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Real-Time Automation of Mushroom Farm using Arduino

A. Akhil, Sunil Kumar, Salil Kathuria, P. Preeti, Sanjay S. Rathore

Abstract


The latest horticultural insights make similar point about the huge commitments mushroom cultivating makes to the world market. Accordingly, mushroom cultivating and development are turning out to be increasingly well known. Weather conditions observing and control is a significant variable for mushroom development, explicitly the effect of temperature and mugginess. A large portion of the ranchers in distant regions overall utilize conventional ways of developing the mushrooms. The conventional method is extremely difficult, and because there is little weather and culture process control, dangerous mushrooms frequently develop. This paper mirrors a building plan of Internet of Things (IoT) based shrewd mushroom cultivating. In a regulated setting, monitoring and control systems powered by the IoT are frequently used to manage agriculture farms, including mushroom farms. IoT can assist with working on rural and cultivating enterprises by lessening work cost through mechanization and expanding the yield by giving ideal circumstances to development. In a mushroom farm, temperature, humidity, light intensity, and CO2 are just a few of the crucial parameters that must be controlled. Wireless sensors are used to gather this information. The use of resources like water and fertiliser has been optimised which results in increasing the quality and output of the mushroom. In this developed prototype, the mushroom yield has increased almost twice compared to mushroom cultivation using conventional methods. Sensors inserted into the mushroom growing system at crucial times allow for real-time monitoring of humidity, temperature, and CO2 in the mushroom farming process. Based on sensor data, growth factors are managed to follow a "growth curve" that is best for the mushrooms at each stage of development. The greatest accuracy attained with the ensemble model is 100% which exceeds each individual classifier. Notwithstanding, the framework will be proficient for continuous ranch robotization and filling in the mushroom area.


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References


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