Digital grain moisture analyzer

Inspiration

Post-harvest grain losses due to improper moisture levels are a major problem for farmers. Traditional moisture testing methods are either expensive, slow, or inaccessible in rural areas.

We were inspired to build an affordable, Iot based, AI-powered Digital Grain Moisture Analyzer that provides fast, accurate, and real-time moisture readings. Our goal was to help farmers make better storage and selling decisions while reducing wastage and improving food security.

What it does

Measures grain moisture levels using sensors Processes data through a microcontroller Applies calibration logic for accurate readings Displays real-time moisture percentage Sends data to a cloud dashboard for monitoring Provides alerts if moisture exceeds safe storage limits This enables farmers, warehouses, and agri-businesses to monitor grain quality efficiently and prevent spoilage.

How we built it

We built the system using a combination of hardware and software components: Hardware: Moisture sensor probes Microcontroller (Arduino/ESP32) LCD display Power supply module Software & Tech Stack: Embedded C / Arduino IDE Python for data processing IoT cloud platform for dashboard visualization Basic ML calibration model for improving accuracy Steps involved: Sensor integration and calibration Microcontroller programming for data acquisition Signal processing and moisture calculation Cloud integration for remote monitoring Testing with different grain samples

Challenges we ran into

Sensor calibration inconsistencies across grain types Noise in analog readings Maintaining accuracy under varying temperature conditions Integrating hardware data with cloud dashboard Ensuring low-cost while maintaining reliability We solved these using filtering techniques, repeated calibration tests, and optimized hardware design.

Accomplishments that we're proud of

Built a working real-time prototype Achieved consistent and reliable moisture readings Designed a low-cost and scalable solution Successfully integrated IoT dashboard Reduced measurement time significantly compared to traditional methods

What we learned

Through this project, we learned: End-to-end IoT system development Sensor calibration and hardware debugging Real-time data processing Cloud integration for smart agriculture Importance of accuracy in agricultural applications This project strengthened our skills in embedded systems, IoT, and applied machine learning.

What's next for Digital grain moisture analyzer

Add temperature compensation for higher accuracy Develop a mobile app for farmers Train a more advanced ML model for multi-grain calibration Enable predictive storage risk analysis Scale deployment for warehouses and FPOs Our long-term vision is to make smart grain storage systems accessible, affordable, and reliable for warehouses, storage facilities, and food supply chains.