Big Data Analytics Framework for Agriculture

Abstract

Farming is undergoing a digital revolution. Farmers are gathering information passively collected

by precision agricultural equipment and manually and many farmers are using information from

large datasets and precision analytics to make on-farm decisions. Big data includes extremely large

data sets that may be analysed computationally to reveal patterns, trends, and associations,

especially relating to human behaviour and interactions. The use of large information sets and the

digital tools for collecting, aggregating and analysing them together is referred to as big data.

Compare a notebook wherein a farmer might log information about his or her crop performance

with a computer used to predict and direct future production practices. Logging information using

the application can be done more efficiently and the volume of information the farmer may access

using profound agricultural management tools provides access to interacting with datasets that

stretch way beyond the individual farm. The analysis was done successfully. Therefore, from the

analysis the researcher proposed development of a big data analytics framework for agriculture

that enables the farmers to assess and to predict the outcomes of the crops before they grow them

by using the historical information. A detailed feasibility study was carried out and it resulted

feasible to design the system and an in-house development solution was recommended. Various

designing tools have been used which includes MYSQL and PHP servers. The system allows the

farm worker to record the farm activities in order to be able to use that data to access and to analyse

the crops behaviour. The system was successfully implemented and parallel changeover was the

recommended changeover strategy due to its many advantages over other strategies. Maintenance

was carried out using perfective maintenance strategy which allows for continual improvement of

the system. It’s the view and aspirations of the researcher to have the system integrating the

training modules which manages recommended training schedules in a bid to continuously cope

with changing technological environment.