Big data analytics framework for agriculture
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...
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| Language: | English |
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Midlands State University
2018
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| Online Access: | http://hdl.handle.net/11408/2999 |
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| _version_ | 1779905649102028800 |
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| author | Zinyoni, Bradwin Danai |
| author_facet | Zinyoni, Bradwin Danai |
| author_sort | Zinyoni, Bradwin Danai |
| collection | DSpace |
| description | 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. |
| id | ir-11408-2999 |
| institution | My University |
| language | English |
| publishDate | 2018 |
| publisher | Midlands State University |
| record_format | dspace |
| spelling | ir-11408-29992022-06-27T13:49:05Z Big data analytics framework for agriculture Zinyoni, Bradwin Danai Digital revolution. Farming 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. 2018-04-23T12:07:50Z 2018-04-23T12:07:50Z 2017 http://hdl.handle.net/11408/2999 en open Midlands State University |
| spellingShingle | Digital revolution. Farming Zinyoni, Bradwin Danai Big data analytics framework for agriculture |
| title | Big data analytics framework for agriculture |
| title_full | Big data analytics framework for agriculture |
| title_fullStr | Big data analytics framework for agriculture |
| title_full_unstemmed | Big data analytics framework for agriculture |
| title_short | Big data analytics framework for agriculture |
| title_sort | big data analytics framework for agriculture |
| topic | Digital revolution. Farming |
| url | http://hdl.handle.net/11408/2999 |
| work_keys_str_mv | AT zinyonibradwindanai bigdataanalyticsframeworkforagriculture |