Agricultural sources are major contributors to GHGs and toxic air pollutants but local monitoring is difficult because of the fragmentary nature of farmlands and the unavailability of inexpensive, portable sensing systems. In this study, we propose a mobile-embedded IoT system to monitor CO and CH? emissions in near real-time and geospatially map the data in agricultural settings. The system contains MQ-7 gas sensor and MQ-4 gas sensor for pollution detection, a DS3231 time clock module for exact time marking, and a HC-05 Bluetooth module for transmitting data to a Flutter mobile app, which is the app developed by two software developers in charge of cloud computing, for display of pm2.5 information. The mobile app provides: View sensors real-time data Show active sensor storage Data cleared special remind Export to CSV file Functions: 1. All datasets are geotagged based on GPS and trivial to use with a geospatial analysis pipeline. By its capability to map the spatial and temporal emission patterns, the identification of pollution hotspots and application of mitigation strategies targeted at them, can be developed, offering useful information for sustainable agriculture.The provided method is practical, enveloped within an accessible system, economically sound, and easily available to farmers, successfully integrating inexpensive sonic measuring devices to sophisticated geospatial analytic systems. Its flexibility makes it applicable for emission tracking in all forms of cultivation, which serves everyone from farmers to policymakers to meet targets in climate-smart agriculture and sustain environmental regulations.