Recently Farmer Suicides and Protests are all over the news. They have been committing suicides under pressure because of debts and money pressure. The main reason behind these problems is that they are not getting proper crop yield, sometimes their whole crop is destroyed by pest diseases, and also because of using primitive techniques to irrigate the field which results in overuse of water. So as a result a lot of freshwaters also gets wasted. This has been our major objective behind the project. We will be providing a complete package to the farmers to increase their Crop Yield by at least 40% and to use almost 50 per cent less water and also detect the diseases. Growing crops is a beautiful activity which makes a farmer proud of himself, for he has created a new life. Despite its beauty though, crop production requires varying farm activities and constant maintenance in order to provide a high and healthy yield.
What it does
We are building an IOT application for mobiles where the user can control the irrigation automatically or manually just by the touch of finger. IoT will be used to connect the valves in the farm through the wireless controllers that will be implanted on the fields which further can be accessed through Wireless devices like Mobile. Soil Moisture, Humidity, Temperature and Acoustic sensors will be deployed in the field which will collect the data about the soil and will be uploaded to a cloud server like ThingsSpeak which the user can access and analyse about the quality of soil. Further, Machine Learning analysis by three different algorithms will be done on the data collected which will predict the crop yield and give instructions to increase it. Starting with the early crop stages, a farmer must closely monitor crops because of various crop insect pests and diseases. They tend to be the biggest threat to successful crop production. Depending on the crop type and growth stage, it's estimated that early pest detection can reduce yield loss by up to 20-40%. Therefore, farmers need to put all of their effort into constant crop monitoring. Therefore, through Deep Learning, image processing will be done on the leaves to identify pest diseases and alert the farmers before only so that they can take corrective measures.
1.) Saving Water with automated precision irrigation at the best possible time.
2.) Saving hundreds of man-hours and elimination the human factor.
3.) Machine Learning Analysis for Crop Yield Prediction and measures to improve it.
4.) Combining sensors data and cloud intelligence to save even more resources.
5.) Pest Disease Detection through Deep Learning.
Challenges we ran into
We faced problem in setting up the IoT module, also faced difficulty in finding the datasets of crop data for training the model like there were many null values in the dataset it took a lot of time to preprocess the data. It was difficult to find the best algorithm to achieve maximum accuracy, we tried three different algorithms and then came up with the conclusion that Deep Neural Networks was the bast model to achieve maximum accuracy so it took a lot of time.
What we learned
We learned how to deal with the complicated datasets present on the government websites as we invested a lot of time correcting it with encoding the data and other similar techniques, got to know how awesome it is to use IoT to control various things just by the click on a device.
IoT, CNN-Keras, Deep Neural Networks, Machine Learning, Deep Learning