A dynamic controlled environment model for sustainable mushroom cultivation by using machine learning methods

Agriculture is the backbone of India which is diversified. Mushroom cultivation is one such farming method that not only gives promising profit to the cultivators but also provides nutritional mushrooms to the consumers. Nowadays, as the demand for the mushroom cultivation is increasing day by day, advanced technologies pave way for the farmers to improve quantity and quality along with its nutritional value of mushrooms. In this paper, pilot study has been conducted in order to extract knowledge-based inference by remotely monitoring the environmental conditions such as temperature and humidity using DHT11 sensor, CO2 using MG811 sensor, rat movement using PIR sensor and light intensity using LDR sensors are monitored through ThingSpeak cloud server to govern the appropriate actuators such as Sprinkler, LED, Humidifier, Exhaust Fan and Buzzer at the given threshold values, along with this Biological Efficiency%(BE%) has been conducted in both manually controlled mushroom and also in IoT enabled mushroom in two types of species i.e., P. florida and P.eous APK1. From the pilot study the results proves that the monitoring and automation of parameters plays a predominate role in producing the nutritious mushroom with increased Biological Efficiency % (BE%) in P.florida as 99.51% in IoT enabled cultivation whereas in manual controlled hut it is 90.99%. The proposed DCE highlights the importance of machine learning algorithm and its collaborative aspects of supervising and governing the environmental conditions in each stage of growth with appropriate actuators and Biological Efficiency %(BE%) which will play a major role in producing the most nutritious mushroom. Therefore, the proposed DCE framework will be greatly helpful for reaping highly nutrient mushrooms and it will help to increase social economic growth of self-help women and agriculturist.