Modernization of Precision Fertilization Based on Artificial Intelligence Technology in Rural Areas
Abstract
Fertilization in rural areas is still largely carried out based on the habits of farmers, so it is inefficient and has a bad impact on the environment, while existing AI research usually only focuses on technical solutions separately. This research creates an AI reference architecture for precision fertilization approaches in rural agriculture using the design science research method. This method involves a systematic literature review (30 articles from Scopus), model creation with ArchiMate, and focus group discussions with farmers in the Pangalengan area. The result is an architecture that has five drivers, three types of assessments, three objectives, and three requirements. This architecture is realized in the form of three modules, namely a soil nutrient analysis module, a fertilizer recommendation engine that uses AI technology, and a decision support application for farmers. This system is supported by soil sensors and hardware in the form of Arduino or ESP32. Validation by farmers shows that this design is appropriate for their needs and has the potential to increase fertilizer use more efficiently and increase yields, although there are still challenges in terms of ease of use, cost of IoT devices, and internet network availability.
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