How can Cloud Computing help farmers increase production and save water and energy?

October 28, 2021 | Comments(0) |

Cutting Edge technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Computer Vision, and Cloud Computing are assisting many industries in increasing production and expanding their business. In this blog, we will look at how these technologies can help farmers with things like farm monitoring and control, weather prediction, disease and pest management, and livestock tracking.

In this blog, we can shed some light on leveraging technology to advance agricultural practices, reaping better produce.

Table of Contents

1. Farm Monitoring by IoT
2. AI/ML Based irrigation
3. AI/ML/Computer-vision Based disease prediction and crop health analysis
4. AWS IoT’S cycle of services
5. IoT Case Studies and CloudThat’s offerings

Farm Monitoring by IoT

Farm monitoring is simply understanding what the farm’s climate conditions are, such as temperature, humidity, wind speed, and other factors, as well as the farm’s state, such as pump status, water-valve status, and energy consumption. 

We can utilize temperature and humidity sensors, soil moisture sensors, soil temperature sensors, wind direction, wind speed, and rainfall sensors to monitor the farm. Sensors are connected to microcontrollers and other communication devices, which can be powered by batteries or solar panels. 

Two scenarios exist for sensor devices to connect with the cloud. One is that all gadgets will be connected to the internet and will send data to the cloud for processing. The other option is to use a gateway to transport all sensor data to the cloud. Both ways are used depending on the application.  

 

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 stand-alone pump module can send data to the cloud at regular intervals via MQTT protocol (MQTT is a lightweight protocol that allows devices to communicate with one another via a publish-subscribe model). The data will include pump on/off status and energy consumption. Data received in the cloud is then processed and stored in a database before being sent to the mobile application to be viewed by the user. The mobile app allows the user to turn on/off the pump and set the running time interval based on the use case. 

 

 

In the diagram above, the Gateway serves as a link between the cloud and the edge devices. At the edge, IoT devices range from a few meters to kilometers, and these devices communicate with the gateway using various communication protocols such as Wi-Fi/BLE for short-range (meters) and LoRa/ZigBee for long-range (kilometers). The edge-node data is received by the gateway, which adds the token, if necessary, before sending it to the cloud. Tokens help to find data corresponding to which edge IoT device. Further processing will take place in the cloud, and further data will be sent to the Mobile-APP/Web/Echo device as needed. 

AI/ML Based Irrigation

We now have a plethora of weather APIs that can anticipate high-probability weather forecasts. These APIs are Application Programming Interfaces that give you global access to current and historical weather data.  

We can anticipate the likelihood of rain falling at a specific place using this weather API, by which we can assist the farmer with irrigation planning.  

For example, if the likelihood of rainfall next week is quite high, then the mobile app can be used to aid the farmer to do minimal irrigation instead of doing 100% irrigation. By conserving water and energy, farmers can ensure that their crops remain healthy by avoiding overwatering. This benefits the farmer by conserving water, energy, and the health of their crops. 

 

 

We can collect sensor data such as temperature, humidity, soil moisture, and so on by using a local weather station located within the farm. Using this parameter, we can automate farm operations to assist farmers in conserving resources. 

AI/ML/Computer-vision Based disease prediction and crop health analysis

       

Images of crops and plants will be captured using drones and cameras. And this data is sent to the cloud for further processing. With the use of AI/ML and these crop pictures, we can discover crop sickness. We can advise the farmer in providing correct fertigation to the crops by forecasting plant disease. 

AWS IoT’s cycle of services

 The diagram below tells us the different services offered by AWS for different scenarios. 

 

Device Services will assist edge devices and gateways in operating and connecting to the AWS cloud. The devices’ security, control, and management are accomplished through the use of connectivity and control services. Analysis/AI/ML services perform value extraction and analysis. With the assistance of these services, one can create a fully automated IoT solution. 

This summarizes the application and benefit of using IoT in the field of agriculture and how to monitor various events using AWS IoT cycle of services. Thus Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning, Computer Vision, and Cloud Computing can therefore help farmers save water and energy and increase crop production.  

IoT Case Studies and CloudThat’s offerings

CloudThat’s IoT team has successfully implemented AWS IoT Core Setup for various customers. Here is a case study about AWS IoT Core Setup for Venlite Energy Limited, which is an energy and asset management company with a global audience footprint across and the Implantation of AWS IoT for Sharp Software, an innovative market-oriented engineering solution provider. CloudThat offers cloud consulting services to our vast clientele to build IoT solutions and advance their businesses.

Connect with our consulting architects for expert advisory to build innovative cloud and IoT solutions to suit your requirements and we will get back to you quickly. Stay tuned for more on IoT, the cloud, and other emerging technologies in this space.


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