planto

Inspiration

Planto has been developed for the farming sector. In India, farmers are major stakeholders and 70% of the population is involved in farming. Having such a huge emphasis on the country, more attention should be paid to this sector's development and growth.

Target Audience

This app mainly targets the farmers whether large scale or small scale. Farming holds a significant share in the country's GDP, and even some small mistakes can impact the economy. The main purpose of this application is to help the farmers deal with certain issues that can play a major role in destroying the whole crop and make it difficult for them to earn a living. These issues can be handled on the ground level and can save the farmers from losses and can help contribute to an even larger share in the GDP of the nation.

What it does

Plant Disease Prediction: Our app is user-friendly and has been built keeping in mind the difficulties that the farmers face and provides them the solution as well. Planto focuses on the condition of leaves. It scans the leaves and informs the farmer about any discrepancy that may arise in a long run and help them save their plantation from getting destroyed.

Disease Information and Management: Our app also guides them and shows the remedies that should be adopted in order to deal with the situation at hand. It also states the symptoms that help farmers to understand more about the problem and what all can be done to protect their plants. It gives them a heads up about it and highlights the precautionary steps that the farmer can consider to prevent the disease altogether.

How we built it

Initially, we built the machine learning model and exported it into the '.tflite' file. Then, we worked on the android application, which was built using flutter. The data for our application is stored in firebase (firestore storage).

Challenges

Connecting firebase (firestore storage) to our flutter application, connecting the machine learning model with our application, and passing the predicted disease value to get information about that particular disease from firebase. We had to go through multiple developer documents and resources to push through our problems.

Accomplishments

1. Aesthetic UI/UX built in just 2 hours.
2. Machine Learning Model connection with flutter.

What we learned

1. Machine Learning Model Implementation (Image Classification).
2. Firebase Implementation.

Future Scope

1. Chatbot for farmers to interact and discuss their problems.
2. Providing them with all the fertilizers and helping them order these.
3. Discussing the condition of the soil as well, before the plantation takes place.
4. Providing them with a timeline and best practices for farming.

APK

To test the application Click Here

Built With

• adobe
• firebase
• flutter
• python