Abstract
There is an increasing demand for healthy and fresh foods, and predicting yield effectively is important to improve production, especially in methods like aeroponics. This paper has two main goals: (i) use data fusion to improve yield prediction in aeroponics, and (ii) find which features are more relevant for yield prediction of six different crops. To reach these goals, a number of artificial intelligence models and an interpretability analysis based on SHapley Additive exPlanations (SHAP) have been implemented. The models were trained using 200 samples that were collected in a nine-month period, including information from different air and water quality sensors in addition to manually recorded data, reaching in the end a coefficient of determination value \(R^{2}\) = 0.752 for the validation dataset in the best case (CNN-based model). As a result, two main features were identified in the dataset: Room \(CO_{2}\) and Reservoir Temperature, along with other useful insights of how these features influence predictions. SHAP values also provided important information for feature selection. These results could be the first steps towards the full automation of an aeroponics crop production system.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and materials
The datasets generated and analyzed during the current study are available in the Mendeley repository, https://dx.doi.org/10.17632/wmyktpx9hv.1
References
Argo BD, Hendrawan Y, Ubaidillah U (2019) A fuzzy micro-climate controller for small indoor aeroponics systems. TELKOMNIKA 17(6):3019–3026. https://doi.org/10.12928/telkomnika.v17i6.12214
Ariza-Garzón MJ, Arroyo J, Caparrini A, Segovia-Vargas MJ (2020) Explainability of a machine learning granting scoring model in peer-to-peer lending. IEEE Access 8:64873–64890. https://doi.org/10.1109/ACCESS.2020.2984412
Carlsson LS, Samuelsson PB, Jönsson PG (2020) Interpretable machine learning—tools to interpret the predictions of a machine learning model predicting the electrical energy consumption of an electric arc furnace. Steel Res Int. https://doi.org/10.1002/srin.202000053
Castro-Zunti R, Chae K, Choi Y, Jin G, Ko S (2021) Assessing the speed-accuracy trade-offs of popular convolutional neural networks for single-crop rib fracture classification. Elsevier Computerized Medical Imaging and Graphics [Accepted]
Chandra S, Khan S, Avula B, Lata H, Yang MH, Elsohly MA, Khan IA (2014) Assessment of total phenolic and flavonoid content, antioxidant properties, and yield of aeroponically and conventionally grown leafy vegetables and fruit crops: a comparative study. Evidence-Based Complementary and Alternative Medicine: eCAM 2014. https://doi.org/10.1155/2014/253875
Francis F, Vishnu PL, Jha M, Rajaram B (2018) IOT-based automated aeroponics system. In: Thalmann D, Subhashini N, Mohanaprasad K, Murugan MSB (eds) Intelligent embedded systems. Lecture Notes in Electrical Engineering. Springer, Singapore, pp 337–345, https://doi.org/10.1007/978-981-10-8575-8_32
Goldstein A, Fink L, Meitin A, Bohadana S, Lutenberg O, Ravid G (2018) Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge. Precision Agric 19(3):421–444. https://doi.org/10.1007/s11119-017-9527-4
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. The MIT Press, Cambridge
Guidotti R, Monreale A, Giannotti F, Pedreschi D, Ruggieri S, Turini F (2019) Factual and counterfactual explanations for black box decision making. IEEE Intell Syst 34(6):14–23. https://doi.org/10.1109/MIS.2019.2957223
Guillemé M, Masson V, Rozé L, Termier A (2019) Agnostic Local Explanation for Time Series Classification. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp 432–439. https://doi.org/10.1109/ICTAI.2019.00067
Ivanov S, Bhargava K, Donnelly W (2015) Precision farming: sensor analytics. IEEE Intell Syst 30(4):76–80. https://doi.org/10.1109/MIS.2015.67
Jiang JA, Liao MS, Lin TS, Huang CK, Chou CY, Yeh SH, Lin TT, Fang W (2018) Toward a higher yield: a wireless sensor network-based temperature monitoring and fan-circulating system for precision cultivation in plant factories. Precision Agric 19(5):929–956. https://doi.org/10.1007/s11119-018-9565-6
Jiang Z, Yepez J, An S, Ko S (2017) Fast, accurate and robust retinal vessel segmentation system. Biocybern Biomed Eng 37(3):412–421. https://doi.org/10.1016/j.bbe.2017.04.001
Lakhiar IA, Jianmin G, Syed TN, Chandio FA, Buttar NA, Qureshi WA (2018) Monitoring and control systems in agriculture using intelligent sensor techniques: a review of the aeroponic system. J Sens 2018:e8672769. https://doi.org/10.1155/2018/8672769
Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791
Li Z, Nie F, Chang X, Nie L, Zhang H, Yang Y (2018) Rank-constrained spectral clustering with flexible embedding. IEEE Trans Neural Netw Learn Syst 29(12):6073–6082. https://doi.org/10.1109/TNNLS.2018.2817538
Li Z, Nie F, Chang X, Yang Y, Zhang C, Sebe N (2018) Dynamic affinity graph construction for spectral clustering using multiple features. IEEE Trans Neural Netw Learn Syst 29(12):6323–6332. https://doi.org/10.1109/TNNLS.2018.2829867
Liu S, Guo L, Webb H, Ya X, Chang X (2019) Internet of Things Monitoring System of Modern Eco-Agriculture Based on Cloud Computing. IEEE Access 7:37050–37058. https://doi.org/10.1109/ACCESS.2019.2903720
Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in Neural Information Processing Systems 30, Curran Associates, Inc., pp 4765–4774
Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Liston DE, Low DKW, Newman SF, Kim J, Lee SI (2018) Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng 2(10):749–760. https://doi.org/10.1038/s41551-018-0304-0
Luo M, Chang X, Li Z, Nie L, Hauptmann AG, Zheng Q (2017) Simple to complex cross-modal learning to rank. Comput Vis Image Underst 163:67–77. https://doi.org/10.1016/j.cviu.2017.07.001
Mehra M, Saxena S, Sankaranarayanan S, Tom RJ, Veeramanikandan M (2018) IoT based hydroponics system using deep neural networks. Comput Electron Agric 155:473–486. https://doi.org/10.1016/j.compag.2018.10.015
Raschka S (2015) Python machine learning, 1st edn. Packt Publishing, Birmingham Mumbai
Thompson HC, Langhans RW, Both AJ, Albright LD (1998) Shoot and root temperature effects on lettuce growth in a floating hydroponic system. J Am Soc Hortic Sci 123(3):361–364. https://doi.org/10.21273/JASHS.123.3.361
Torres-Tello J, Guamán AV, Ko SB (2020) Improving the detection of explosives in a MOX chemical sensors array with LSTM networks. IEEE Sens J 20(23):14302–14309. https://doi.org/10.1109/JSEN.2020.3007431
Torres-Tello J, Venkatachalam S, Moreno L, Ko SB (2020b) Ensemble learning for improving generalization in aeroponics yield prediction. In: 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Spain, pp 1–5. https://doi.org/10.1109/ISCAS45731.2020.9181283
Torres-Tello J, Venkatachalam S, Moreno L, Ko SB (2020c) Yield measurements in aeroponics for six different crops. Mendeley Data (V1). https://doi.org/10.17632/wmyktpx9hv.1
Wang Y, Choi EJ, Choi Y, Zhang H, Jin GY, Ko SB (2020) Breast cancer classification in automated breast ultrasound using multiview convolutional neural network with transfer learning. Ultrasound Med Biol 46(5):1119–1132. https://doi.org/10.1016/j.ultrasmedbio.2020.01.001
Wang Y, Zhang H, Chae KJ, Choi Y, Jin GY, Ko SB (2020) Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography. Multidimens Syst Signal Process 31(3):1163–1183. https://doi.org/10.1007/s11045-020-00703-6
Ziska LH (2008) Rising atmospheric carbon dioxide and plant biology: the overlooked paradigm. DNA Cell Biol 27(4):165–172. https://doi.org/10.1089/dna.2007.0726
Funding
The work of Julio Torres-Tello was supported by the scholarship CZ02-000745-2018 from Secretaría de Educación Superior, Ciencia, Tecnología e Innovación SENESCYT.
Author information
Authors and Affiliations
Contributions
Conceptualization: Julio Torres-Tello; Methodology: Julio Torres-Tello; Software: Julio Torres-Tello; Validation: Julio Torres-Tello; Data Curation: Julio Torres-Tello; Writing - Original Draft: Julio Torres-Tello; Visualization: Julio Torres-Tello; Resources: Seok-Bum Ko; Writing - Review & Editing: Seok-Bum Ko; Supervision: Seok-Bum Ko; Project administration: Seok-Bum Ko.
Corresponding author
Ethics declarations
Conflict of interest
Julio Torres-Tello is currently affiliated with the University of Saskatchewan, Canada, and the Universidad de las Fuerzas Armadas ESPE, Ecuador.
Code availability
The code generated during the current study is available in the GitHub repository, https://github.com/juliotorrest/yield_aeroponics.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Torres-Tello, J., Ko, SB. Interpretability of artificial intelligence models that use data fusion to predict yield in aeroponics. J Ambient Intell Human Comput 14, 3331–3342 (2023). https://doi.org/10.1007/s12652-021-03470-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03470-9