The problem Krishi Sahay solves

The problem addressed by "KrishiSahay" is the need to empower farmers through innovative technology to identify crop diseases promptly, provide eco-friendly solutions, and promote sustainable agricultural practices. This solution is particularly crucial in the context of India, where agriculture plays a significant role in the economy and livelihoods of a large population. By leveraging advanced technologies like AI and utilizing the widespread use of smartphones, KrishiSahay aims to revolutionize how farmers can access information and support for their farming practices.

Certainly! Here are some additional points about the use of a WhatsApp bot and its benefits:

1. Accessibility: WhatsApp is widely used, making it accessible to a large number of farmers who can easily interact with the bot using a platform they are familiar with.
2. Convenience: Farmers can access the bot anytime, anywhere, making it convenient for them to get information and assistance without the need for travel or physical presence.
3. Cost-Effective: Using a WhatsApp bot can be more cost-effective than traditional methods of providing support and information to farmers, such as field visits or call centers.
4. Real-Time Assistance: The bot can provide real-time assistance to farmers, helping them diagnose crop diseases and provide solutions quickly.
5. Data Collection: The bot can also be used to collect valuable data from farmers, such as information about crop diseases and their prevalence, which can be used for research and planning.
6. Scalability: A WhatsApp bot can scale to accommodate a large number of users, making it suitable for widespread adoption and use across different regions.
7. Interactive Features: The bot can be designed to provide interactive features, such as quizzes or surveys, to engage farmers and provide them with a more personalized experience.

Challenges we ran into

Challenges were encountered in several key areas throughout the development of KrishiSahay. Firstly, there was a scarcity of local crop disease datasets, necessitating the creation of our dataset. This involved collecting 2000 images per disease across three classes, a task that was both time-consuming and resource-intensive. Ensuring the quality of the collected data was also challenging, requiring meticulous attention to detail to ensure the images were clear and representative.

Training the machine learning models to accurately identify crop diseases was another significant challenge. This process required substantial computational resources and expertise, and tuning the models for optimal performance was complex. Additionally, adapting the solution to local languages and agricultural practices posed challenges, as we needed to ensure that the bot's responses and recommendations were culturally and contextually appropriate.

Integrating the AI-powered disease identification system with the WhatsApp platform was technically challenging, requiring careful implementation and testing to ensure smooth communication and interaction with users. Maintaining user engagement and encouraging ongoing use of the bot was also a challenge, requiring us to design the user experience to be intuitive and valuable for farmers. Finally, establishing a feedback loop to continuously improve the system based on user input and data analysis was crucial but challenging, requiring effective collection and analysis of feedback to enhance the bot's performance and usefulness.