### Related Discussion  
Based on the current research, we found that most of the research studies are focused on further IoT control operations after collecting agricultural data, including water valve management, light source management, etc. [PERSON] (2019) [10] proposed to adopt a control system of the node sensors to carry out data management in the field through smart phones and web applications. Combined with web applications and mobile applications, mobile phone software design operations are performed, data retrieved from sensors are collected, and subsequent human intervention operations are performed. This method samples data through device endpoints, makes judgments based on human experience, and manages related hardware. In contrast, current deep learning technology can be trained to gain more human experience and judgment without considering relevant outcomes. For example, during the growth of plants, it determines whether the fixed time of sunshine or the non-fixed time of sunshine will affect the growth results of the plants. Empirical studies on similar problems have not been made so far. The main reason for this is that the growth cycle of plants takes a long time, and the longer the timeline, the more disturbances there will be. Therefore, taking Taiwan as an example, the sunshine hours in the months of July and August are relatively long, which may affect the harvest status of vegetables, which in turn will affect the market value of vegetables. [PERSON] (2022) [11] previously designed an IoT communication system for automated irrigation water valves, which uses the MQTT protocol for monitoring the current environmental status and transmitting operation messages. This study mainly deals with remote controlling and monitoring operations but does not discuss predicting data. This study explores the possibility of further establishing a repeatable predictive and verified growth factor model through the predictive analysis model after data retrieval, referring to the communication and broadcasting framework proposed by [PERSON] (2022). Several studies have investigated LoRa-based communication models for distributed data collection [12; 13]. LoRa is characterized by low power consumption. It can be used to reduce the amount of data transmitted after repeated data sampling. However, in this technology, due to a large amount of data sampling, data are observed and analyzed based on the data transmitted through the communication environment, but when used for machine learning and modeling, we can replace the data transmission of a large number of edge computing. Therefore, in the study, the computational model is placed on the edge device, the association model of computational result is transmitted to other nodes for verification through the MQTT broker, and the verification result report is used to change the association rule. [PERSON], [PERSON] (2020) [14] also discussed an edge computing platform that uses deep learning to predict low temperatures in agriculture. According to the study, to enable deep learning inference and training, edge computing needs to be performed by GPUs with higher performance in the computing environment. This is due to the cost dilemma between edge nodes, high-performance core computing environments, and real-world construction sites. A better GPU environment requires a lot of power-hungry computing, and most agricultural environment fields cannot provide a more stable power environment. This study refers to the federated learning model based on edge computing. [PERSON] [15] once proposed to use the edge environment setting to calculate separately first, and after data collection, use the calculation and analysis results to transfer to the training and learning endpoints through the communication environment. In the study, the images have been under training, and finally, the correct analysis results have been extracted.