Arvind Chudasama
Verlag Dr. Albert Bartens KG, Lückhoffstr. 16, 14129 Berlin, Germany; arvind.chudasama@bartens.com
Digital agriculture (DA) is a combination of technologies spanning devices sensing the environment from a close distance or thousands of kilometres in the skies to chips monitoring crop-production systems. Underpinned by the Internet of Things (IoT), the technology is advancing traditional agriculture production systems into data-driven smart farming, promising substantive benefits regarding improving efficiency, effectiveness and productivity. DA relies heavily on the data sources and techniques used to collect it. This data is then organized and analyzed in agricultural data warehouses. The process of deploying DA is derived from data science, but farmers seeking to embrace DA face significant challenges. High-speed networks are imperative for collecting and distilling data to support optimized farm operations. Poor connectivity in rural regions is a severe drawback in adopting DA. Even where this can be addressed, farmers assuming the role of data scientists to utilize the technology effectively is a further hindrance. In addition, there is a need to put in place robust security protocols against cyberattacks. This invariably comes with costs. Further, farming operations involve many moving parts – soil management, agronomy, crop protection, weather, labour and much more. Agricultural production is a complex enterprise where informed decision-making is invariably based on embracing knowledge of how these moving parts can best be managed for a desirable outcome. While many datasets support crop production operations, the data is usually rich, large, complex, and heterogeneous. Therefore, its analysis is not straightforward. The central issue is weighing the information about various parameters. For example, should soil moisture have a greater weighting than soil structure, or are their relative weighting impacted by the season? Finally, the greater threat from increasing reliance on the technology is trading ‘experiential knowledge’ that farmers have developed over the years, acquiring a sound knowledge of their local ecosystem with ‘technocratic expertise’ that is effectively spoon-fed. The challenges impacting the adoption of smart farming are discussed.