Overview
The Lake Rover Project is a research and prototyping effort. The effort endeavors to take advantage of the current sweet spot in multiple technologies.
Intended Results
- A low cost, autonomous rover able to
- accurately maneuver about the surface of a lake
- record images of aquatic plants and lake bottom
- record GPS coordinates, water depth, water temperature
- Machine learning (AI) software to classify recorded images into
- invasive plant found
- suspicious looking plant found
- no concern
- Interactive Geographic Information System (GIS) maps providing
- markers on the maps where invasive plants were found
- each marker providing
- image at that location
- classification, GPS coordinates, date found, depth
- A website
- to publish the interactive GIS maps
Technologies To Leverage
Internet of Things
The Internet of Things (IoT) refers to the network of physical objects embedded with sensors, software, and other technologies that connect and exchange data with other devices and systems over the internet. The win for the Lake Rover Project is how the popularity of IOT has lead to a large assortment of low cost sensors.
Sensors play a crucial role in the Internet of Things (IoT) by enabling devices to collect real-time data from their surroundings. These sensors detect various physical parameters such as temperature, humidity, motion, light, pressure, GPS location, water depth, etc. The sensors convert the physical parameters into digital signals that can be processed and analyzed.
The immense popularity of IOT has produced a large assortment of low cost Single Board Computers (SBCs). Many SBCs are designed to easily connect and have readily available software to work with the sensors described above.
The SBC and sensors for the current rover are less than $500.
Drones
The explosion of drone technologies has revolutionized multiple industries by providing innovative solutions for aerial surveillance, delivery services, agriculture, and photography. Advances in sensor technology, artificial intelligence, and battery efficiency have made drones more accessible and capable than ever before. This rapid development has enabled precise data collection, real-time monitoring, and automated operations, enhancing productivity and safety. As regulations evolve to accommodate these advancements, the integration of drones into everyday applications continues to expand, promising transformative impacts on logistics, environmental monitoring, disaster response, and beyond.
AI Deep Learning Image Classifiers
Deep learning image classifiers are advanced computational models designed to automatically identify and categorize objects within images. The classifiers leverage multi-layered neural networks to analyze visual data, enabling them to learn intricate patterns and features directly from raw pixels. Unlike traditional image recognition methods that require manual feature extraction, deep learning models can autonomously discover salient features, making them highly effective.
The surge in popularity of deep learning image classifiers can be attributed to several factors. Advances in computational power, particularly with the use of GPUs, have made it feasible to train large neural networks on large datasets efficiently. Additionally, the availability of extensive labeled image collections has facilitated the development of robust models capable of generalizing well to new data. These classifiers have demonstrated exceptional performance in diverse applications, from medical imaging to autonomous vehicles, contributing to their widespread adoption across industries.