Using MLPA for smart mushroom farm monitoring system based on IoT

Velliangiri, S.; Sekar, R. Raja; Anbhazhagan, P.

doi:10.1504/ijnvo.2020.107559

Cited by 9 publications

(2 citation statements)

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“…They proposed an IoT and MLintegrated system using an ESP32 micro-controller, achieving an impressive 100% accuracy in edible mushroom classification. Velliangiri et al [8] introduced machine learning with prediction analysis (MLPA) using Dht22 sensors and IoT for predicting mushroom diseases, while Singh & Anand [9]focused on reducing human intervention, utilizing a Raspberry Pi 3B+ with ESP8266 sensing nodes for real-time data monitoring. Furthermore, Chong et al [10] proposed using unmanned aerial vehicles (UAVs) and ground cameras to enhance data gathering, aiming for improved efficiency in mushroom harvesting.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT Enabled Mushroom Farm Automation with Machine Learning

Shafie Omar,

Wan Mohd Faizal Wan Nik,

Muhammad Imran Ahmad

et al. 2024

ASET

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Mushroom farming has gained prominence due to its significant contribution to the global market. One major challenge for mushroom cultivation is maintaining optimal environmental conditions, specifically temperature and humidity. Traditional farming methods, prevalent in many parts of the world, lack precise control over these parameters, often leading to poor yield. This paper presents an innovative approach combining the Internet of Things (IoT) and Machine Learning (ML) for mushroom farm automation. The proposed system employs the ESP8266 microcontroller with specific agricultural sensors for smart monitoring. To regulate the farm's environmental conditions, ML algorithms predict mushroom farm weather states: mild, normal, and hot. The ensemble ML model, comprising five classifiers – Decision Tree, Logistic Regression, K-nearest neighbor, Support Vector Machine, and Random Forest – delivers a commendable accuracy of 100% when combining predictions, surpassing the performance of individual classifiers. This integrated IoT and ML approach promises to revolutionize real-time automation and cultivation practices in the mushroom industry.

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Section: Literature Reviewmentioning

confidence: 99%

IoT Enabled Mushroom Farm Automation with Machine Learning

Shafie Omar,

Wan Mohd Faizal Wan Nik,

Muhammad Imran Ahmad

et al. 2024

ASET

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“…Their use for the purpose of controlling and monitoring the cultivation of various agricultural products allows such processes to be made more efficient and less engaging for the human operator (Lakhiar et al, 2018;Navarro et al, 2020). The production of mushrooms is no exception in this regard (Mahmud et al, 2018;Velliangiri et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Optimizing Mushroom Cultivation Process - Concepts for Control and Monitoring System

Chwietczuk

Siarnowski

Lipiński

2020

Agricultural Engineering

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The first part of the article contains a basic description of the mushroom production process. The analysis of this process allows to propose algorithms for monitoring the growing conditions and controlling the cultivation process − in the latter case with possible human operator intervention. These algorithms, along with their description, are included in the second part of the article - they can be used in the design and implementation of intelligent mushroom farm management systems.

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