Smart Agriculture System (1-2S)
From iis-projects
Status: Available
- Type: Semester Thesis
- Professor: Prof. Dr. L. Benini
- Supervisors:
- Philip Wiese (IIS): wiesep@iis.ee.ethz.ch
- Victor Kartsch Morinigo (IIS): victor.kartsch@iis.ee.ethz.ch
Introduction
Smart sensors, which are sensors equipped with advanced computation capabilities, present a unique opportunity for innovation in multiple critical domains such as environmental sensing to reduce global greenhouse gas emissions or infrastructure monitoring.
A near-sensor processing of the gathered data that directly extracts meaning from the signal can lead to huge improvements in terms of energy efficiency and increased privacy. The reduced energy consumption comes from the fact that more specialized hardware such as the PULP platform including the GAP9 [1] can be used, and, additionally, that the information density of the transmitted data is higher, and thus less data must be transmitted via the often very power-intensive wireless link.
Our long-term goal is to develop a low-power sensing node with very low power consumption. These nodes should incorporate multiple sensors to enable multi-modal sensing. In other words, combine the data of the different sensors to extract additional data. The idea is to use the very efficient GAP9 multicore processor and deploy multi-modal neural networks to perform feature extraction, prediction, anomaly detection, and even on-device learning to adaptively fine-tune the model based on the measured data.
In this project, you will develop a smart agriculture system utilizing a sensor platform to optimize irrigation and pest control, aiming to reduce water usage and improve crop yield. You will develop algorithms to analyze moisture levels through temperature and humidity sensors and to detect pest presence using cameras and environmental sensors. Your task will include integrating data processing models on the GAP9 platform to dynamically control irrigation systems, effectively reducing water consumption. Additionally, you will use camera and image processing techniques to identify unhealthy plants or pest infestations, enabling targeted pest control measures. The culmination of your project will be the creation of a dashboard for farmers, allowing them to monitor field conditions and receive actionable suggestions for crop management, thereby enhancing agricultural efficiency and sustainability.
Project
- Application Specific Algorithm Development: Develop algorithms that analyze moisture levels (using temperature and humidity sensors) and detect pest presence (using the camera and environmental sensors) in agricultural fields.
- Deployment on Edge Device: Integrate data processing models on GAP9 to control irrigation systems dynamically, reducing water consumption.
- Application Specific Optimization: Use the camera and image processing techniques to identify unhealthy plants or pest infestations, enabling targeted pest control measures.
- Data Visualization: Create a dashboard for farmers to monitor field conditions and receive suggestions for crop management.
Prerequisites
- Good understanding of deep learning concepts.
- PCB Design and embedded C programming.
- Familiarity with Python programming.
- Familiarity with the PyTorch deep learning framework.
- Basics understanding of JavaScript and Netron.JS
