Farmer's friend by idly sambar team number 130

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Inspiration

India is the second largest agricultural producer and seventh largest exporter of agricultural goods. Agriculture plays a major role in the country’s economy as it provides employment to a lot of people, a major source of raw materials and is important for international trade. The share of agriculture in GDP increased to 19.9 per cent in 2020-21 from 17.8 per cent in 2019-20, the share of agriculture has increased to 20% after a gap of about 17 years.

Although the increase in GDP is appreciable, it cannot be deterred that many farmers commit suicides due to the less yield of crops. In February 2022, over 17,000 farmers between 2018 and 2020 committed suicide in different parts of the India. These numbers are highly alarming as farmers and farming is the backbone of a country like India.

What it does

In order to help the farmers with the types of crop that they can plant in their farm to increase the yield, and to also let them know of the kind of crops that can be grown along with the ones that are there for better yield we built various models using machine learning algorithms and the model with the best accuracy was chosen for the real time prediction.

Using deep learning models, convolution neural networks, disease of a plant is detected i.e; many times, farmers might not be able to detect the issue with their crop thus leading to the disease spreading to other crops and the entire yield produced being wasted. Therefore upon uploading the picture of the crop, the farmer will get complete information about the disease and how it can be mitigated by them. A user-friendly web application has been created using the Flask framework. This application will be available for the users at the comfort of their phone where they can know about what kind of crops to grow as well as how to mitigate the diseases acquired by the plants.

How we built it

Django, python Mongodb machine learning deep learning android studio/ web development stack different sensors

Applied descriptive statistics visualization and predictive modeling has been applied to identify the crops that can be grown at a stipulated temperature and other natural conditions.

Challenges we ran into

Since we are using transfer Learning there were difficulties in reshaping and resizing of the images upto the preferred size. We also faced issues while extracting the weights of pretrained models of AlexNet Architecture

It was quite difficult for us to get the real-time data as all the API's were paid. We were not able to efficiently put deep learning into use. Hence we went ahead with machine learning algorithms.

Accomplishments that we're proud of

we successfully predicted the crop most suitable for the soil to be grown and classified the plant diseases through leaves beneficial to the farmers.

What we learned

We have made two models in our solution, in order to compare the accuracies. - In Deep CNN, the input data is passed into three convolution layers . The image is thereby optimised and the output is passed through relu activation layer. - In AlexNET, along with convolution layer, max pooling layer is used. The model with highest accuracy has been used.

fullstack development using FLASK was learnt and working with github and git was a new experience in front end we learnt to build the website using html css and javascript

What's next for Farmer's friend

we will build a IOT device which will be present in farm and take the readings for the soil in real time and the same data will be analysed by ML and DL models which will help to give real time prediction as well