Industrial greenhouse mushroom cultivation is currently promising, due to the nutritious and commercial mushroom benefits and its convenience in adapting smart agriculture technologies. Traditional Device-Cloud protocol in smart agriculture wastes network resources when big data from Internet of Things (IoT) devices are directly transmitted to the cloud server without processing, delaying network connection and increasing costs. Edge computing has emerged to bridge these gaps by shifting partial data storage and computation capability from the cloud server to edge devices. However, selecting which tasks can be applied in edge computing depends on user-specific demands, suggesting the necessity to design a suitable Smart Agriculture Information System (SAIS) architecture for single-crop requirements. This study aims to design and implement a cost-saving multilayered SAIS architecture customized for smart greenhouse mushroom cultivation toward leveraging edge computing. A three-layer SAIS adopting the Device-Edge-Cloud protocol, which enables the integration of key environmental parameter data collected from the IoT sensor and RGB images collected from the camera, was tested in this research. Implementation of this designed SAIS architecture with typical examples of mushroom cultivation indicated that low-cost data pre-processing procedures including small-data storage, temporal resampling-based data reduction, and lightweight artificial intelligence (AI)-based data quality control (for anomalous environmental conditions detection) together with real-time AI model deployment (for mushroom detection) are compatible with edge computing. Integrating the Edge Layer as the center of the traditional protocol can significantly save network resources and operational costs by reducing unnecessary data sent from the device to the cloud, while keeping sufficient information.

Finer spatial resolution (roughly 1 km) of terrestrial rainfall is necessary for urban hydrology and microscale agriculture demands but still limited in various ‘top-down’ satellite rainfall products (SRPs). This study aims to evaluate the potential application of an emerged ‘bottom-up’ approach, the parameterized SM2RAIN model, to the C-band Synthetic Aperture Radar Sentinel-1 (S1) satellites for a high-spatial-resolution SRP (SM2RAIN-S1, 0.01º/6-day) over Central South Korea. By comparing its performance with the Integrated Multi-satellitE Retrieval for Global Precipitation Measurement (IMERG) Early Run product at a similar resolution against a common reanalysis rainfall product and rain gauges network during a 1-year period, we explore whether this novel bottom-up SRP can add improvements to the conventional top-down SRP regarding differences in climate areas (coast and inland), vegetation covers (cropland and mixed forest), and seasonality (rainy and non-rainy seasons). The SM2RAIN-S1 demonstrates its feasibility in capturing general spatiotemporal rainfall patterns in the study area compared to other rainfall products; however, noises associated with the S1 images still occurred. For the comparison over climate-vegetation areas, the extended triple collocation revealed that the SM2RAIN-S1 is relatively superior to the IMERG mainly over inland and forest regions, suggesting the suitability of 6-day SM2RAIN-S1 data to the stable and lower temporal rainfall variation in such regions, where the IMERG data are limited. The comparison against rain gauge data over rainfall seasons indicated that the SM2RAIN-S1 generally outperformed IMERG for bias and Root-Mean-Square Error, particularly during the rainy season. Moreover, the SM2RAIN-S1 shows its feasibility for moderate and heavy rain detection, while it is less effective in detecting light rain events compared to the IMERG. This study encourages the potential use of the bottom-up SM2RAIN-S1 rainfall product to complement, downscale, and correct the biases in top-down SRPs; together with supporting their integration for improving high spatio-temporal resolution rainfall products.