用物联网实时测西瓜地土壤盐分，精度更高更智能。
Soil Characterization of Watermelon Field through Internet of Things: A New Approach to Soil Salinity Measurement
- 用传感器+云端+手机端实现土壤温湿酸碱度实时监测
- 通过人工神经网络建立土壤盐分与电阻率的对应关系
- 对比实测数据验证系统精度，适合智慧农业研究者
在现代农业中，技术对种植发展至关重要。为提升作物产量，土壤需具备特定属性。西瓜种植要求土壤疏松、温度高且灌溉得当。本研究设计并实现了一套基于物联网的智能土壤表征系统，用于西瓜田土壤特性监测。该系统利用不同传感器测量土壤湿度、温度和pH值，数据通过Arduino与Raspberry Pi上传至云端，用户可通过配套移动应用或网页获取信息。为确保系统精度，本研究将系统测得参数与传统田间土壤仪读数及土壤科学实验室数据进行对比。土壤盐度过高会抑制西瓜产量。本文提出一种基于土壤电阻率的盐分测量模型，利用人工神经网络（ANN）根据实验数据建立土壤盐分与电阻率之间的关系。
原文摘要 · Abstract (English)
In the modern agricultural industry, technology plays a crucial role in the advancement of cultivation. To increase crop productivity, soil require some specific characteristics. For watermelon cultivation, soil needs to be sandy and of high temperature with proper irrigation. This research aims to design and implement an intelligent IoT-based soil characterization system for the watermelon field to measure the soil characteristics. IoT based developed system measures moisture, temperature, and pH of soil using different sensors, and the sensor data is uploaded to the cloud via Arduino and Raspberry Pi, from where users can obtain the data using mobile application and webpage developed for this system. To ensure the precision of the framework, this study includes the comparison between the readings of the soil parameters by the existing field soil meters, the values obtained from the sensors integrated IoT system, and data obtained from soil science laboratory. Excessive salinity in soil affects the watermelon yield. This paper proposes a model for the measurement of soil salinity based on soil resistivity. It establishes a relationship between soil salinity and soil resistivity from the data obtained in the laboratory using artificial neural network (ANN).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。