This project demonstrates how to build a device using TensorFlow and Artemis module to solve most of the problems faced by the rural or agricultural communities anywhere. The device uses Machine Learning algorithms to check overall plant health, extreme climate prediction and protection, auto-greenhouse adaptation and detecting deadly disease spreading vectors or illegal logging of forests using audio analysis. The project also demonstrates how we can have more insights for our farm just by collecting and utilizing the data got from our sensors. I got inspiration for making this project from news like Australian wildfires, Indian GDP falling down due to faulty agricultural practices and locust swarm which is damaging the crops in East Africa, Pakistan and many other nations very rapidly, mosquitoes breeding at an alarming rate, neglected tropical diseases, so I found a call within me as an active member of this innovative, committed community.

The important measure taken while making this project was collecting data efficiently and patiently, once the data is collected the job becomes lot more easier (You are bound to fail if you don't collect correct data to feed your hungry ML frameworks). For data collection part I used my past project which just gathers data from the sensors and sends to backend using Sigfox protocols but here I am going to train my device on those data. Here is the link for my past project - . Try to understand the data collection part first.

The backbone of this project is that you need to tune your device after every stage. So lets get started, enjoy learning with Machine Learning.

Technical Overview:

I still love my village though we have shifted to a modern city. The freshness of village still restores me from any pensive mood but due to pollution all around we are suffering from bad health and whatever crop we grow they too have some undesired effect on our body, so it is important to look after those crops as well as early sign of diseases in plants as well as humans that too without using complex, power-hungry devices which needs lots of maintenance and far beyond the reach of poor farmers or village men. So I decided to use Edge devices capable of using ML features with low latency and almost no carbon footprint.

Using TensorFlow on Edge Devices would reduce global carbon emissions and save electricity.


1) Farm Analysis:

With increasing population a sustainable method of farming is important. Previously we just used to play with sensors but now using Tensorflow we can not only sense but analyse, predict and take actions. Using all the collected data we will find abnormalities in crop growth, photosynthesis rate, extreme climate and need for smart greenhouse adaptation. This step will stop the competition among the farmers for using excess fertilizers, pesticides, insecticides to boost production. Using the correct farm analysis the machine would auto-suggest the farmer when to use the chemicals in his farm and thus save his money, efforts and degradation of environment. You can check this website for more problems faced by my country's agricultural community.

2) Detection of certain beneficial and harmful organisms using audio analysis :

Flowers can hear buzzing bees—and it makes their nectar sweeter

Farmers are using lots of chemicals to boost their farm yield but we tend to forget...

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