CLAY-MIST: IoT-cloud enabled CMM index for smart agriculture monitoring system. (February 2019) - Mekala, Mahammad Shareef; Viswanathan, P.
Highlights: CMM index measurement function having 94% accuracy in heterogeneous seasons. Temperature Quotient function for cell growth assessment. Multi-user real time remote monitoring system of crop growth. Stable accurate measurement analysis with less than 6% error rate. 75% comfort level in crops for large volume Agro-environmental applications. Abstract: The temperature and soil moisture factors affect the growth of agriculture such as productivity, diseases, and yield production. Most of the existing techniques were used to assess comfort level based on dew-point-humidity data which gives a false decision with time and energy consumption. To comprehend these issues, we proposed a cloud-enabled CLAY-MIST measurement (CMM) index based on temperature and relative humidity to assess the comfort levels of a crop. In this research, temperature quotient is evaluated based on the amount of water vapour and pressure in the air which appraises plant growth. The relative humidity is subtracted with the standard constant optimal temperature to extract the comfort level. Therefore, the CMM index experiments with real-time data show an accurate decision and the detailed report sent to farmers. The results are 94% accurate with less execution time when compared with the existing thermal comfort techniques.