An IoT-Based Data-Driven Real-Time Monitoring System for Control of Heavy Metals to Ensure Optimal Lettuce Growth in Hydroponic Set-Ups

Dhal, Sambandh Bhusan; Mahanta, Shikhadri; Gumero, Jonathan; O'Sullivan, Nick; Soetan, Morayo; Louis, Julia; Gadepally, Krishna Chaitanya; Mahanta, Snehadri; Lusher, John; Kalafatis, Stavros

doi:10.3390/s23010451

Cited by 19 publications

(8 citation statements)

References 27 publications

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“…The Extra-Trees algorithm (ERT) was utilized for estimating the yield and quality indices of forage. This ensemble method constructs multiple decision trees with random splits on features, enhancing predictive accuracy for multi-dimensional agricultural data [35]. Extra-Trees introduces greater randomness in the selection of split nodes, potentially reducing computational time and improving model performance in certain scenarios.…”

Section: Estimation Process and Evaluation Metricmentioning

confidence: 99%

Comparative Analysis of Feature Importance Algorithms for Grassland Aboveground Biomass and Nutrient Prediction Using Hyperspectral Data

Zhao,

Xu,

et al. 2024

Agriculture

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Estimating forage yield and nutrient composition using hyperspectral remote sensing is a major challenge. However, there is still a lack of comprehensive research on the optimal wavelength for the analysis of various nutrients in pasture. In this research, conducted in Hailar District, Hulunber City, Inner Mongolia Autonomous Region, China, 126 sets of hyperspectral data were collected, covering a spectral range of 350 to 1800 nanometers. The primary objective was to identify key spectral bands for estimating forage dry matter yield (DMY), nitrogen content (NC), neutral detergent fiber (NDF), and acid detergent fiber (ADF) using principal component analysis (PCA), random forests (RF), and SHapley Additive exPlanations (SHAP) analysis methods, and then the RF and Extra-Trees algorithm (ERT) model was used to predict aboveground biomass (AGB) and nutrient parameters using the optimized spectral bands and vegetation indices. Our approach effectively minimizes redundancy in hyperspectral data by selectively employing crucial spectral bands, thus improving the accuracy of forage nutrient estimation. PCA identified the most variable bands at 400 nm, 520–550 nm, 670–720 nm, and 930–950 nm, reflecting their general spectral significance rather than a link to specific forage nutrients. Further analysis using RF feature importance pinpointed influential bands, predominantly within 930–940 nm and 700–730 nm. SHAP analysis confirmed critical bands for DMY (965 nm, 712 nm, and 1652 nm), NC (1390 nm and 713 nm), ADF (1390 nm and 715–725 nm), and NDF (400 nm, 983 nm, 1350 nm, and 1800 nm). The fitting accuracy for ADF estimated using RF was lower (R2 = 0.58), while the fitting accuracy for other indicators was higher (R2 ≥ 0.59). The performance and prediction accuracy of ERT (R2 = 0.63) were noticeably superior to those of RF. In conclusion, our method effectively identifies influential bands, optimizing forage yield and quality estimation.

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Section: Estimation Process and Evaluation Metricmentioning

confidence: 99%

Comparative Analysis of Feature Importance Algorithms for Grassland Aboveground Biomass and Nutrient Prediction Using Hyperspectral Data

Zhao,

Xu,

et al. 2024

Agriculture

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“…Furthermore, a study by [17] involves monitoring nutrient concentrations (e.g., calcium, sulfate, and phosphate) in hydroponic solutions. Data is sent to an android app with a machine learning (ML) model.…”

Section: Related Workmentioning

confidence: 99%

Smart indoor gardening: elevating growth, health, and automation

Alrawashdeh,

Alkore Alshalabi,

Al-Jaafreh

et al. 2024

Bulletin EEI

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Recently, indoor systems for growing plants have emerged as a promising approach to address the problems related to extreme weather conditions outdoors. However, such systems must manage the plants surrounding environments to satisfy the environmental and the economical requirements. In this line, any proposed solution must address challenging factors such as plants diseases and unordinary climate situations. In this paper, propose an internet of things (IoT) indoor system that can be used to facilitate the plant growing process. The proposed system is designed to provide alternatives for outdoor climate dependency such as the vitamins provided through sunlight. Moreover, renewable energy sources (sunlight) are employed to reduce the impact on the environment. With the help of several types of sensors, the system continuously monitors the plants through their growing journey. Whereas actuator devices are employed to control the plant-feeding process based on the sensors’ reported values. All the collected data will be uploaded to the cloud for analysis, utilizing a website. Additionally, the architecture of the provided system eliminates the need for human involvement, which has a degrading effect on the plant growing process.

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“…This research aims to create a low-cost real-time smart sensing and actuation system for controlling heavy metal concentrations in aquaponic solutions by implementing IoT and machine learning tools. The results of the research are known concentrations from the hydroponics environment without the need to bring them to the laboratory so that they can save time and money [8] .…”

Section: Objectivementioning

confidence: 99%

A simple dataset of water quality on aquaponic fish ponds based on an internet of things measurement device

et al. 2023

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An IoT-Based Data-Driven Real-Time Monitoring System for Control of Heavy Metals to Ensure Optimal Lettuce Growth in Hydroponic Set-Ups

Cited by 19 publications

References 27 publications

Comparative Analysis of Feature Importance Algorithms for Grassland Aboveground Biomass and Nutrient Prediction Using Hyperspectral Data

Comparative Analysis of Feature Importance Algorithms for Grassland Aboveground Biomass and Nutrient Prediction Using Hyperspectral Data

Smart indoor gardening: elevating growth, health, and automation

A simple dataset of water quality on aquaponic fish ponds based on an internet of things measurement device

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