Energy Optimized Edge Computing Framework for the Sustainable Development of Modern Agriculture

The effects of climate change has a direct impact on the crop production as the environmental conditions become unsatisfactory to support the proper growth of crops, this can lead to severe economic loss and create a backlog in the food production. Smart agriculture has proven to be an effective solution in maximising the crop yield while ensuring sustainable farming by alleviating the consequences of traditional agricultural practices. As effective as it may be, implementation of this is confronted with various challenges such as lack of infrastructure and isolation from networking facilities that are required for the smooth operation of the Wireless Sensor Network established. The sensors and imaging systems present in the cropland generate large amounts of data that need to be processed in an affordable and scalable manner even when the internet connection is limited. Edge computing is an emerging technology that is capable of processing data close to the user and can thus reduce the latency and provide functional capabilities even in the absence of sufficient internet bandwidth. This paper proposes an architecture that utilizes agricultural waste to power the edge devices being deployed in a given crop land. In order to ensure efficient energy usage and processing we implement the DBSCAN clustering algorithm integrated with the FPKM algorithm to efficiently denoise the collected data and an offloading mechanism that ensures efficient usage of computational resources by enabling parallel computation to minimize errors and delays in actuator instructions that could potentially increase the crop productivity and significantly diminish the possibility of crop loss.