Computer Vision for Monitor and Control of Vertical Farms Using Machine Learning Methods

Obu, Ubio; Sarkarkar, Gopal; Ambekar, Yash

doi:10.1109/iccica52458.2021.9697152

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Reinforcement learning has been adopted by the authors for finding the phenotyping of the crops chili, beans, potatoes, and onions with a prediction accuracy of 83.563%. This work extracted plant leaves as the observed features for learning purposes [25]. The authors in the article [26] analyzed the effectiveness of the random forest regression model in predicting the aeroponic lettuce crop yield.…”

Section: Survey Of Literaturementioning

confidence: 99%

Optimizing Lettuce Crop Yield Prediction in an Indoor Aeroponic Vertical Farming System Using IoT-Integrated Machine Learning Regression Models

Rajendiran,

Rethnaraj

2024

RIA

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Section: Survey Of Literaturementioning

confidence: 99%

Optimizing Lettuce Crop Yield Prediction in an Indoor Aeroponic Vertical Farming System Using IoT-Integrated Machine Learning Regression Models

Rajendiran,

Rethnaraj

2024

RIA

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Development of Vertical Indoor Farming Using an Arduino and IoT

Nor,

Sani,

Yusof

et al. 2024

SpringerBriefs in Applied Sciences and Technology

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Technological Trends and Engineering Issues on Vertical Farms: A Review

Kabir,

Reza,

Chowdhury

et al. 2023

Horticulturae

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Vertical farming has emerged as a promising solution to cope with increasing food demand, urbanization pressure, and limited resources and to ensure sustainable year-round urban agriculture. The aim of this review was to investigate the evolving technological landscape and engineering considerations, with a focus on innovative developments and future prospects. This paper presents technological trends in vertical farming, covering advances in sensing technologies, monitoring and control systems, and unmanned systems. It also highlights the growing role of artificial intelligence (AI) in contributing to data-driven decision-making and the optimization of vertical farms. A global perspective on vertical farming is presented, including the current status and advanced technological trends across regions like Asia, the USA, and Europe. Innovative concepts and upcoming enterprises that could shape the future of vertical agriculture are explored. Additionally, the challenges and future prospects of vertical farming are also addressed, focusing on crop production limitations, environmental sustainability, economic feasibility, and contributions to global food security. This review provides guidance on the state of vertical farming, technological advancements, global trends, challenges, and prospects, offering insights into the roles of researchers, practitioners, and policymakers in advancing sustainable vertical agriculture and food security.

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Computer Vision for Monitor and Control of Vertical Farms Using Machine Learning Methods

Cited by 3 publications

References 11 publications

Optimizing Lettuce Crop Yield Prediction in an Indoor Aeroponic Vertical Farming System Using IoT-Integrated Machine Learning Regression Models

Optimizing Lettuce Crop Yield Prediction in an Indoor Aeroponic Vertical Farming System Using IoT-Integrated Machine Learning Regression Models

Development of Vertical Indoor Farming Using an Arduino and IoT

Technological Trends and Engineering Issues on Vertical Farms: A Review

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