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Utilising blockchain and satellite technology, every transaction and movement of food products from their point of origin to their final destination is tracked in a transparent and immutable ledger. A safe and auditable record of the product’s journey is ensured by the ability of every stakeholder in the supply chain, including farmers, processors, distributors, and retailers, to submit data into the blockchain.

Through the provision of a decentralised and tamper-proof system for tracking and confirming the authenticity and integrity of food products and this technology facilitates traceability.

QR codes and barcodes are frequently used on food packaging to give consumers access to thorough information on the product’s origin and manufacturing processes, and supply chain trip. Consumers can track the merchandise by using a smartphone to scan these codes.

Satellite remote sensing data is critical for monitoring soil, drought and snow cover, and crop development. Satellite rainfall estimates, for example, assist farmers in planning the quantity and timing of irrigation required for their crops.

For example, weather forecasting enables you to properly plan your farm operations, such as planting, irrigation, fertilizer application, pruning/weeding, harvesting or livestock mating and since farming and agriculture as a whole chiefly depend on seasons and weather.

Applications for smart farming are being utilised more frequently to improve food traceability throughout the agricultural supply chain. To ensure transparency and traceability from farm to fork, these applications make use of a variety of technology and data-driven solutions. For food traceability, the following smart farming applications are used.

IoT Sensors and RFID Tags: Throughout the supply chain, different parameters are tracked and monitored using Internet of Things (IoT) sensors and Radio Frequency Identification (RFID) tags. To gather information on temperature, humidity, soil moisture, and pesticide use, IoT sensors can be installed in fields and farms. To track the movement and status of individual products as they travel through the supply chain, such as crates or pallets, RFID tags are affixed to them.

Traceability within food control systems is applied as a tool to control food hazards, provide reliable product information and guarantee product authenticity. Recall or Product Recall is defined as “the action to remove food from the market at any stage of the food chain, including that possessed by consumers”.

Feature Selection: To provide precise predictions, machine learning algorithms need pertinent features. The most important factors that have an impact on market demand are determined using feature selection approaches. Product characteristics, pricing details, marketing initiatives, seasonality, and outside variables like economic indicators are a few examples of these elements.

Regression, decision trees, random forests, and neural networks are just a few examples of the machine learning models that may be trained utilising preprocessed data. The models discover patterns and connections between the feature inputs and market demand. The models’ internal parameters are changed during training in order to reduce prediction errors.

Model Evaluation and Validation: The trained models are evaluated and validated using historical data that was not used for training. This helps assess the performance and accuracy of the models in predicting market demand. Various evaluation metrics, such as mean absolute error (MAE) or root mean square error (RMSE), are calculated to measure the predictive performance.