Digital Technology Boosting Agricultural Supply-Side Constitutive Revolution in Poor Areas Based on the Intelligent Environment of the Internet of Things.

China is a big agricultural country, and China's agricultural development is related to the development of the country. Especially in poor rural areas, the development of agriculture still stays at low-level manual farming. Therefore, with the help of the developed Internet of Things technology, it is necessary for digital technology to develop and reform the rural areas in poverty-stricken areas. This paper aims to promote the supply-side structural revolution of agriculture in poverty-stricken areas by digital technology based on intelligent environment of Internet of Things. Firstly, the KNN intelligent algorithm is improved to improve the accuracy and reduce the time complexity. Then, based on the data support of Internet of Things technology, a prediction model is built, and the parameters are optimized by particle swarm optimization, and finally the best prediction model with high prediction accuracy, good stability and reduced prediction time complexity is obtained. Taking the source area of the Danjiang River in the South-to-North Water Transfer Project as the research object, the total grain output and soybean production demand in this area from 2010 to 2018 were selected, and the change trend of soybean production and demand in this area from 2019 to 2021 was predicted. The experimental data show that the soybean output in this region has increased year by year, with a cumulative increase of 6,985.4 tons from 2010 to 2018. Soybean output sometimes exceeds demand, and sometimes it is less than demand. There will be an oversupply situation in 2019 and 2021, and an oversupply situation in 2020. Therefore, the prediction model of Internet of Things can effectively predict the supply and demand situation of soybean with an accuracy rate of over 90%, which can provide data support for the decision-making of structural change of agricultural supply side in poverty-stricken areas. [ABSTRACT FROM AUTHOR]