How AIoT Optimised Production Within the Agricultural Industry


As Industry Revolution 4.0 (IR 4.0) slowly becomes a reality, businesses who have not implemented digitalization or integrated artificial intelligence (AI) face the challenge of maintaining a competitive edge and risk getting left behind. In order to address this, Plus Xnergy Edge developed smart AIoT energy solutions that can be tailored to fit the needs of businesses across different industries. With agriculture being a key industry for many, a rice miller engaged Plus Xnergy Edge to implement AIoT solutions for their factory.


When it comes to rice, quality is a key factor. The final quality of rice is dependent to the post-harvest processes as it includes drying the harvested paddy.

The process of drying paddy is very important as it cannot be overdried, due to the fact it will result to a higher percentage of broken rice leading to lower selling prices or under-dried, as it would result to the growth of fungus, spoiling the entire batch. Therefore, ensuring that paddy is dried in a balanced amount of time for an optimum moisture has been a key concern for many industry leaders in agriculture.

As drying is typically conducted using dryers, this is often done manually, which results in low rice yield and high energy consumption, while facing the limitation of operators who are experienced in the drying process.

Given that the high capital outlay is needed for dryers, for factories to upgrade to IR 4.0 ready equipment are not suitable, hence a retrofit is required, which is where we come in to take care of everything for you.


To ensure high quality and resolve the business pain points, Plus Xnergy Edge and the rice miller embarked on its trials for a three-step approach to design and build an AIoT solution, also known as SOURCE KAI, our solution for industrial building automation.

STEP 1: Data Collection

After conducting a preliminary site survey and rounds of consultation with us, the rice miller identified that temperature and moisture are key factors of the drying process. As such, sensors were installed in several dryers to collect its data history to be used as a performance benchmark in comparison with duration of drying and final quality of the paddy. All the collected data will be automatically sent to the KAI cloud platform to be further analyzed.

STEP 2: Developing & Training Paddy-Drying With AI

With power, temperature, moisture and the data collection of the quality of the paddy, our engineers developed and trained the Paddy AI system using over 900,000 data points in collaboration with the rice miller and a local university.

A model was priced and then applied, to predict the moisture content of the rice and recommendations and insights for better energy management of the drying process. When applied to new batches of paddy to test the accuracy of the predicted moisture content vs the actual moisture content on site.

At the same time, a dashboard for high-level visualization was also developed to give operators a view of conditions in real-time and control the dryers remotely if needed.

STEP 3: Testing & Verification

After the AI model was deemed to be accurate, additional controllers were installed to automatically control the dryer to be tested. The system tested over 40 different batches under varied operating conditions to ensure the accuracy & reliability of the system.

A detailed form of testing was also conducted by comparing samples in terms of the weight and quality of rice, produced from eight (8) different batches where half was done manually, and the other half was tested with KAI. The energy usage of the dryers was also recorded.

AI Prediction of Dried Paddy
Samples of Data Batches Used to Train the AI Models

By adopting AI and industrial automation, the rice miller saw a significant increase in grade A rice yields and reduction in energy consumption due to shorter and more efficient drying cycles. This led to higher profit margins from better selling prices and lower costs.

On top of that, this AIoT solution has enabled higher output for the rice miller, saving time on hiring and purchase of equipment.

By referring to all the data on KAI’s dashboard, the rice miller now has visibility on their energy consumption and can use the various reports to further improve operations and drive business performance.

As a result of the positive results for the pilot, the rice miller implemented KAI Paddy AI to all dryers within the facility.

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