Data Storage, collection, and Transmission in Smart Agriculture Using Bloom Filter

Population growth, climate change, and sustainable farming practices drive the demand for smart agriculture. To meet the projected 60% increase in food demand by 2050, efficient production and sustainable practices are crucial. Smart technologies like sensors and drones address climate change’s impact on agriculture by monitoring crops and adapting to changing conditions. However, smart agriculture generates vast amounts of data that traditional methods struggle to handle. As probabilistic data structures, Bloom filters offer efficient solutions for data management by enabling quick identification and retrieval without extensive computational resources. They are particularly useful in crop disease detection, aiding prompt management and prevention. Scalable Bloom Filters (SBF) address limitations like false positives and dataset size by dynamically expanding the filter as the dataset grows. SBF improves efficiency, reliability, data security, and transmission by limiting access to authorized nodes, reducing congestion and costs. Using SBF in smart agriculture makes the data storage, collection, and transmission process less time-consuming, space efficient, and with higher accuracy.