![]() Strawberries are popular fruits consumed in almost all parts of the globe, and this expansion is a result of the fruit adaption capability that makes the cultivation possible in multiple climates. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. ![]() In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. This model supports efficient disease detection with 92% accuracy. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. 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. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. Therefore, there is an intense use of agrochemicals and pesticides during production. ![]() ![]() Strawberries are sensitive fruits that are afflicted by various pests and diseases. ![]()
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