IoT AI-driven Yogurt Processing & Texture Prediction
Collect environmental factors and cultural amount while producing yogurt. Then, run a neural network model via Blynk to predict its texture.
Seeed Hardware: Seeed Studio XIAO ESP32C3、Seeed Studio Seeeduino XIAO Expansion board、Seeed Studio Grove – Temperature&Humidity Sensor (SHT40)、Seeed Studio Grove – Integrated Pressure Sensor Kit
Software: Arduino IDE、Blynk、Edge Impulse
Industry: Industrial Site
Solution Deployment: Turkey
Background
As many of us know, yogurt is produced by bacterial fermentation of milk, which can be of cow, goat, ewe, sheep, etc. The fermentation process thickens the milk and provides a characteristic tangy flavor to yogurt. Considering organisms contained in yogurt stimulate the gut’s friendly bacteria and suppress harmful bacteria looming in the digestive system, it is not surprising that yogurt is consumed worldwide as a healthy and nutritious food.
The bacteria utilized to produce yogurt are known as yogurt cultures (or starters). Fermentation of sugars in the milk by yogurt cultures yields lactic acid, which decomposes and coagulates proteins in the milk to give yogurt its texture and characteristic tangy flavor. Also, this process improves the digestibility of proteins in the milk and enhances the nutritional value of proteins. After the fermentation of the milk, yogurt culture could help the human intestinal tract to absorb the amino acids more efficiently.
In this regard, most companies employ food (chemical) additives while mass-producing yogurt to maintain its freshness, taste, texture, and appearance. Depending on the production method, yogurt additives can include diluents, water, artificial flavorings, rehashed starch, sugar, and gelatine.
In recent years, due to the surge in food awareness and apposite health regulations, companies were coerced into changing their yogurt production methods or labeling them conspicuously on the packaging. Since people started to have a penchant for consuming more healthy and organic (natural) yogurt, it became a necessity to prepare prerequisites precisely for yogurt production without any additives. However, unfortunately, organic (natural) yogurt production besets some local dairies since following strict requirements can be expensive and demanding for small businesses trying to gain a foothold in the dairy industry.
After perusing recent research papers on yogurt production, I decided to utilize temperature, humidity, pressure, milk temperature, and culture weight measurements denoting yogurt consistency before fermentation so as to create an easy-to-use and budget-friendly device in the hope of assisting dairies in reducing total cost and improving product quality.
Since XIAO ESP32C3 is an ultra-small size IoT development board that can easily collect data and run my neural network model after being trained to predict yogurt consistency levels, I decided to employ XIAO ESP32C3 in this project. To collect the required measurements to train my model, I used a temperature & humidity sensor (Grove), an integrated pressure sensor kit (Grove), an I2C weight sensor kit (Gravity), and a DS18B20 waterproof temperature sensor. Since the XIAO expansion board provides various prototyping options and built-in peripherals, such as an SSD1306 OLED display and a MicroSD card module, I used the expansion board to make rigid connections between XIAO ESP32C3 and the sensors.
The Challenge
While deploying the project described, there may be several potential challenges that you could encounter, including:
Sensor calibration: The accuracy of the collected sensor data may be affected by sensor calibration issues, which may require careful calibration procedures to ensure accurate and reliable data collection.
Power management: The sensors and other peripherals used in the project may consume significant amounts of power, which may limit the device’s battery life and performance. Careful consideration of power management strategies, such as low-power modes and efficient data transmission protocols, may be necessary to ensure optimal device performance and longevity.
Data storage and management: Storing and managing large amounts of data collected by the sensors may require careful consideration of data storage and management strategies, including file formats, storage location, and backup procedures.
Model optimization: While Edge Impulse is compatible with XIAO ESP32C3, optimizing the ANN model to run efficiently on the device may require careful consideration of model architecture, input/output data formats, and performance metrics.
Overall, while there may be some challenges involved in deploying this project, careful consideration of these factors, as well as testing and validation of the system in different environments, can help ensure that the device is reliable, effective, and easy to use. By doing so, the project can provide valuable insights into yogurt consistency levels and help improve yogurt production processes.
The Solution
To address the potential challenges of deploying the project described, several strategies can be employed:
Sensor calibration: To address sensor calibration issues, it may be necessary to carefully calibrate the sensors used in the project using appropriate procedures and tools. Additionally, implementing sensor error detection and correction mechanisms can help ensure accurate and reliable data collection.
Power management: Power management strategies such as low-power modes, efficient data transmission protocols, and careful selection of power sources can help optimize device performance and longevity.
Data storage and management: Implementing appropriate data storage and management strategies, such as selecting appropriate file formats, storage location, and backup procedures, can help ensure reliable and efficient data collection and management.
Model optimization: Optimizing the ANN model to run efficiently on XIAO ESP32C3 may require careful consideration of model architecture, input/output data formats, and performance metrics. Additionally, implementing model compression techniques, such as quantization and pruning, can help reduce the model’s memory and computing requirements.
Overall, by employing these strategies, it is possible to overcome the challenges of deploying the project described, ensuring that the device is reliable, effective, and easy to use. By doing so, the project can provide valuable insights into yogurt consistency levels and help improve yogurt production processes.
The Results
The project described, which involves using XIAO ESP32C3 and Edge Impulse to predict yogurt consistency levels, has significant value and potential applications in the food industry. By providing real-time insights into yogurt consistency levels, the device can help improve yogurt production processes, resulting in higher quality and more consistent products.
The device can also have significant applications in other areas of the food industry, such as in the production of other dairy products, as well as in the processing and packaging of other food products. By providing real-time insights into the characteristics of food products, the device can help improve the efficiency and effectiveness of food production processes, resulting in higher quality and more consistent products.
Overall, the device has significant potential to improve the quality and consistency of food products, providing valuable insights to food producers and enabling more efficient and effective food production processes. As such, the device has promising applications in a variety of industries, including agriculture, food processing, and food packaging.
More Info
- Learn more about products used: Seeed Studio XIAO ESP32C3
- Discover Seeed Studio XIAO Series
- View and Download: System-on-Modules (SoM) User Manual
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