International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Smart agriculture innovation enables the intelligent optimisation of crop growth conditions based on environmental and soil parameters. A Cloud-Based Big Data analytics framework can be adopted to analyse different ingested data sources in order to develop models of predictive analytics for crop stages and assist the decision-making processes of farmers. Big Data Analytics entails several sophisticated methods and tools, which can be provided as a service using several services integrated under a Big Data cloud framework. The aim is to support the decision-making processes of farmers by providing a comprehensive view of the past, present, and future of farms. Data from diverse sources of different types and characteristics are injected into the system for cleaning and pre-processing. Big Data Analytics techniques are efficiently applied for predicting crop growth stages and diseases using predictive analytics, remote sensing, and computer vision technologies. Smart agriculture solutions should also consider the issues of data quality, privacy, and security.
The continuous growth of the world population raises the need to increase food production. Agriculture and rural development must, therefore, remain top priorities for governments. As the population increases, the demand for food, clean water, and energy increases as well, and the challenge is to fulfil this demand. On the one hand, the response to this growing demand requires an evolution of the agricultural sector through the adoption of new technologies. On the other hand, climate change imposes a new set of challenges to farmers. In this context, information and communication technologies (ICTs) can help farmers increase productivity, fertilisation efficiency, irrigation application, and pest control while reducing operational and management costs. The proper combination of these technologies leads to Smart Agriculture.
Keywords : Cloud computing; agriculture; big data; Internet of Things; data analytics; data processing; machine learning; cloud storage; data acquisition; wireless sensor network; Apache Spark; fog computing; smart agriculture; intelligent agriculture; deep learning.

Downloads: | DOI: 10.17148/IJARCCE.2020.91227