020) 2014 Politis (bb0145) Apr. 2023 Codeluppi, Davoli, Ferrari (bb0045) 2021; 21 Mamdouh, Wael, Khattab (bb0105) 2022 Benito-Picazo, Domínguez, Palomo, López-Rubio, Ortiz-de-Lazcano-Lobato (bb0035) 2018 Zhang, Zhang, Cao, Bian, Yi, Zheng, Li (bb0240) 2022 N. Mamdouh and A. Khattab, “YOLO-based deep Learning framework for olive fruit Fly detection and counting,” IEEE Access, vol. 9, pp.84252–84262, June 2021. Shastri, Singh, Kumar, Kour, Mansotra (bb0185) Aug. 2020; 140 Pinheiro, Dader, Wanumen, Pereira, Santos, Medina (bb0140) Nov. 2020; 10 Cho, Merrienboer, Gulcehre, Bahdanau, Bougares, Schwenk, Bengio (bb0040) 2014 Garbin, Zhu, Marques (bb0070) May 2020; 79 Yu, Lukefahr, Das, Mahlke (bb0225) Oct. 2019; 18 Liu, Hou, Bao, Qi (bb0095) Nov. 2017 Sparkfun (bb0200) 2023 Digi-Key Electronics (bb0055) 2023 Institute for Agricultural and Fisheries Research and Training (IFAPA) (bb0080) 2023 Samanta, Prakash, Chilukuri (bb0180) 2022; 593 UNESCO World Heritage Convention (bb0215) 2023 Rybalkin, Sudarshan, Weis, Lappas, Wehn, Cheng (bb0170) 2020; 92 Shovon (bb0195) Du, Li, Yang, Horng (bb0060) May 2020; 388 Mealey (bb0110) 2018 Baharani, Tabkhi (bb0015) September 2022; 21 Belavadi, Rajagopal, Ranjani, Mohan (bb0030) Jan. 2020; 170 Qin, Song, Cheng, Cheng, Jiang, Cottrell (bb0150) 2017 STMicroelectronics (10.1016/j.ecoinf.2023.102113_bb0205) Mungarwal (10.1016/j.ecoinf.2023.102113_bb0115) Novac (10.1016/j.ecoinf.2023.102113_bb0125) 2021 Raspberry Pi (10.1016/j.ecoinf.2023.102113_bb0160) Coral (10.1016/j.ecoinf.2023.102113_bb0050) Bahdanau (10.1016/j.ecoinf.2023.102113_bb0020) 2014 Sagheer (10.1016/j.ecoinf.2023.102113_bb0175) 2019; 9 Samanta (10.1016/j.ecoinf.2023.102113_bb0180) 2022; 593 Codeluppi (10.1016/j.ecoinf.2023.102113_bb0045) 2021; 21 Adams (10.1016/j.ecoinf.2023.102113_bb0005) Banbury (10.1016/j.ecoinf.2023.102113_bb0025) 2021 Hewage (10.1016/j.ecoinf.2023.102113_bb0075) 2021; 24 Zhang (10.1016/j.ecoinf.2023.102113_bb0240) 2022 Qing (10.1016/j.ecoinf.2023.102113_bb0155) 2018; 148 Benito-Picazo (10.1016/j.ecoinf.2023.102113_bb0035) 2018 Mamdouh (10.1016/j.ecoinf.2023.102113_bb0105) 2022 Valenčič (10.1016/j.ecoinf.2023.102113_bb0220) 2021; 26 Institute for Agricultural and Fisheries Research and Training (IFAPA) (10.1016/j.ecoinf.2023.102113_bb0080) Digi-Key Electronics (10.1016/j.ecoinf.2023.102113_bb0055) NanoPi (10.1016/j.ecoinf.2023.102113_bb0120) Observatory of Economic Complexity (OEC) (10.1016/j.ecoinf.2023.102113_bb0135) Qin (10.1016/j.ecoinf.2023.102113_bb0150) 2017 Shastri (10.1016/j.ecoinf.2023.102113_bb0185) 2020; 140 Shovon (10.1016/j.ecoinf.2023.102113_bb0195) Garbin (10.1016/j.ecoinf.2023.102113_bb0070) 2020; 79 Raspberry Pi (10.1016/j.ecoinf.2023.102113_bb0165) Atef (10.1016/j.ecoinf.2023.102113_bb0010) 2021 Du (10.1016/j.ecoinf.2023.102113_bb0060) 2020; 388 Fang (10.1016/j.ecoinf.2023.102113_bb0065) 2019; 85 Belavadi (10.1016/j.ecoinf.2023.102113_bb0030) 2020; 170 Kalamatianos (10.1016/j.ecoinf.2023.102113_bb0085) 2017 UCI Machine Learning Repository (10.1016/j.ecoinf.2023.102113_bb0210) Cho (10.1016/j.ecoinf.2023.102113_bb0040) 2014 Zhang (10.1016/j.ecoinf.2023.102113_bb0235) 2018 UNESCO World Heritage Convention (10.1016/j.ecoinf.2023.102113_bb0215) Zhang (10.1016/j.ecoinf.2023.102113_bb0230) 2017 Politis (10.1016/j.ecoinf.2023.102113_bb0145) Pinheiro (10.1016/j.ecoinf.2023.102113_bb0140) 2020; 10 Rybalkin (10.1016/j.ecoinf.2023.102113_bb0170) 2020; 92 Sparkfun (10.1016/j.ecoinf.2023.102113_bb0200) Yu (10.1016/j.ecoinf.2023.102113_bb0225) 2019; 18 Nvidia Developer (10.1016/j.ecoinf.2023.102113_bb0130) Laubscher (10.1016/j.ecoinf.2023.102113_bb0090) 2019; 189 Baharani (10.1016/j.ecoinf.2023.102113_bb0015) 2022; 21 Liu (10.1016/j.ecoinf.2023.102113_bb0095) 2017 10.1016/j.ecoinf.2023.102113_bb0100 Mealey (10.1016/j.ecoinf.2023.102113_bb0110) 2018 Shih (10.1016/j.ecoinf.2023.102113_bb0190) 2019; 108 |
| References_xml | – year: 2023 ident: bb0135 article-title: Olive oil (HS: 1510) Product Trade, Exporters and Importers – ident: bb0195 article-title: Raspberry Pi 3 Power Requirements – year: 2023 ident: bb0200 article-title: NVIDIA Jetson Nano 2GB Developer kit – volume: 92 start-page: 1219 year: 2020 end-page: 1245 ident: bb0170 article-title: Efficient hardware architectures for 1D-and MD-LSTM networks publication-title: J. Sig. Process Syst. – volume: 21 start-page: 3973 year: 2021 ident: bb0045 article-title: Forecasting air temperature on edge devices with embedded AI publication-title: Sensors (Basel) – year: 2018 ident: bb0035 article-title: Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras publication-title: Proc of IEEE International Joint Conference on Neural Networks (IJCNN) – volume: 388 year: May 2020 ident: bb0060 article-title: Multivariate time series forecasting via attention-based encoder-decoder framework publication-title: Neurocomputing – volume: 593 start-page: 364 year: 2022 end-page: 384 ident: bb0180 article-title: MLTF: model less time-series forecasting publication-title: Inf. Sci. – year: 2014 ident: bb0040 article-title: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation – year: 2017 ident: bb0085 article-title: Microclimates and their stochastic effect on olive fruit Fly evolution: modeling and simulation publication-title: Proc. 8th International Conference on Information and Communication Technologies in Agriculture, Food & Environment (HAICTA) – year: 2023 ident: bb0215 article-title: The Olive Grove Landscapes of Andalusia – year: 2017 ident: bb0150 article-title: A dual-stage attention-based recurrent neural network for time series prediction publication-title: Proc of 26 International Joint Conference on Artificial Intelligence (IJCAI-17) – reference: N. Mamdouh and A. Khattab, “YOLO-based deep Learning framework for olive fruit Fly detection and counting,” IEEE Access, vol. 9, pp.84252–84262, June 2021. – year: 2018 ident: bb0235 article-title: The implementation of CNN-based object detector on ARM embedded platforms publication-title: Proc. of IEEE 16th International Conference on Dependable, Autonomic and Secure Computing – volume: 9 year: Dec. 2019 ident: bb0175 article-title: Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems publication-title: Sci. Rep. – year: 2023 ident: bb0165 article-title: Raspberry Pi documentation – year: 2017 ident: bb0230 article-title: Hello edge: keyword spotting on microcontrollers publication-title: Computing Research Repository (CoRR) – start-page: 1062 year: Aug. 2021 end-page: 1065 ident: bb0010 article-title: Deep learning based time-series forecasting framework for olive precision farming publication-title: IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) – year: 2023 ident: bb0130 article-title: Jetson Nano Developer Kit – year: Apr. 2023 ident: bb0145 article-title: Greek weather data – volume: 10 year: Nov. 2020 ident: bb0140 article-title: Side effects of pesticides on the olive fruit Fly parasitoid Psyttalia concolor (Szépligeti): A Review publication-title: Agronomy – year: 2023 ident: bb0120 article-title: NanoPi2 – volume: 85 start-page: 533 year: 2019 end-page: 542 ident: bb0065 article-title: Performance enhancing techniques for deep learning models in time series forecasting publication-title: Eng. Appl. Artif. Intell. – volume: 108 start-page: 1421 year: 2019 end-page: 1441 ident: bb0190 article-title: Temporal pattern attention for multivariate time series forecasting publication-title: Mach. Learn. – year: 2023 ident: bb0080 article-title: Andalusian Agroclimatic Information Network (RIA) – year: 2021 ident: bb0025 article-title: MicroNets: neural network architectures for deploying TinyML applications on commodity microcontrollers publication-title: Computing Research Repository (CoRR) – year: 2023 ident: bb0055 article-title: RASPBERRY PI 3 – volume: 21 start-page: 1 year: September 2022 end-page: 19 ident: bb0015 article-title: ATCN: resource-efficient processing of time series on edge publication-title: ACM Trans. Embed. Comput. Syst. – volume: 18 year: Oct. 2019 ident: bb0225 article-title: TF-Net: deploying sub-byte deep neural networks on microcontrollers publication-title: Associat. Comp. Mach. Transact. Embedded Comput. Syst. – volume: 189 year: 2019 ident: bb0090 article-title: Time-series forecasting of coal-fired power plant reheater metal temperatures using encoder-decoder recurrent neural networks publication-title: Energy – volume: 26 year: 2021 ident: bb0220 article-title: The effect of olive fruit Fly Bactrocera oleae (Rossi) infestation on certain chemical parameters of produced olive oils publication-title: Molecules – year: 2022 ident: bb0240 article-title: Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures – volume: 170 start-page: 241 year: Jan. 2020 end-page: 248 ident: bb0030 article-title: Air quality forecasting using LSTM RNN and wireless sensor networks publication-title: Procedia Comp. Sci. – year: 2022 ident: bb0105 article-title: Artificial intelligence based detection and counting of olive fruit flies: a comprehensive survey publication-title: Deep Learning for Sustainable Agriculture – year: 2023 ident: bb0005 article-title: Meet raspberry silicon: raspberry Pi Pico now on sale at $4 – volume: 79 start-page: 1 year: May 2020 end-page: 39 ident: bb0070 article-title: Dropout vs. batch normalization: an empirical study of their impact to deep learning publication-title: Multimed. Tools Appl. – year: 2018 ident: bb0110 article-title: Binary Recurrent Unit: Using FPGA Hardware to Accelerate Inference in Long Short-Term Memory Neural Networks – year: 2021 ident: bb0125 article-title: Quantization and deployment of deep neural networks on microcontrollers publication-title: Computing Research Repository (CoRR) – year: 2023 ident: bb0210 article-title: Beijing pm2.5 data dataset – volume: 24 year: Feb. 2021 ident: bb0075 article-title: Deep learning-based effective fine-grained weather forecasting model publication-title: Pattern. Anal. Applic. – year: 2023 ident: bb0050 article-title: Dev Board – year: 2023 ident: bb0115 article-title: Why Farmers Today Need to Take up Precision Farming – year: 2023 ident: bb0160 article-title: Raspberry Pi 3 model B – volume: 148 year: 2018 ident: bb0155 article-title: Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM publication-title: Energy – year: 2023 ident: bb0205 article-title: STM32 Nucleo-64 Development Board with STM32F446RE MCU, Supports Arduino and ST Morpho Connectivity – year: 2014 ident: bb0020 article-title: Neural machine translation by jointly learning to align and translate publication-title: Computing Research Repository (CoRR) – year: Nov. 2017 ident: bb0095 article-title: Multi-step ahead time series forecasting for different data patterns based on LSTM recurrent neural network publication-title: Proc. 14th Web Information Systems and Applications Conference (WISA) – volume: 140 start-page: 1 year: Aug. 2020 end-page: 10 ident: bb0185 article-title: Time series forecasting of Covid-19 using deep Learning models: India-USA comparative case study publication-title: Chaos, Solitons Fractals – year: 2017 ident: 10.1016/j.ecoinf.2023.102113_bb0085 article-title: Microclimates and their stochastic effect on olive fruit Fly evolution: modeling and simulation – ident: 10.1016/j.ecoinf.2023.102113_bb0115 – volume: 21 start-page: 1 issue: 5 year: 2022 ident: 10.1016/j.ecoinf.2023.102113_bb0015 article-title: ATCN: resource-efficient processing of time series on edge publication-title: ACM Trans. Embed. Comput. Syst. doi: 10.1145/3524070 – ident: 10.1016/j.ecoinf.2023.102113_bb0165 – volume: 92 start-page: 1219 issue: 11 year: 2020 ident: 10.1016/j.ecoinf.2023.102113_bb0170 article-title: Efficient hardware architectures for 1D-and MD-LSTM networks publication-title: J. Sig. Process Syst. doi: 10.1007/s11265-020-01554-x – ident: 10.1016/j.ecoinf.2023.102113_bb0120 – volume: 593 start-page: 364 year: 2022 ident: 10.1016/j.ecoinf.2023.102113_bb0180 article-title: MLTF: model less time-series forecasting publication-title: Inf. Sci. doi: 10.1016/j.ins.2022.02.007 – volume: 26 year: 2021 ident: 10.1016/j.ecoinf.2023.102113_bb0220 article-title: The effect of olive fruit Fly Bactrocera oleae (Rossi) infestation on certain chemical parameters of produced olive oils publication-title: Molecules – year: 2014 ident: 10.1016/j.ecoinf.2023.102113_bb0040 – ident: 10.1016/j.ecoinf.2023.102113_bb0055 – ident: 10.1016/j.ecoinf.2023.102113_bb0130 – volume: 9 year: 2019 ident: 10.1016/j.ecoinf.2023.102113_bb0175 article-title: Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems publication-title: Sci. Rep. doi: 10.1038/s41598-019-55320-6 – volume: 85 start-page: 533 year: 2019 ident: 10.1016/j.ecoinf.2023.102113_bb0065 article-title: Performance enhancing techniques for deep learning models in time series forecasting publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2019.07.011 – ident: 10.1016/j.ecoinf.2023.102113_bb0100 doi: 10.1109/ACCESS.2021.3088075 – year: 2017 ident: 10.1016/j.ecoinf.2023.102113_bb0150 article-title: A dual-stage attention-based recurrent neural network for time series prediction – ident: 10.1016/j.ecoinf.2023.102113_bb0145 – volume: 108 start-page: 1421 year: 2019 ident: 10.1016/j.ecoinf.2023.102113_bb0190 article-title: Temporal pattern attention for multivariate time series forecasting publication-title: Mach. Learn. doi: 10.1007/s10994-019-05815-0 – ident: 10.1016/j.ecoinf.2023.102113_bb0080 – year: 2017 ident: 10.1016/j.ecoinf.2023.102113_bb0095 article-title: Multi-step ahead time series forecasting for different data patterns based on LSTM recurrent neural network – ident: 10.1016/j.ecoinf.2023.102113_bb0160 – ident: 10.1016/j.ecoinf.2023.102113_bb0205 – year: 2014 ident: 10.1016/j.ecoinf.2023.102113_bb0020 article-title: Neural machine translation by jointly learning to align and translate – year: 2018 ident: 10.1016/j.ecoinf.2023.102113_bb0235 article-title: The implementation of CNN-based object detector on ARM embedded platforms – volume: 189 year: 2019 ident: 10.1016/j.ecoinf.2023.102113_bb0090 article-title: Time-series forecasting of coal-fired power plant reheater metal temperatures using encoder-decoder recurrent neural networks publication-title: Energy doi: 10.1016/j.energy.2019.116187 – volume: 24 year: 2021 ident: 10.1016/j.ecoinf.2023.102113_bb0075 article-title: Deep learning-based effective fine-grained weather forecasting model publication-title: Pattern. Anal. Applic. doi: 10.1007/s10044-020-00898-1 – year: 2022 ident: 10.1016/j.ecoinf.2023.102113_bb0105 article-title: Artificial intelligence based detection and counting of olive fruit flies: a comprehensive survey – year: 2021 ident: 10.1016/j.ecoinf.2023.102113_bb0125 article-title: Quantization and deployment of deep neural networks on microcontrollers – year: 2022 ident: 10.1016/j.ecoinf.2023.102113_bb0240 – start-page: 1062 year: 2021 ident: 10.1016/j.ecoinf.2023.102113_bb0010 article-title: Deep learning based time-series forecasting framework for olive precision farming – year: 2018 ident: 10.1016/j.ecoinf.2023.102113_bb0110 – volume: 140 start-page: 1 year: 2020 ident: 10.1016/j.ecoinf.2023.102113_bb0185 article-title: Time series forecasting of Covid-19 using deep Learning models: India-USA comparative case study publication-title: Chaos, Solitons Fractals doi: 10.1016/j.chaos.2020.110227 – year: 2021 ident: 10.1016/j.ecoinf.2023.102113_bb0025 article-title: MicroNets: neural network architectures for deploying TinyML applications on commodity microcontrollers – volume: 10 year: 2020 ident: 10.1016/j.ecoinf.2023.102113_bb0140 article-title: Side effects of pesticides on the olive fruit Fly parasitoid Psyttalia concolor (Szépligeti): A Review publication-title: Agronomy doi: 10.3390/agronomy10111755 – year: 2017 ident: 10.1016/j.ecoinf.2023.102113_bb0230 article-title: Hello edge: keyword spotting on microcontrollers – ident: 10.1016/j.ecoinf.2023.102113_bb0005 – volume: 21 start-page: 3973 issue: 12 year: 2021 ident: 10.1016/j.ecoinf.2023.102113_bb0045 article-title: Forecasting air temperature on edge devices with embedded AI publication-title: Sensors (Basel) doi: 10.3390/s21123973 – ident: 10.1016/j.ecoinf.2023.102113_bb0200 – ident: 10.1016/j.ecoinf.2023.102113_bb0050 – volume: 18 issue: 45 year: 2019 ident: 10.1016/j.ecoinf.2023.102113_bb0225 article-title: TF-Net: deploying sub-byte deep neural networks on microcontrollers publication-title: Associat. Comp. Mach. Transact. Embedded Comput. Syst. – ident: 10.1016/j.ecoinf.2023.102113_bb0215 – ident: 10.1016/j.ecoinf.2023.102113_bb0135 – volume: 148 year: 2018 ident: 10.1016/j.ecoinf.2023.102113_bb0155 article-title: Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM publication-title: Energy doi: 10.1016/j.energy.2018.01.177 – volume: 79 start-page: 1 year: 2020 ident: 10.1016/j.ecoinf.2023.102113_bb0070 article-title: Dropout vs. batch normalization: an empirical study of their impact to deep learning publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-019-08453-9 – ident: 10.1016/j.ecoinf.2023.102113_bb0195 – volume: 170 start-page: 241 year: 2020 ident: 10.1016/j.ecoinf.2023.102113_bb0030 article-title: Air quality forecasting using LSTM RNN and wireless sensor networks publication-title: Procedia Comp. Sci. doi: 10.1016/j.procs.2020.03.036 – volume: 388 year: 2020 ident: 10.1016/j.ecoinf.2023.102113_bb0060 article-title: Multivariate time series forecasting via attention-based encoder-decoder framework publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.12.118 – year: 2018 ident: 10.1016/j.ecoinf.2023.102113_bb0035 article-title: Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras – ident: 10.1016/j.ecoinf.2023.102113_bb0210 |
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| SubjectTerms | Deep learning Encoder-decoder Long short-term memory Precision agriculture Time series forecasting |
| Title | A low footprint olive grove weather forecasting using a single-layered seasonal attention encoder-decoder model |
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