Note: The project is not complete. It's in the development phase. UNET needs to be used for image segmentation to provide an alternate model. NDVI needs to be calculated from NIR data from .TIFF file instead of .jpg file
Project agro captain is aimed at optimising agriculture, the objective is to use deep learning algorithms and drone tech to create a report for farmers to analyze their crops.
The drone has a camera and 3 sensors. Based on the images of the crops and sensor data of a farm, We can analyze the health of the crops. We can also track the growth of the crops. We can produce reports that suggest changes to the farm practices Analyze and find What kind of produce the farmer can expect based on data Give feedback to the farmer based on the data collected to maximize his profitability
Also, farmers cannot manually check massive scales of land in order to evaluate their crops, but drones can do it.
The data of temperature, humidity, and light intensity of the land is needed to analyze the crop conditions. When this data is fed onto the ml module we can get useful insights
Using the camera from the drone, we can collect visual images of the crops, this will help determine the health of the plants. We have a database of plants and plant growth on the internet. The data can also be used to compare the current states of the plants with what an ideal state of plants should be. Based on tracking the various stages of growth, we can predict if the growth is healthy and if the crop yield is good for the farmer. This will help the farmer know if the crop is worthy to be maintained or not.
The crop produces metrics using databases. Using analysis we need to What kind of produce the farmer can expect based on data
Give feedback to the farmer based on the data collected to maximize his profitability
Smart app for Drone status as well as suggestive feedback on the farm. Bring the output of all the data into the app. The app must show the insights and reports that the drone has captured.