A crop health monitoring system using IoT and low altitude remote sensing.
Crohmi is an IoT enabled agriculture monitoring system which monitors agricultural lands in real-time and helps in crop health and yield prediction.
Our product and services aim to produce an efficient crop health monitoring system based on IoT to improve crop yield significantly and facilitate the farmers. The system is automated based on the sensed parameters such as temperature, humidity, soil moisture, and multi-spectral images of the crop during the growing season. We also capture multi-spectral images of the field for remote surveillance. These images are captured using an imaging sensor mounted on a drone for low altitude remote sensing. We provide the following services:
- A drone with a multispectral camera to capture spectral images of the field.
- A wireless network of IoT nodes to provide real-time data of different parameters.
- A fully functional and user-friendly web portal for interactive data exploration, visualization, and statistical analysis.
Our system integrates data from Iot nodes and spectral imagery to generate more accurate and precise readings.
The whole system is powered by a solar panel and battery for 24-hour monitoring.
The nodes communicate through a microcontroller to the LoRA module to a localized gateway in the WSN.
All the hardware i.e. the IoT nodes and the drone is completely portable, so it is easy to place the system anywhere.
An NDVI image is given as input to the model and it generates a health map of the image.
Our web portal provides services like data visualization, data analytics, NDVI mapping, and comparative analysis.
It's time to apply science-based decisions to stop wasting our valuable resources like water, energy, and nutrients.
Our aim is to help the farmers in optimizing their farming practices leading to significant water, fertilzer, energy and labor savings. Our multi-model system collects data from two different sources to generate highly accurate and precise results. It will help farmers in taking timely decisions, generate more yield, and saving resources. Our system has three main modules.
Drone attached to a multispectral camera
A multispectral camera mounted on DJI Phantom 4 is used to collect spectral images of the crop field. The images are then preprocessed for usage in a predictive model that uses the data collected from the WSN and these preprocessed images, which serves the role of an early warning system.
Wireless Sensor network
Our system collects data through the Wireless Sensor Network. The data consists of the real-time values of humidity, air temperature, soil moisture, and soil temperature.
Our web portal is used display the collected data. The data obtained from the IoT nodes are displayed in the form of graphs and charts. The user of the website also has an option of performing comparative analysis between two parameters.
24/7 real-time monitoring of crops
Primitive methods of checking crop health are very tedious and inefficient and require human resources.
Our solution uses hardware technology to monitor crop in real-time 24/7 and use a predictive model to generate health maps which give accurate health status of the crop, health map can be generated every week or month and in a matter of minutes as compared to primitive methods of checking crop manually which may take many hours to complete.
This will not only solve time and human resources but also the cost is reduced.
How are we different?
We provide value to our solution by integrating multi-modal data, the rest of the existing solutions provide either IoT based monitoring or drone-based multispectral imagery. Our solution integrates both of these models to create accurate predictions and real-time monitoring solutions.
It also involves a user friendly web portal where users can interact and view different crop health parameters. Another aspect of this solution is that we have created predictive models to generate health maps that depict the current health status of the crop.
All of this is accessible by a user-friendly web portal.
Meet the team that made it possible.
Dr. Rafia MumtazPrincipal Investigator
Dr. Faisal ShafaitCo PI
Uferah ShafiTeam Lead
Moeez MalikEmbedded System Engineer
Usama ShujaatEmbedded System Engineer
Ihsan HaqEmbedded System Engineer
Osama JamilImaging and ML Engineer
One-time fixed cost
- Master Node
- Slave Node
- Training Service
- Drone Survey Service (Subscription)
- Drone Survey Service (Visit)
- Travelling cost*
- Machine learning service
- Historic data
- 24/7 Support System