Cloud Perspectives Blog - Microsoft
Problem: The land is one of the main natural
resource available. There is a huge potential in reaping the benefits
out of it. In contrast, 2017 GDP report says Agriculture sector
contributes only 6.4 percent of the total world’s economic production.
Lack of proper irrigation facilities in many underdeveloped and
developing countries is affecting the agriculture sector at a greater
extent. Which is mainly due to low rainfall and unavailability of
electricity.
Solution: To tap the potential from
this untapped area, Microsoft’s AI & IoT Insider Labs is helping
“SunCulture”. By combining solar power and precision irrigation,
SunCulture’s RainMaker2 pump is making cost-effective for farmers in
yielding more returns.
Description: This device
collects the sensor data, like soil moisture, pump efficiency, solar
battery storage, and other factors using a cloud environment. Due to
presence of SunCulture’s network in almost 2,000 locations, they are
combining all the data from sensors and using machine learning tools in
providing effective irrigation recommendations to farmers through mobile
SMS which is helping them in taking efficient irrigation decisions.
Machine learning
is everywhere throughout the whole growing and harvesting cycle. It
begins with a seed being planted in the soil - from the soil
preparation, seeds breeding and water feed measurement - and it ends
when robots pick up the harvest determining the rightness with the help
of computer vision.
Extension: This can be
extended by analyzing more variables like land area, yield, leaf vein
morphology, crop quality characteristics, pesticides data, weeds data,
material stocks and using appropriate machine learning tools, we can be
able to predict the things like,
2. Expected weather phenomena and estimate evapotranspiration and evaporation,
3. Yield Prediction,
4. Accurate detection and classification of crop quality which can decide price,
5. Pest & Disease Detection,
6. Weed Detection,
7. Effective and efficient livestock production systems.
With these predictions, the agriculture sector can increase the production levels and products quality.
References:
1. AI & IoT Insider Labs: Helping transform smallholder farming, 24 January 2019, https://azure.microsoft.com/en-us/blog/ai-iot-insider-labs-helping-transform-smallholder-farming/
2. Machine Learning in Agriculture: Applications and Techniques, 22 March 2019, https://medium.com/sciforce/machine-learning-in-agriculture-applications-and-techniques-6ab501f4d1b5
3. Integrating IoT into Healthcare, Agriculture and Transportation Use Cases, 13 May 2019, https://www.iotforall.com/integrating-machine-learning-ml-iot-applications/