AI and IoT to Monitor Citarum River Reforestation
The Citarum River in Java supplies many of the rural, outlying regions of the country. However, in recent years, a lack of oversight has allowed the local textile production companies to pollute the river with microplastics and runoff. The local population has dedicated itself to conservation efforts, and a small startup named Jejak.in has been helping with an innovative application of IoT technology for real-time feedback of the conservation efforts.
How IoT is Helping to Inform Conservation
The system developed by Jejak.in is primarily a tree management system with several features added in for this particular project. The software has image-mapping and identification abilities, as well as data collection through IoT sensors, QR codes, and the connected mobile app. Additionally, it utilizes remote sensing data to pinpoint the location of an individual using the app and deliver overhead satellite imagery of the area.
IoT is used in calculating the height and circumference of a tree and also used in collecting data about the soil content. The data that the app collects is then aggregated to provide insights on how well the conservation process is progressing. The collective knowledge informs the methodology of this particular conservation tactic that trees tend to enhance a river’s catchment areas. More trees, it is expected, will slow runoff into the stream and provide a filtration buffer against effluent being washed into the river directly.
Incorporating Cloud Architecture
Jejak.in’s conservation system is built on a Microsoft Azure framework, and designed to run within the cloud. Utilizing machine learning, the network aims to inform users of how each tree planted affects the long-term outlook for the region in terms of carbon absorption. Combining IoT and AI into a single framework, Jejak.in offers a real-time solution to monitor conservation efforts. It also lets teams involved in those efforts to see how their contributions can affect the local environment in real-time.
