Key points are not available for this paper at this time.
Abstract In agriculture identifying the "nature of the soil" is a significant phase for serval sorts of landscapes. Even though numerous studies have been conducted for continuous soil health monitoring, however, existing techniques face challenges like labor-intensive, cost efficiency, need extensive field sampling and not suitable for tracking of soil parameters. To overcome these issues this paper proposed a novel IoT-based TeaSoil for precision soil monitoring of tea plant cultivation. The proposed TeaSoil’s end nodes are known as Tea Soil Monitoring Units (TSMU), are solar-powered and placed up for extended periods of time. The TSMU wirelessly sends soil NPK sensor, soil temperature, moisture, pH, organic matter, zinc, copper, iron, magnesium, manganese, carbon dioxide (CO2), and geo-location data using SigFox communication. SigFox receives the data and uploads it to the cloud server for data storage and analysis over a long period of time. On this receiver side, the soil classification is carried out using a deep learning-based EfficientNet for tea plant cultivation. A TSMU dashboard allows users to examine gathered data. The proposed Tea soil achieves an overall accuracy of 99.15%. The accuracy of proposed techniques attains 0.75%, 2.16%, and 20.32% better than Fast R-BAG, CNN, and CSMO. It is demonstrated that the proposed TeaSoil is suitable for precision agriculture based on the experimental results.
Santhiya et al. (Thu,) studied this question.