Many industries are benefiting from cutting-edge technologies like the Internet of Things (IoT), Artificial Intelligence, Machine Learning (ML), Computer Vision, Cloud Computing, and Artificial Intelligence. This blog will discuss how these technologies can be used to help farmers with things such as farm monitoring and control and weather prediction and management of disease and pests, and livestock tracking.
This blog will provide some insight into how technology can be used to improve agricultural practices and produce better products.
Table of Contents
1. IoT2 Farm Monitoring AI/ML-based irrigation3. AI/ML/Computer-vision Based disease prediction and crop health analysis4. AWS IoT’S range of services5. IoT Case Studies and CloudThat’s offerings
IoT Farm Monitoring
Farm monitoring simply means understanding the farm’s climate conditions, such as temperature and humidity, wind speed and other factors. It also includes the farm’s current state, such water-valve status, pump status, and energy consumption.
To monitor the farm, we can use temperature and humidity sensors as well as soil moisture sensors, soil temperature sensor, soil temperature sensors, wind direction and wind speed sensors. Microcontrollers and other communication devices can be connected to sensors. These devices can be powered by solar panels or batteries.
There are two scenarios that sensor devices can use to connect to the cloud. The first is that all devices will be connected to an internet connection and will send data to cloud for processing. Another option is to use a gateway that transports all sensor data to cloud. Depending on the application, both options can be used.
In the image above, you can see that the pump and actuator module (microcontroller & relay) are linked to the cloud via an internet connection made via GPRS/LTE/Wi-Fi/NB-IoT. This standalone pump module can send data to cloud at regular intervals using MQTT protocol. (MQTT is lightweight protocol that allows devices and computers to communicate via a publish/subscribe model). The data will include information such as the status of the pump and energy consumption. The cloud data is processed and stored in a database. Finally, the data is sent to the mobile app to be viewed. The mobile app allows users to turn on/off the pump, and set the running times based on their use case.
The Gateway acts as a link between cloud and edge devices in the diagram. The edge is home to IoT devices that range in distance from a few meters up to kilometers. These devices communicate with the gateway using different communication protocols, such as Wi-Fi/BLE (meters) or LoRa/ZigBee (kilometers). The gateway receives the edge-node data and adds the token if necessary before sending it to cloud. Tokens are used to identify which edge IoT device has the data. Further processing will occur in the cloud and additional data will be sent to Mobile-APP/Web/Echo devices as required.
Irrigation based on AI/ML
There are many APIs available that can forecast high-probability weather. These APIs are Application Programming Interfaces, which give you global access and historical weather data.
This weather API allows us to predict the likelihood of rain falling at a particular place. This will allow us to assist farmers with irrigation planning.
If the likelihood of rain next week is high, the mobile app can be used by farmers to help them to do minimal irrigation instead. Farmers can conserve water and energy to ensure their crops are healthy by not overwatering. This is a win-win situation for the farmer as it conserves water, energy and improves the health of their crops.
A local weather station is located in the farm and can be used to collect sensor data like temperature, humidity, soil moisture, etc. This param is used to collect sensor data.
