Instituto Nacional de Telecomunições (INATEL) Santa Rita Sapucai, Santa Rita do Sapucai 37540-000, MG, Brazil.
Sensors (Basel). 2022 Aug 5;22(15):5866. doi: 10.3390/s22155866.
Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. This model supports efficient disease detection with 92% accuracy. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed.
草莓是敏感的水果，容易受到各种病虫害的侵害。因此，在生产过程中会大量使用农药和杀虫剂。由于其敏感性，温度或湿度达到极端水平会对种植园和水果质量造成各种损害。为了解决这个问题，本研究开发了一种边缘技术，能够处理草莓种植中的异构数据的收集、分析、预测和检测。所提出的物联网平台将各种监测服务集成到一个用于数字农业的通用平台中。该系统连接并管理物联网 (IoT) 设备，以分析环境和作物信息。此外，使用 Yolo v5 架构的计算机视觉模型实时搜索七种最常见的草莓病害。该模型支持高效的疾病检测，准确率达到 92%。此外，该系统支持 LoRa 通信，用于在节点之间远距离传输数据。此外，物联网平台集成了机器学习功能，用于捕获收集数据中的异常值，确保为用户提供可靠的信息。所有这些技术都被统一起来，以减轻种植园的疾病问题和环境破坏。该系统通过在草莓农场的实施和测试得到了验证，并对其功能进行了分析和评估。
