Smart Strawberry Farming Using Edge Computing and IoT.

Abstract

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.

Plant phenotyping relevance

イチゴ病害を画像からリアルタイム検出するコンピュータビジョン手法をIoTプラットフォームの中心機能として開発・実装し、農場で評価しているため、植物病害状態のフェノタイピング手法に該当する。

Code and data availability

The paper provides an authors' public GitHub repository for the proposed IoT/edge phenotyping platform (sensor collection, YOLO v5 disease detection, Isolation Forest ML) and points to a public Kaggle strawberry disease detection dataset used for the computer vision model.