AgricultureAgriculture plays a vital role in the economic development of nations. HydroponicsHydroponics, a soil-less cultivationSoil-less cultivation technique utilizing nutrient solutionsNutrient solutions, has gained popularity as an innovative agricultural strategyInnovative agricultural strategy. This research focuses on enhancing hydroponic plant growthHydroponic plant growth through the integration of machine learning algorithmsMachine learning algorithms, such as neural networks and Bayesian networks (Banerjee T, Roy S in Deep neural network fitting model for hydroponics system, IEEE, pp 1–6 (2019); Deepak B, Singh R in IoT-based smart agriculture: a review on smart hydroponic systems. IEEE, pp 1–6 (2021)) [1, 2]. Additionally, leveraging the Internet of Things (IoTInternet of Things (IoT)), we aim to develop an intelligent system capable of autonomous control based on diverse input data. HydroponicsHydroponics offers the potential for soil-less plant cultivation, provided specific requirements are met. By optimizing nutrient solutionsNutrient solutions and environmental variables, plants can thrive without traditional soil-based methods. Machine learning algorithmsMachine learning algorithms, such as neural networks and Bayesian networks, have been extensively investigated to improve hydroponicHydroponics growth conditions. These algorithms analyze various input data, identify patterns, and predict optimal control actions for maintaining an ideal hydroponicHydroponics environment. The IoTInternet of Things (IoT) enables machine-to-machine communication, allowing for independent and intelligent control of hydroponicHydroponics systems. By integrating IoTInternet of Things (IoT) devices, sensors, and actuators into the hydroponicHydroponics setup, real-time data collection and feedback can be achieved. This data is then processed by machine learning algorithmsMachine learning algorithms to make informed decisions regarding nutrient composition, pH levels, temperature, humidity, and lighting conditions.

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Enhancing Hydroponic Efficiency Using IoT and Machine Learning Algorithms

  • R. Punith Raj,
  • R. Shushank,
  • S. Vaibhav,
  • M. Shahina Parveen

摘要

AgricultureAgriculture plays a vital role in the economic development of nations. HydroponicsHydroponics, a soil-less cultivationSoil-less cultivation technique utilizing nutrient solutionsNutrient solutions, has gained popularity as an innovative agricultural strategyInnovative agricultural strategy. This research focuses on enhancing hydroponic plant growthHydroponic plant growth through the integration of machine learning algorithmsMachine learning algorithms, such as neural networks and Bayesian networks (Banerjee T, Roy S in Deep neural network fitting model for hydroponics system, IEEE, pp 1–6 (2019); Deepak B, Singh R in IoT-based smart agriculture: a review on smart hydroponic systems. IEEE, pp 1–6 (2021)) [1, 2]. Additionally, leveraging the Internet of Things (IoTInternet of Things (IoT)), we aim to develop an intelligent system capable of autonomous control based on diverse input data. HydroponicsHydroponics offers the potential for soil-less plant cultivation, provided specific requirements are met. By optimizing nutrient solutionsNutrient solutions and environmental variables, plants can thrive without traditional soil-based methods. Machine learning algorithmsMachine learning algorithms, such as neural networks and Bayesian networks, have been extensively investigated to improve hydroponicHydroponics growth conditions. These algorithms analyze various input data, identify patterns, and predict optimal control actions for maintaining an ideal hydroponicHydroponics environment. The IoTInternet of Things (IoT) enables machine-to-machine communication, allowing for independent and intelligent control of hydroponicHydroponics systems. By integrating IoTInternet of Things (IoT) devices, sensors, and actuators into the hydroponicHydroponics setup, real-time data collection and feedback can be achieved. This data is then processed by machine learning algorithmsMachine learning algorithms to make informed decisions regarding nutrient composition, pH levels, temperature, humidity, and lighting conditions.