Abstract: This paper presents a solution to improve fruit yield monitoring and sales in agriculture by combining the ESP32CAM module with Telegram bot technology. The system uses the ESP32CAM to capture real-time images of fruit, which are then analysed with machine learning algorithms for accurate counting and quality assessment. The images are uploaded to a cloud server, where advanced analytics estimate the yield and predict the best harvest times. This approach helps farmers make better decisions about when to harvest and manage their inventory.
The Telegram bot acts as an easy-to-use interface for farmers and customers, allowing smooth communication and realtime updates on inventory, sales, and yield information. Farmers can use the bot to manage their stock, get notifi-cations about fruit quality and quantity, and directly sell to consumers, removing intermediaries and improving prof-itability.
By automating the processes of monitoring yield, assessing quality, and handling sales, this system cuts down on manual work, boosts efficiency, and creates a direct link between farmers and customers. Using machine learning, realtime communication, and cloud-based analytics, the system offers a modern and scalable solution to problems in agriculture, paving the way for future advances in precision farming and direct sales to consumers.
Keywords: Road safety, Fruit yield monitoring, ESP32CAM module, Telegram bot, Machine learning algorithms, Image processing, Real-time data, Cloud-based server, Yield estimation, Sales management, Agriculture technology, Precision farming, Scalable solution.
