New framework fuses AI and blockchain to deliver real-time crop intelligence

The integration of IoT, machine learning, and blockchain in a single agricultural forecasting framework could mark a pivotal transformation in how farming is conducted. According to the study, one of the standout features of the proposed system is its capacity to offer multi-crop forecasting. This means that instead of merely predicting a single crop, the system suggests multiple viable options, taking into account both environmental and historical data patterns.

In a major step toward modernizing global agriculture, a team of researchers from Jagannath University and the University of New South Wales has unveiled a breakthrough framework that combines the Internet of Things (IoT), machine learning, and blockchain to deliver secure, precise, and transparent crop forecasting.

The study, titled “A Secured Triad of IoT, Machine Learning, and Blockchain for Crop Forecasting in Agriculture”, was published on arXiv in May 2025. It demonstrates how this integrated triad can offer actionable insights to farmers and agricultural stakeholders, dramatically improving forecasting accuracy while ensuring the integrity of data used to guide critical farming decisions.

What role does artificial intelligence play in improving crop prediction accuracy?

The system comprises a machine learning engine trained on a comprehensive agricultural dataset containing 2,200 instances and seven environmental features including nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall. These variables are essential indicators for determining optimal crop types and projected yields. Various machine learning algorithms were tested, including Decision Trees, Support Vector Machines, Naive Bayes, Logistic Regression, Neural Networks, and K-Nearest Neighbors. Among them, the Random Forest model emerged as the most accurate, achieving a prediction rate of 99.45%.

This ensemble learning method significantly outperformed others across multiple metrics, including precision and recall, proving particularly effective at managing multi-dimensional agricultural data. Not only does it predict the most suitable crops under current environmental conditions, but it also provides a ranked list of multiple crop options, offering flexibility to farmers based on market demands and cultivation strategies.

Unlike traditional methods that rely heavily on historical crop performance or manual field assessment, this AI model dynamically analyzes real-time data to produce forecasts that adjust with environmental shifts. This real-time adaptability makes the system resilient against sudden weather changes, pest outbreaks, and other unpredictable farming challenges.

How does blockchain ensure the reliability and transparency of crop data?

A major innovation of this framework is its integration of the Ethereum blockchain to secure all environmental data and machine learning outputs. Blockchain, a decentralized digital ledger, offers tamper-proof storage of the information collected from IoT sensors. Through smart contracts written in Solidity and executed via platforms like MetaMask and Ganache, every transaction, including data capture, crop forecast generation, and user access, is logged with full traceability.

This process ensures that data cannot be altered or deleted after submission, which is crucial for maintaining trust in the system. All forecasts, whether accessed by farmers, agribusinesses, or policymakers, are backed by a verifiable audit trail. This feature makes the platform especially relevant for countries or regions where data manipulation in agriculture is a recurring concern.

Additionally, stakeholders can interact with the blockchain directly through an intuitive web interface developed using Django. This interface allows users to input data, view current environmental readings, and access forecast results along with their underlying blockchain entries. The interface ensures real-time accessibility, while the backend guarantees security, making it a compelling use case for digital agriculture in the context of Industry 5.0.

What does this mean for the future of farming?

The integration of IoT, machine learning, and blockchain in a single agricultural forecasting framework could mark a pivotal transformation in how farming is conducted. According to the study, one of the standout features of the proposed system is its capacity to offer multi-crop forecasting. This means that instead of merely predicting a single crop, the system suggests multiple viable options, taking into account both environmental and historical data patterns.

Economic and operational implications for farmers are substantial. Accurate, real-time forecasts can optimize resource use, reducing waste of water, fertilizer, and pesticides, thereby lowering costs and enhancing sustainability. Meanwhile, blockchain-secured transparency may increase farmers’ bargaining power in supply chains by offering certified yield estimates that can be used in trade negotiations or insurance claims.

The researchers compared their framework against other contemporary systems and found consistent superiority not only in accuracy but also in scope. Whereas most prior systems focused on single-crop predictions or lacked security integration, this framework supports multi-crop forecasting with high precision and integrates an immutable data protection layer.

The paper provides a roadmap for future enhancements, including real-time sensor network expansion, external data integration (such as market prices and pest trends), AI-driven personalized recommendations, and mobile accessibility to ensure usability for farmers in remote regions. It also encourages exploring alternative blockchain platforms like Hyperledger or Polygon for improved scalability and cost-efficiency.