Inspiration
Agriculture is the main aspect for the economic development of a country. Agriculture is the heart and life of most Indians. But in recent days, the field was going down due to various natural calamities. In order to overcome the problem, various issues in this field need to be addressed. The soil type, fertilizer recommendation, diseases in plants and leaves. All these features need to be considered. Our proposed system was organized in such a way, to analyze the suitable crop, diseases in the crop and finally to recommend the appropriate fertilizer to the farmers, that may be o of great help to them .
What it does
Yield right crop at the right time, Balancing the crop production ,control plant disease, Economic growth, and planning to reduce the crop scarcity. Hence to Detect and recognize the plant diseases and to recommend fertilizer it necessary to provide symptoms in identifying the disease at its earliest.
How we built it
The proposed approach was organized in such a manner, that it is universal to all the users in the world. • The first step involves the registration phase, where the user has to present his personal details, details of land and the soil type. • In the second step the user will upload the soil test report into the system for soil analysis. In this step, if the soils test report was not submitted by the user, soil analysis will be carried out by the sensors. Sensors measure the nutrients level of the soil and the data was stored within the database. • In the third step, the corresponding crops infection status will be analyzed and recorded. • In the fourth step, comparison and classification of the soil type was carried out using Long or Short term Memory algorithm. Finally the fertilizers are recommended. The proposed approach was data centric and connected through the cloud. The main advantage of our proposed system is that, it was user friendly and highly efficient. The proposed system maintains privacy and also predicts accuracy.
Challenges we ran into
Plant Leaf Disease Detection and Classification Based on CNN with and Support Vector Machine. The application of Convolutional Neural Networks (CNN) algorithms for the optimum real-time detection of diseases that impact the plant and the afflicted area, so that proper fertilisers can be employed to prevent additional harm to plants from pathogenic viruses. The activation function is at the heart of the CNN model since it combines non-linearity to create a true artificial intelligence system for classification.
Accomplishments that we're proud of
We are proud to built a fertilizer recommedations system for Farmers. Without fertilizers, nature struggles to replenish the nutrients in the soil.
What we learned
We are obtaining adequate findings for proper crop production and fertilizer to recommend to farmers for crop cultivation. The disease detection tool also provides the finest advice for recovering from crop disease, ensuring that the crop or specific land is not ruined and that soil fertility and crop yield are increased.
What's next for Sustainable Environment
The technology will assist farmers by providing required advice on crops, their growth, and other basic information. It will also offer the location of the nearest store where farmers can purchase fertilizer and other materials. It would also assist farmers in selling their commodities to merchants by providing accurate information on market prices and merchant details. The device can also help farmers calculate crop MSP. The disease detection feature can also be improved by adding dedicated cameras to the device, which will improve the device’s accuracy even further.
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