Comparative analysis of data using machine learning algorithms: A hydroponics system use case

Idoje, Godwin; Mouroutoglou, Christos; Dagiuklas, Tasos; Kotsiras, Anastasios; Muddesar, Iqbal; Alefragkis, Panagiotis

doi:10.1016/j.atech.2023.100207

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…This result was obtained by practically cultivating onion bulbs in the soil as well as in a smart DWC hydroponics setup and then verifying and validating the results with the results obtained from the 'AquaCrop' simulator. The previous works done in this regard have focused on the prediction of onion bulbs in hydroponics [65]. The authors of this research work have considered the onion as a medicinal plant whose shoot as well as the bulb contains medicinal properties, thus the growth responses of the shoot as well as the bulb of the onion were monitored and enhanced with the use of DWC hydroponics, which makes the work novel.…”

Section: Discussionmentioning

confidence: 99%

Comparative Growth Analysis of Onion in Deep Water Culture and Soil Based Systems: Enhancing Medicinal Plant Cultivation in Urbanized Environments

Dutta,

Gupta,

Juneja

et al. 2024

IEEE Access

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Medicinal plants play a vital role in treating human and livestock ailments. A majority of the population living across the world relies on medicinal plants as a primary source of healthcare. The rural as well as the urban habitats depend on medicinal plants. Medicinal plants may be of root type or shoot type. Onion is one of the root types of medicinal plants that has abundant health benefits as well as economic value. Due to increasing urbanization, the availability of agricultural land is decreasing rapidly which is causing an increase in the demand for medicinal plants in a reduced cultivation area. This has led to the cultivation of soilless smart precision farming methods. Sensors are used to monitor and control the environmental factors affecting the growth and yield of agricultural produce. Among all the vertical farming methods, hydroponics is found to be the best way for cultivating herbaceous plants. This article deals with the comparison of growth responses of hydroponically grown onion (Allium cepa) in a Deep-Water Culture (DWC) setup, as compared to that cultivated in soil. A systematic literature survey of onion grown in a hydroponic cultivation system is carried out. Finally, the growth responses of hydroponic and soil-grown onions are checked and compared with the results obtained from the 'AquaCrop' simulator. Results of the actual and simulator are verified and validated, and it is found that the actual and the simulation results are similar.INDEX TERMS hydroponics, medicinal plants, onion (Allium cepa), smart precision farming methods, urbanization.

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Section: Discussionmentioning

confidence: 99%

Comparative Growth Analysis of Onion in Deep Water Culture and Soil Based Systems: Enhancing Medicinal Plant Cultivation in Urbanized Environments

Dutta,

Gupta,

Juneja

et al. 2024

IEEE Access

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“…The federated deep learning approach enhances resource usage and data privacy, leading to classification results comparable to the fundamental ML setup. Applying this sophisticated learning method involves incorporating IoT technology to identify crop diseases precisely [590], [591]. Furthermore, encryption techniques can be employed when sharing trained models to address privacy issues in the federated setting [592].…”

Section: ) Federated Learning (Fl)mentioning

confidence: 99%

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Mowla,

Shah

et al. 2023

IEEE Access

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The increasing food scarcity necessitates sustainable agriculture achieved through automation to meet the growing demand. Integrating the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) is crucial in enhancing food production across various agricultural domains, encompassing irrigation, soil moisture monitoring, fertilizer optimization and control, early-stage pest and crop disease management, and energy conservation. Wireless application protocols such as ZigBee, WiFi, SigFox, and LoRaWAN are commonly employed to collect real-time data for monitoring purposes. Embracing advanced technology is imperative to ensure efficient annual production. Therefore, this study emphasizes a comprehensive, futureoriented approach, delving into IoT-WSNs, wireless network protocols, and their applications in agriculture since 2019. It thoroughly discusses the overview of IoT and WSNs, encompassing their architectures and summarization of network protocols. Furthermore, the study addresses recent issues and challenges related to IoT-WSNs and proposes mitigation strategies. It provides clear recommendations for the future, emphasizing the integration of advanced technology aiming to contribute to the future development of smart agriculture systems. INDEX TERMSInternet of things, wireless sensor networks, wireless network protocols, smart agriculture applications.

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“…The result outcome of the proposed LCGM-boost regression model were comparatively analyzed with the other regression models such as Support vector regressor [34], Random forest [35], XGBoost [36] respectively as shown in Table 3.…”

Section: Comparative Analysis Of Lcgm-boost Regression Model With Oth...mentioning

confidence: 99%

“…The proposed work was compared with the other regression models, such as support vector regressor [34] where, the authors used three different scenarios for predicting the yield. The proposed model was compared with random forest model [35], and XGBoost regression model [36].…”

Section: Comparative Analysis Of Lcgm-boost Regression Model With Oth...mentioning

confidence: 99%

Smart Aeroponic Farming System: Using IoT with LCGM-Boost Regression Model for Monitoring and Predicting Lettuce Crop Yield

2023

IJIES

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Aeroponics is a popular soilless crop cultivation technology that integrates plant nutrition, physiology, and ecological control. It offers automated monitoring, protected cultivation, improved growth mechanisms, better yield and requires less maintenance. Here, to predict the crop yield, two systems are available: manual and automated. Manual systems often fail to produce better prediction results, leading to substantial crop losses whereas, the automated systems use machine intelligence for growth monitoring. This article proposes a lettuce crop growth monitoring-boost (LCGM-Boost) regression model for lettuce yield forecasting in aeroponic vertical farming system. This model is highly robust to outliers, produces better prediction results of 95.86% and lower error rates of 0.36 (MAE), 0.40 (MSE), and 0.63 (RMSE) than other machine learning models namely, support vector, random forest and XGBoost regressors. Hence, it is preferable for growth monitoring and yield prediction of the lettuce crop in the real-time aeroponics system.

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Comparative analysis of data using machine learning algorithms: A hydroponics system use case

Cited by 6 publications

References 27 publications

Comparative Growth Analysis of Onion in Deep Water Culture and Soil Based Systems: Enhancing Medicinal Plant Cultivation in Urbanized Environments

Comparative Growth Analysis of Onion in Deep Water Culture and Soil Based Systems: Enhancing Medicinal Plant Cultivation in Urbanized Environments

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Smart Aeroponic Farming System: Using IoT with LCGM-Boost Regression Model for Monitoring and Predicting Lettuce Crop Yield

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