Background
Inspite of various efforts by farmers, they are never quite able to generate the yield they desire. Primary reasons are -
- Weather conditions
- Insufficient knowledge
- Untimely pesticide or herbicide treatment
- Lack of labour
- CROP DATA
Hence, it becomes really important for us to think of tangible solution to these problems. The population is increasing at alarming rates and food produce will sure run out at some point in time with the current yields. It is for this reason that I would like to set up a data-driven farming culture to improve yields, quality of life of farmers and security against weather disturbances.
IDEATo enable data-driven farming, I propose large numbers of environmental and soil quality sensors at startegic postions on the farm. The data from these nodes would be uploaded to a mesh network covering the entire farm using LoRaWAN with the aid of the RFM95 Lora module controlled by an ESP-01 MCU. The PCB design is attached herewith. The farmer can request for the service of getting this data to this mesh network which charges him according to the data acquired.
A drone can also be requested by the farmer to fly over the farm and capture images of crops on the field. The drone is implemented on standard hardware using Raspberry Pi 4B. The code is also enclosed here.
Various commands can be sent to the drone with the help of a web application before takeoff. The drones charges the farmer according to the type of data collected.
eCl@ss is implemented in choosing the best drone that can do the job. This is demonstarted by keeping a dummy drone as a service. There would also be web-apps which would provide services in the form of cloud storage, maintaining a realtime market database as well as crop analytics based on data collected by the drone or the farmer. Again, eCl@ss would help with the decision from the farmer's end to choose between relying on the drone for analytics or directly the web applications.
Implementation
The ESP-01 remain in deep sleep till a service request through a web app is received by the mesh network. It then wakes up and sends the sensor readings to the web app that the farmer requests from.
The farmer then calls the drone for imagery data and autonomously receives an analysis of his farm by eCl@ss requests to the marketplace.
PP
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