To address the challenges faced by the client, our team developed a prototype that leveraged data collection and analysis from IoT devices to gain insight into soil nutrient levels, temperature changes, and other environmental factors that impact wheat yield potential. The prototype used Machine Learning algorithms such as Linear Regression and Decision Trees to forecast yield potential based on historical data about soil nutrient levels and other parameters.

The features of the prototype included:

• Real-time data collection from IoT devices for soil nutrient levels, temperature, rainfall rates, and other environmental factors
• Historical data analysis to identify patterns and trends in soil nutrient levels and other parameters that impact yield potential
• Machine Learning algorithms such as Linear Regression and Decision Trees for forecasting yield potential based on past data
• User-friendly interface for viewing yield potential predictions and making informed decisions about crop management strategies

The IoT devices used in the prototype for paddock-level wheat yield potential had the capability to gather and transmit real-time data on various environmental factors that affect the growth and yield of wheat crops. These devices were placed in strategic locations in the field to capture data on soil moisture, temperature, humidity, light intensity, and other parameters. Some of the key features of these devices included:

• Wireless Connectivity: The IoT devices were designed to communicate wirelessly with a central data hub using low-power, long-range wireless technology – LoRaWAN. This allowed for easy deployment of devices across large areas without the need for complex wiring or infrastructure.
• Battery-Powered: The devices were powered by long-lasting, low-power batteries that could last for several months to years depending on the usage and configuration. This eliminated the need for frequent battery replacement or maintenance.
• Robust Design: The IoT devices were built to withstand harsh environmental conditions such as rain, dust, and extreme temperatures. They were also designed to be tamper-proof and resistant to vandalism or theft.
• Data Encryption: The IoT devices used advanced encryption techniques to secure the data transmitted over the wireless network. This ensured that the data was protected from unauthorized access or interception.
• Scalable: The IoT devices could be easily scaled up or down depending on the size of the field or the number of crops being monitored. This made it easy to expand or modify the system as per the needs of the client.

Overall, the IoT devices played a critical role in the prototype by providing real-time data on various environmental factors that affect the growth and yield of wheat crops. This data was then used in conjunction with Machine Learning algorithms to generate accurate forecasts and recommendations for farmers.

The client reaped multi-fold benefits from the prototype, including:

• Accurate and timely predictions of wheat yield potential at a paddock level, enabling farmers to make informed decisions about crop management strategies and improve overall productivity
• Improved efficiency and reduced costs through targeted use of fertilizers and other inputs based on soil nutrient levels and other environmental factors
• Enhanced sustainability through better management of resources and reduced environmental impact
• Improved collaboration and knowledge sharing among researchers and farmers in the industry through the use of a common platform for data analysis and decision-making