Smart Farming Technology Integration to Mitigate Crop Failure Risks on Small-Scale Farms
Keywords:
Smart Farming, Internet of Things, Digital Agriculture, Risk Mitigation, Crop Failure, Small-Scale Farming.Abstract
The development of digital technology has encouraged the implementation of Smart Farming as a solution to increase productivity and efficiency in the agricultural sector. This technology integrates the Internet of Things (IoT), environmental sensors, and data-driven monitoring systems to help farmers manage their land more effectively. However, small-scale farmers still face the risk of crop failure due to weather changes, drought, unstable soil moisture, and limited access to information on land conditions. This study aims to determine how the integration of Smart Farming technology can be used to mitigate the risk of crop failure in small-scale agricultural land. The study used a quantitative method with an applied research approach. Data were obtained through observation, literature review, and sensor data collection covering air temperature, air humidity, soil moisture, light intensity, and rainfall. Data were analyzed descriptively to identify land conditions and potential risks that could affect crop yields. The study resulted in the design of a Smart Farming system that integrates IoT sensors, a database, and a monitoring dashboard. The system is capable of monitoring land conditions in real time, automatically displaying environmental parameters, and providing early warning notifications when land conditions are outside the optimal limits for plant growth. System integration enables farmers to obtain more accurate information to determine land management actions. The integration of Smart Farming technology has the potential to reduce the risk of crop failure through more effective land condition monitoring and data-driven decision-making. This research demonstrates that the use of digital technology can support increased productivity in small-scale agriculture. Future research can focus on implementing the system directly in the field and developing artificial intelligence-based predictive models to improve the accuracy of risk mitigation.
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