Sensor Vegetable Greenhouse and Agricultural Product Supply Chain Management Based on Improved Neural Network

Abstract

In recent years, people have begun to collect environmental data in vegetable greenhouses. Therefore, this article studies the current internal control technology of vegetable greenhouses, and improves the sensor application in vegetable greenhouses, in a targeted manner by combining with the improved neural network algorithm. The model has more accurate prediction accuracy than traditional BP. By using the Android client and ZigBee artificial intelligence control technology it creates the most suitable living conditions for the vegetables, fruits, and other crops in the vegetable greenhouse by controlling the various valves, sun visors, and light supplementary switches inside the vegetable greenhouse. The simulation results show that the model proposed in this article has higher convergence accuracy. In addition, this article applies the improved neural network to the management of the agricultural product supply chain, starting from the agricultural product supply chain and agricultural product supply chain management. Based on this, the analytic hierarchy process is used to explore the influencing factors of agricultural product supply chain management, and the analysis shows that consumers are targeting different consumer demand raised by the agricultural product supply chain. The improved neural network provides new ideas for the research of sensor vegetable greenhouses and agricultural product supply chain management.

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

“…Through numerous plant physiological sensors, ZigBee technology captures the complete development process of plant germination, growth, blooming, and fruit and delivers the information record to the computer for information processing to ensure nutritional content (Dou, 2022;Yan et al, 2021;Balamurugan et al, 2020). IoT is projected to be a key differentiator in the food and farming industries as this invisible technology works behind the scenes, reacting dynamically to how people want "things" to behave to improve food safety (Liu et al, 2016;Parkes et al, 2022;Ruan et al, 2020;Brewster et al, 2017).…”