Mushrooms contain valuable nutrients, proteins, minerals, and vitamins, and it is suggested to include them in our diet. Many farmers grow mushrooms in restricted environments with specific atmospheric parameters in greenhouses. In addition, recent technologies of the Internet of things intend to give solutions in the agriculture area. In this paper, we evaluate the effectiveness of machine learning for mushroom growth monitoring for the genus Pleurotus. We use YOLOv5 to detect mushrooms’ growing stage and indicate those ready to harvest. The results show that it can detect mushrooms in the greenhouse with an F1-score of up to 76.5%. The classification in the final stage of mushroom growth gives an accuracy of up to 70%, which is acceptable considering the complexity of the photos used. In addition, we propose a method for mushroom growth monitoring based on Detectron2. Our method shows that the average growth period of the mushrooms is 5.22 days. Moreover, our method is also adequate to indicate the harvesting day. The evaluation results show that it could improve the time to harvest for 14.04% of the mushrooms.

Remote sensing stands out as one of the most widely used operations in the field. In this research area, UAVs offer full coverage of large cultivation areas in a few minutes and provide orthomosaic images with valuable information based on multispectral cameras. Especially for orchards, it is helpful to isolate each tree and then calculate the preferred vegetation indices separately. Thus, tree detection and crown extraction is another important research area in the domain of Smart Farming. In this paper, we propose an innovative tree detection method based on machine learning, designed to isolate each individual tree in an orchard. First, we evaluate the effectiveness of Detectron2 and YOLOv8 object detection algorithms in identifying individual trees and generating corresponding masks. Both algorithms yield satisfactory results in cherry tree detection, with the best F1-Score up to 94.85%. In the second stage, we apply a method based on OTSU thresholding to improve the provided masks and precisely cover the crowns of the detected trees. The proposed method achieves 85.30% on IoU while Detectron2 gives 79.83% and YOLOv8 has 75.36%. Our work uses cherry trees, but it is easy to apply to any other tree species. We believe that our approach will be a key factor in enabling health monitoring for each individual tree.

Mobility is a critical aspect of the Internet of Things, reflecting the diverse and dynamic nature of potential applications. LoRa, a low power wide area network, is a promising wireless technology suitable for a wide range of applications. In this paper, we propose mobile self adaptive data rate (M‐SADR), a method designed to handle LoRa‐enabled mobile end devices, aiming to reduce energy consumption while maintaining a high packet delivery ratio. M‐SADR operates on the end device side by selecting the spreading factor and transmission power for the next packet based on previous attempts. The proposed method stands out from similar solutions by enabling mobile LoRa end devices to determine transmission parameters efficiently, using a simple approach that requires minimal processing and storage resources. We evaluated our method in two distinct scenarios: the first involves three mobile gateways following a moving group of end devices, while the second uses a single static gateway with multiple mobile end devices moving arbitrarily around it. The evaluation of the two simulation scenarios showed that our method achieves better results, with up to 2.4% higher packet delivery ratio and 5.7% lower energy consumption per successfully delivered packet compared with other methods (ADR‐MIN, BADR, and HADR) for LoRa mobile end devices. Furthermore, a small‐scale testbed was implemented, demonstrating that the proposed algorithm performs better than its competitors in real‐world scenarios. Finally, in the first simulation scenario, we present a methodology uses RSSI‐based position estimation to reposition the mobile gateways and maintain proximity to the moving group of end devices.