The problem Krishi Dost solves

Problem faced by the farmers which we are solving are:
#Crop failure due to disease
#An over reliance on traditional crops
#Lack of information causes economic loss
#Middleman reaping the bulk of the revenue
1.Crop recommendation: Designed this classification model using random forest algorithm
Problem- Farmers in India have been producing the same crops for a very long time because of which soil composition and nutrients decreases, they are unable to maximize their profits and occasionally have to deal with crops failing completely.
Our solution- We have created a ML model to address the issue that is taught to evaluate soil quality based on NPK (nitrogen, potassium, and phosphorus) levels in the soil and forecast the best crop for that area to farmers so, they may maximize their income. But waking up to reality we cannot solve a ground level problem, so we need an IOT device which will test the soil give us the N P K values which we will be giving to our model.

2. Fertilizer recommendation: Designed this classification model using random forest algorithm, giving input as nitrogen, phosphorous and potassium values, and suggest the best suitable fertilizer to improve the quality of soil health.
3. Crop Disease Detection: We know that Without proper identification of the disease and the disease-causing agent, disease control measures can be a waste of time and money and can lead to further plant losses. Proper disease diagnosis is therefore vital. Used CNN and its architecture ResNet to design our mobile app which will be connected with our ML model. Farmers just need to click the picture of leaf or select from gallery to detect disease and know the best treatment.
4. Lack of information and role of middleman: Farmers in India are typically not so well educated, which make it easy for middleman to influence them and convince them to sell their yield for low prices. KRISHI-DOST remove middleman role and provide a platform, where farmers may purchase anything.

Challenges we ran into

In this case, the target audience of families, farmers, and environmentalists, this project was created with their accessibility and ease of use in mind. As a result, it was crucial to include as many features as possible that would be truly helpful to both novice and expert users. These features include allowing users to access the service and the assistance they require in the format and language that is most convenient for them, as well as enabling them to connect with and receive assistance from local or specialised experts who can advise them on the best course of action for a fee.
The integration of all the features into a single service while maintaining consistency and validity across all front end HTML and CSS pages, ensuring that the Flask framework was designed and implemented in such a way that all necessary pages and resources load quickly and efficiently through the browser, and ensuring that the additional APIs that we are incorporating are completed were some of the biggest challenges we encountered while building the project. During multiple testing sessions, the website would frequently take a long time to reply to straightforward commands, crash, or produce unexpected Python errors as a result of routing directives coming from HTML sites.
To solve these obstacles, pair programming was used, where one member modified the code while the other member continuously checked for consistency and syntactical correctness, allowing for effective building.