Adoption of blockchain plus Internet of Things in demand forecasting of farm supply chain

It aims to improve the application efficiency of modern science and technology in farm operation, enhance the prediction effect of machine learning and blockchain technology on farm supply and demand issues, and help enterprise managers to better manage and operate enterprise projects. First, the agricultural super business model under the blockchain + Internet of Things (IoT) is analysed, and the farm management system of the IoT + blockchain is constructed. Then, the demand forecasting model of agricultural products is established. Autoregressive Integrated Moving Average model (ARIMA) and Support Vector Machine (SVM) models are used to predict demand. Finally, the characteristics of the two models in prediction are discussed. The results show that the ARIMA model can reach 99.2% in the application efficiency evaluation of farm operation. Moreover, in the case of proper selection of model indicators, the prediction result is close to the actual value, the prediction error fluctuation is small, and the maximum error value is 0.4318. The local error of SVM model in predicting the demand of farm supply chain is slightly larger, and the maximum error value is 8.5430. By comparing the error results of the two models, the prediction accuracy of ARIMA model is higher than that of SVM model. The ARIMA model designed in this work can provide effective product demand prediction for farm operation and provide a strong technical reference for farm operation. The research not only provides technical support for machine learning and blockchain technology to predict farm supply and demand, but also contributes to the integrated management and sustainable development of enterprises.