Vertical Farming Perspectives in Support of Precision Agriculture Using Artificial Intelligence: A Review

Siregar, Riki Ruli A.; Seminar, Kudang Boro; Wahjuni, Sri; Santosa, Edi

doi:10.3390/computers11090135

Cited by 42 publications

(23 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…With the rapid advancement of digital technologies, IoT devices, artificial intelligence, and other cutting-edge technologies are increasingly being incorporated into VF. According to Siregar et al [51], IoT devices and AI are being employed to monitor and control VF environments, and researchers such as those found in [52][53][54] have proposed methodologies that integrate sensors and IoT techniques for smart VF. Additionally, Kaur et al [55,56] demonstrated how hydroponic VF could be carried out using IoT-based sensors with minimal water and soil usage.…”

Section: Vertical Farming and Smart Greenhousementioning

confidence: 99%

Enhancing Safety in IoT Systems: A Model-Based Assessment of a Smart Irrigation System Using Fault Tree Analysis

Abdulhamid,

Rahman,

Kabir

et al. 2024

Electronics

View full text Add to dashboard Cite

The agricultural industry has the potential to undergo a revolutionary transformation with the use of Internet of Things (IoT) technology. Crop monitoring can be improved, waste reduced, and efficiency increased. However, there are risks associated with system failures that can lead to significant losses and food insecurity. Therefore, a proactive approach is necessary to ensure the effective safety assessment of new IoT systems before deployment. It is crucial to identify potential causes of failure and their severity from the conceptual design phase of the IoT system within smart agricultural ecosystems. This will help prevent such risks and ensure the safety of the system. This study examines the failure behaviour of IoT-based Smart Irrigation Systems (SIS) to identify potential causes of failure. This study proposes a comprehensive Model-Based Safety Analysis (MBSA) framework to model the failure behaviour of SIS and generate analysable safety artefacts of the system using System Modelling Language (SysML). The MBSA approach provides meticulousness to the analysis, supports model reuse, and makes the development of a Fault Tree Analysis (FTA) model easier, thereby reducing the inherent limitations of informal system analysis. The FTA model identifies component failures and their propagation, providing a detailed understanding of how individual component failures can lead to the overall failure of the SIS. This study offers valuable insights into the interconnectedness of various component failures by evaluating the SIS failure behaviour through the FTA model. This study generates multiple minimal cut sets, which provide actionable insights into designing dependable IoT-based SIS. This analysis identifies potential weak points in the design and provides a foundation for safety risk mitigation strategies. This study emphasises the significance of a systematic and model-driven approach to improving the dependability of IoT systems in agriculture, ensuring sustainable and safe implementation.

show abstract

Section: Vertical Farming and Smart Greenhousementioning

confidence: 99%

Enhancing Safety in IoT Systems: A Model-Based Assessment of a Smart Irrigation System Using Fault Tree Analysis

Abdulhamid,

Rahman,

Kabir

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Further, it is noticed that various survey work is carried out towards addressing various PA-based issues via. Privacy (Demestichas et al [101], Gupta et al [102]), control strategies in PA (Hassan et al [103]), emerging technologies in PA (Mesías-Ruiz et al [104], Singh et al [105]), ML-based approaches (Benos et al [106], Sharma et al [107]), ML approaches for pest and disease identification (Domingues et al [108]), IoT based approaches (Dhanaraju et al [109]), AI-based approaches for vertical farming (Holzinger et al [110], Siregar et al [111]), DL-based approaches used in PA (Altalak et al [112]). However, it has been noted that some review works have emphasized IoT-based deployment approaches towards PA [113]- [119].…”

Section: Research Gapsmentioning

confidence: 99%

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Panduranga,

Ranganathasharma

2024

IJECE

View full text Add to dashboard Cite

Precision agriculture (PA) is meant to automate the complete agricultural processes with the sole target of enhanced crop yield with reduced cost of operation. However, deployment of PA in internet of things (IoT) based architecture demands solutions towards addressing various challenges where most are related to proper and precise predictive management of agricultural data. In this perspective, it is noted that learning-based approaches have made some contributory success towards addressing different variants of issues in PA; however, such methods suffer from certain loopholes, primarily related to the non-inclusion of practical constraints of IoT infrastructure in PA and lack of emphasis towards bridging the trade-off between higher accuracy and computational burden that is eventually associated with this. This paper contributes towards highlighting the strengths and weaknesses of recent learning approaches and contributes towards novel findings.

show abstract

“…Saat ini, algoritma Learning Vector Quantization (LVQ) sebagai bagian pendekatan teknik Machine Learning (ML) telah banyak di implementasikan pada penelitian berbagai bidang di antaranya teknik lingkungan [1], [2], pembelajaran online semi-supervised [3], [4], pengenalan wajah [5] hingga pertanian [6], [7]. Deteksi dan klasifikasi adalah salah satu topik yang menarik dan banyak dibahas pada penelitian di bidang ilmu komputer.…”

Section: Pendahuluanunclassified

Klasifikasi Citra Penyakit Daun Cabai Menggunakan Algoritma Learning Vector Quantization

Siswipraptini,

Haris,

Sari

2023

FaktorExacta

View full text Add to dashboard Cite

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="388"><p>The problem often occurs in chili leaves is organisms that interfere with chili plants which can reduce chili production. There are chili plant diseases that are difficult for farmers to recognize by using their eyes and without using tools. The purpose of this study was to produce a model capable of identifying chili leaf diseases based on leaf colour in order to make it easier for farmers to identify chili leaf diseases, especially Phytophthora, Anthracnose, and Cercospora diseases, using the Learning Vector Quantization (LVQ) classification algorithm. Data was collected in the form of digital images of 30 chili leaves which were processed by resizing and transforming RGB to HSV which then proceeded to Canny Edge detection process with the aim of getting patterns from images of chili leaves. The result of testing LVQ algorithm using a confusion matrix get an accuracy of 80%, the precision value of 80%, recall value of 82%, and f-1 score of 81%.</p><p> </p></td></tr></tbody></table>

show abstract

Vertical Farming Perspectives in Support of Precision Agriculture Using Artificial Intelligence: A Review

Cited by 42 publications

References 64 publications

Enhancing Safety in IoT Systems: A Model-Based Assessment of a Smart Irrigation System Using Fault Tree Analysis

Enhancing Safety in IoT Systems: A Model-Based Assessment of a Smart Irrigation System Using Fault Tree Analysis

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Klasifikasi Citra Penyakit Daun Cabai Menggunakan Algoritma Learning Vector Quantization

Contact Info

Product

Resources

About