The stability of rock and soil masses has become increasingly critical due to large-scale expansion and landfilling, resulting in frequent landslides that pose significant threats to safety and property. Consequently, soil slope stability monitoring is essential. To mitigate slope instability risks, this study investigates soil slope stability monitoring using big data technology within the context of the internet of things. The research examines slope monitoring techniques and summarizes various methods for detecting slope deformation. By monitoring displacement and deformation, the operational status of slopes can be assessed, safety evaluated, disasters prevented, and adverse social impacts avoided. Collected geological data support the development of slope models, enabling analysis under different damage conditions. The findings indicate that 50% damage corresponds to a warning threshold, while 80% damage triggers an alarm. Simulation results show that slope stability increases with higher internal friction angle and cohesion but decreases as the slope angle increases.

The proposed system integrates various services into a common platform for digital agriculture, linking various IoT sensor nodes distributed in the field and connected via LoRaWAN technology to collect soil and climate data that will be processed using a kappa Big Data architecture to display the data collected by the sensors in real-time and provide control and monitoring of tomato crop through notifications to the farmer via a mobile application. In addition, the system offers the ability to detect tomato diseases, using an image-based classification model. This model is able to detect leaf diseases with an accuracy of 86%. The goal is to provide farmers with an accurate view of their crops and mitigate disease and environmental damage.